@article {1543971, title = {High-Resolution Descriptions of Social Behavior in Rats and Their Deficits in Genetic Models of ASD}, journal = {Biological Psychiatry}, volume = {93}, year = {2023}, pages = {S7-S8}, abstract = {

Background

While rats offer a unique opportunity to study social behavior in a well-characterized and tractable model system, most descriptions tend to be subjective, anecdotal, or derived from a narrow set of behavioral assays. Extending a recently developed 3D tracking method to multiple animals (social-DANNCE), we provide an unbiased and quantitative description of\ social interactions\ in rats and pinpoint social deficits in rat models of\ autism spectrum disorder\ (ASD).

DOI:\ https://doi.org/10.1016/j.biopsych.2023.02.039

}, url = {https://www.sciencedirect.com/science/article/pii/S0006322323001130?via\%3Dihub}, author = {Ugne Klibaite and Bence {\"O}lveczky} } @article {1543961, title = {Catalyzing next-generation Artificial Intelligence through NeuroAI}, journal = {Nature Communications}, volume = {14}, year = {2023}, pages = {1597}, abstract = {Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We propose that to accelerate progress in AI, we must invest in fundamental research in NeuroAI. A core component of this is the embodied Turing test, which challenges AI animal models to interact with the sensorimotor world at skill levels akin to their living counterparts. The embodied Turing test shifts the focus from those capabilities like game playing and language that are especially well-developed or uniquely human to those capabilities {\textendash} inherited from over 500 million years of evolution {\textendash} that are shared with all animals. Building models that can pass the embodied Turing test will provide a roadmap for the next generation of AI.}, url = {https://www.nature.com/articles/s41467-023-37180-x}, author = {Anthony Zador and Sean Escola and Blake Richards and Bence {\"O}lveczky and Yoshua Bengio and Kwabena Boahen and Matthew Botvinick and Dmitri Chklovskii and Anne Churchland and Claudia Clopath and James DiCarlo and Surya Ganguli and Jeff Hawkins and Konrad K{\"o}rding and Alexei Koulakov and Yann LeCun and Timothy Lillicrap and Adam Marblestone and Bruno Olshausen and Alexandre Pouget and Cristina Savin and Terrence Sejnowski and Eero Simoncelli and Sara Solla and David Sussillo and Andreas S. Tolias and Doris Tsao} } @article {1543936, title = {Dissociating the contributions of sensorimotor striatum to automatic and visually guided motor sequences}, journal = {Nature Neuroscience}, volume = {26}, year = {2023}, pages = {1791{\textendash}1804}, abstract = {

The ability to sequence movements in response to new task demands enables rich and adaptive behavior. However, such flexibility is computationally costly and can result in halting performances. Practicing the same motor sequence repeatedly can render its execution precise, fast and effortless, that is, {\textquoteleft}automatic{\textquoteright}. The basal ganglia are thought to underlie both types of sequence execution, yet whether and how their contributions differ is unclear. We parse this in rats trained to perform the same motor sequence instructed by cues and in a self-initiated overtrained, or {\textquoteleft}automatic,{\textquoteright} condition. Neural recordings in the sensorimotor striatum revealed a kinematic code independent of the execution mode. Although lesions reduced the movement speed and affected detailed kinematics similarly, they disrupted high-level sequence structure for automatic, but not visually guided, behaviors. These results suggest that the basal ganglia are essential for {\textquoteleft}automatic{\textquoteright} motor skills that are defined in terms of continuous kinematics, but can be dispensable for discrete motor sequences guided by sensory cues.

}, url = {https://www.nature.com/articles/s41593-023-01431-3}, author = {Kevin G. C. Mizes and Jack Lindsey and G. Sean Escola and Bence P. {\"O}lveczky} } @article {1543921, title = {Motor cortex is required for flexible but not automatic motor sequences}, journal = {bioRxiv}, volume = {10}, year = {2023}, pages = {556348}, abstract = {

How motor cortex contributes to motor sequence execution is much debated, with studies supporting disparate views. Here we probe the degree to which motor cortex{\textquoteright}s engagement depends on task demands, specifically whether its role differs for highly practiced, or {\textquoteleft}automatic{\textquoteright}, sequences versus flexible sequences informed by external events. To test this, we trained rats to generate three-element motor sequences either by overtraining them on a single sequence or by having them follow instructive visual cues. Lesioning motor cortex revealed that it is necessary for flexible cue-driven motor sequences but dispensable for single automatic behaviors trained in isolation. However, when an automatic motor sequence was practiced alongside the flexible task, it became motor cortex-dependent, suggesting that subcortical consolidation of an automatic motor sequence is delayed or prevented when the same sequence is produced also in a flexible context. A simple neural network model recapitulated these results and explained the underlying circuit mechanisms. Our results critically delineate the role of motor cortex in motor sequence execution, describing the condition under which it is engaged and the functions it fulfills, thus reconciling seemingly conflicting views about motor cortex{\textquoteright}s role in motor sequence generation.

doi:\ https://doi.org/10.1101/2023.09.05.556348

}, url = {https://www.biorxiv.org/content/10.1101/2023.09.05.556348v1.abstract}, author = {Kevin G. C. Mizes and Jack Lindsey and G. Sean Escola and Bence P. {\"O}lveczky} } @article {1543916, title = {Differential kinematic coding in sensorimotor striatum across species-typical and learned behaviors reflects a difference in control}, journal = {bioRxiv}, volume = {10}, year = {2023}, pages = {562282}, abstract = {

The sensorimotor arm of the basal ganglia is a major part of the mammalian motor control network, yet whether it is essential for generating natural behaviors or specialized for learning and controlling motor skills is unclear. We examine this by contrasting contributions of the sensorimotor striatum (rodent dorsolateral striatum, DLS) to spontaneously expressed species-typical behaviors versus those adapted for a task. In stark contrast to earlier work implicating DLS in the control of acquired skills, bilateral lesions had no discernable effects on the expression or detailed kinematics of species-typical behaviors, such as grooming, rearing, or walking. To probe the neural correlates underlying this dissociation, we compared DLS activity across the behavioral domains. While neural activity reflected the kinematics of both learned and species-typical behaviors, the coding schemes were very different. Taken together, we did not find evidence for the basal ganglia circuit being required for species-typical behaviors; rather, our results suggest that it monitors ongoing movement and learns to alter its output to shape skilled behaviors in adaptive and task-specific ways.

doi:\ https://doi.org/10.1101/2023.10.13.562282

}, url = {https://www.biorxiv.org/content/10.1101/2023.10.13.562282v1.abstract}, author = {Kiah~Hardcastle and Jesse D.~Marshall and Amanda~Gellis and Ugne~Klibaite and William~Wang and Selimzhan~Chalyshkan and Bence P.~{\"O}lveczky} } @article {1462101, title = {Long-term stability of single neuron activity in the motor system}, journal = {Nature Neuroscience}, volume = {25}, year = {2022}, pages = {1664-1674}, abstract = { How an established behavior is retained and consistently produced by a nervous system in constant flux remains a mystery. One possible solution to ensure long-term stability in motor output is to fix the activity patterns of single neurons in the relevant circuits. Alternatively, activity in single cells could drift over time provided that the population dynamics are constrained to produce the same behavior. To arbitrate between these possibilities, we recorded single-unit activity in motor cortex and striatum continuously for several weeks as rats performed stereotyped motor behaviors{\textemdash}both learned and innate. We found long-term stability in single neuron activity patterns across both brain regions. A small amount of drift in neural activity, observed over weeks of recording, could be explained by concomitant changes in task-irrelevant aspects of the behavior. These results suggest that long-term stable behaviors are\ {\textellipsis}}, url = {https://www.nature.com/articles/s41593-022-01194-3}, author = {Kristopher T Jensen and Naama Kadmon Harpaz and Ashesh K Dhawale and Steffen BE Wolff and Bence P {\"O}lveczky} } @article {1462091, title = {Toward next-generation artificial intelligence: Catalyzing the neuroai revolution}, journal = {arXiv preprint arXiv:}, year = {2022}, abstract = { Neuroscience has long been an important driver of progress in artificial intelligence (AI). We propose that to accelerate progress in AI, we must invest in fundamental research in NeuroAI.}, url = {https://arxiv.org/abs/2210.08340}, author = {Anthony Zador and Blake Richards and Bence {\"O}lveczky and Sean Escola and Yoshua Bengio and Kwabena Boahen and Matthew Botvinick and Dmitri Chklovskii and Anne Churchland and Claudia Clopath and James DiCarlo and Surya Ganguli and Jeff Hawkins and Konrad Koerding and Alexei Koulakov, and Yann LeCun and Timothy Lillicrap and Adam Marblestone and Bruno Olshause and Alexandre Pouget and Cristina Savin and Terrence Sejnowski and Eero Simoncelli and Sara Solla and David Sussillo and Andreas S Tolias and Doris Tsao} } @article {1462081, title = {Learning-induced changes in the neural circuits underlying motor sequence execution}, journal = {Current Opinion in Neurobiology}, year = {2022}, abstract = {

As the old adage goes: practice makes perfect. Yet, the neural mechanisms by which rote repetition transforms a halting behavior into a fluid, effortless, and {\textquotedblleft}automatic{\textquotedblright} action are not well understood. Here we consider the possibility that well-practiced motor sequences, which initially rely on higher-level decision-making circuits, become wholly specified in lower-level control circuits. We review studies informing this idea, discuss the constraints on such shift in control, and suggest approaches to pinpoint circuit-level changes associated with motor sequence learning.

}, url = {https://www.sciencedirect.com/science/article/pii/S0959438822001180}, author = {Naama Kadmon Harpaz and Kiah Hardcastle and Bence P {\"O}lveczky} } @article {1462076, title = {Characterizing social impairments in rat models of ASD}, journal = {American Physical Society}, volume = {67}, year = {2022}, abstract = {

Social interaction is a core component of animal behavior, and the tracking and quantification of spontaneous social behavior presents several challenges in both computer vision and interpretation. We extend a recently developed technique for 3D kinematic tracking of single animals (DANNCE) to capture the 3D poses of freely interacting animals by tracking animal identity and refining keypoint labeling networks to maintain accuracy during touching and occlusion. Using this approach, we have acquired a rich dataset of interactions across pairings from autism spectrum disorder (ASD) knockout rats and their normal counterparts. We use a dynamical embedding approach to parse animal movement throughout solitary and social contexts to find behaviors or {\textquoteright}gestures{\textquoteright} which are preferentially expressed in the social context, and timestamp periods of behavioral synchronization during interaction. We find that social exchanges differ between ASD and control animals, and preliminary analysis of these interactions in the Cntnap2 rat model suggests that epochs of synchronized behaviors are dominated by aggressive behaviors in ASD pairs and more canonical play-fighting behavior in wild type animals.\  \ 

}, url = {https://meetings.aps.org/Meeting/MAR22/Session/N03.3}, author = {U Klibaite and J Marshall and T Dunn and D Aldarondo and Bence P {\"O}lveczky} } @article {1462066, title = {Similar striatal activity exerts different control over automatic and flexible motor sequences}, journal = {bioRxiv}, year = {2022}, abstract = {

The ability to sequence movements in response to new task demands enables rich and adaptive behavior. Such flexibility, however, is computationally costly and can result in halting performances. Practicing the same motor sequence repeatedly can render its execution precise, fast, and effortless, i.e., {\textquoteright}automatic{\textquoteright}. The basal ganglia are thought to underlie both modes of sequence execution, yet whether and how their contributions differ is unclear. We parse this in rats trained to perform the same motor sequence in response to cues and in an overtrained, or {\textquoteright}automatic{\textquoteright}, condition. Neural recordings in the sensorimotor striatum revealed a kinematic code independent of execution mode. While lesions affected the detailed kinematics similarly across modes, they disrupted high-level sequence structure for automatic, but not visually-guided, behaviors. These results suggest that the basal ganglia contribute to learned movement kinematics and are essential for {\textquoteright}automatic{\textquoteright} motor skills but can be dispensable for sensory-guided motor sequences.

doi:https://doi.org/10.1101/2022.06.13.495989

}, url = {https://www.biorxiv.org/content/10.1101/2022.06.13.495989v2.abstract}, author = {Kevin G. C. Mizes and Jack Lindsey and G. Sean Escola and Bence P. {\"O}lveczky} } @article {1435866, title = {Distinct roles for motor cortical and thalamic inputs to striatum during motor skill learning and execution}, journal = {Science Advances}, volume = {8}, year = {2022}, abstract = {The acquisition and execution of motor skills are mediated by a distributed motor network, spanning cortical and subcortical brain areas. The sensorimotor striatum is an important cog in this network, yet the roles of its two main inputs, from motor cortex and thalamus, remain largely unknown. To address this, we silenced the inputs in rats trained on a task that results in highly stereotyped and idiosyncratic movement patterns. While striatal-projecting motor cortex neurons were critical for learning these skills, silencing this pathway after learning had no effect on performance. In contrast, silencing striatal-projecting thalamus neurons disrupted the execution of the learned skills, causing rats to revert to species-typical pressing behaviors and preventing them from relearning the task. These results show distinct roles for motor cortex and thalamus in the learning and execution of motor skills and suggest that their interaction in the striatum underlies experience-dependent changes in subcortical motor circuits.}, url = {https://www.science.org/doi/full/10.1126/sciadv.abk0231}, author = {SBE Wolff and Ko, R. and {\"O}lveczky, B. P.} } @article {1410756, title = {The PAIR-R24M Dataset for Multi-animal 3D Pose Estimation}, journal = {NeurIPS}, year = {2021}, abstract = {

Understanding the biological basis of social and collective behaviors in animals is a key goal of the life sciences, and may yield important insights for engineering intelligent multi-agent systems. A critical step in understanding the mechanisms underlying social behaviors is a precise readout of the full 3D pose of interacting animals. While approaches for multi-animal pose estimation are beginning to emerge, they remain challenging to compare due to the lack of standardized benchmark datasets for multi-animal 3D pose estimation. Here we introduce the PAIR-R24M (Paired Acquisition of Interacting Rats) dataset for multi-animal 3D pose estimation, which contains 21.5 million frames of RGB video and 3D ground-truth motion capture of dyadic interactions in laboratory rats. PAIR-R24M contains data from 18 distinct pairs of rats across diverse behaviors, from 30 different viewpoints. The data are temporally contiguous and annotated with 11 behavioral categories, and 3 interaction behavioral categories, using a multi-animal extension of a recently developed behavioral segmentation approach. We used a novel multi-animal version of the recently published DANNCE network to establish a strong baseline for multi-animal 3D pose estimation without motion capture. These recordings are of sufficient resolution to allow us to examine cross-pair differences in social interactions, and identify different conserved patterns of social interaction across rats.

}, url = {https://openreview.net/forum?id=-wVVl_UPr8}, author = {Marshall, Jesse D and Klibaite, Ugne and Gellis, Amanda and Aldarondo, Diego E and Bence P {\"O}lveczky and Dunn, Tim} } @article {1410754, title = {The basal ganglia control the detailed kinematics of learned motor skills}, journal = {Nature Neuroscience }, year = {2021}, abstract = {

The basal ganglia are known to influence action selection and modulation of movement vigor, but whether and how they contribute to specifying the kinematics of learned motor skills is not understood. Here, we probe this question by recording and manipulating basal ganglia activity in rats trained to generate complex task-specific movement patterns with rich kinematic structure. We find that the sensorimotor arm of the basal ganglia circuit is crucial for generating the detailed movement patterns underlying the acquired motor skills. Furthermore, the neural representations in the striatum, and the control function they subserve, do not depend on inputs from the motor cortex. Taken together, these results extend our understanding of the basal ganglia by showing that they can specify and control the fine-grained details of learned motor skills through their interactions with lower-level motor circuits.

}, url = {https://www.nature.com/articles/s41593-021-00889-3}, author = {Ashesh K Dhawale and Wolff, Steffen B E and Ko, Raymond and Bence P {\"O}lveczky} } @article {1410747, title = {Geometric deep learning enables 3D kinematic profiling across species and environments}, journal = {Nature Methods}, volume = {18}, year = {2021}, pages = {564{\textendash}573}, abstract = { Comprehensive descriptions of animal behavior require precise three-dimensional (3D) measurements of whole-body movements. Although two-dimensional approaches can track visible landmarks in restrictive environments, performance drops in freely moving animals, due to occlusions and appearance changes. Therefore, we designed DANNCE to robustly track anatomical landmarks in 3D across species and behaviors. DANNCE uses projective geometry to construct inputs to a convolutional neural network that leverages learned 3D geometric reasoning. We trained and benchmarked DANNCE using a dataset of nearly seven million frames that relates color videos and rodent 3D poses. In rats and mice, DANNCE robustly tracked dozens of landmarks on the head, trunk, and limbs of freely moving animals in naturalistic settings. We extended DANNCE to datasets from rat pups, marmosets, and chickadees, and demonstrate quantitative profiling of behavioral lineage during development.}, url = {https://www.nature.com/articles/s41592-021-01106-6}, author = {Dunn, Timothy W and Marshall, Jesse D and Severson, Kyle S and Aldarondo, Diego E and Hildebrand, David G C and Chettih, Selmaan N and Wang, William L and Gellis, Amanda J and Carlson, David E and Dmitriy Aronov and Freiwald, Winrich A and Fan Wang and Bence P.{\"O}lveczky} } @article {1410739, title = {Animal pose estimation from video data with a hierarchical von Mises-Fisher-Gaussian model}, journal = {Proceedings of Machine Learning Research}, volume = {130}, year = {2021}, pages = {2800-2808}, abstract = {

Animal pose estimation from video data is an important step in many biological studies, but current methods struggle in complex environments where occlusions are common and training data is scarce. Recent work has demonstrated improved accuracy with deep neural networks, but these methods often do not incorporate prior distributions that could improve localization. Here we present GIMBAL: a hierarchical von Mises-Fisher-Gaussian model that improves upon deep networks{\textquoteright} estimates by leveraging spatiotemporal constraints. The spatial constraints come from the animal{\textquoteright}s skeleton, which induces a curved manifold of keypoint configurations. The temporal constraints come from the postural dynamics, which govern how angles between keypoints change over time. Importantly, the conditional conjugacy of the model permits simple and efficient Bayesian inference algorithms. We assess the model on a unique experimental dataset with video of a freely-behaving rodent from multiple viewpoints and ground-truth motion capture data for 20 keypoints. GIMBAL extends existing techniques, and in doing so offers more accurate estimates of keypoint positions, especially in challenging contexts.

}, url = {http://proceedings.mlr.press/v130/zhang21h.html}, author = {Zhang, Libby and Dunn, Tim and Marshall, Jesse and Bence P {\"O}lveczky and Linderman, Scott} } @article {1389695, title = {Continuous Whole-Body 3D Kinematic Recordings across the Rodent Behavioral Repertoire}, journal = {Neuron}, volume = {109}, year = {2020}, pages = {420-437.e8}, abstract = { In mammalian animal models, high-resolution kinematic tracking is restricted to brief sessions in constrained environments, limiting our ability to probe naturalistic behaviors and their neural underpinnings. To address this, we developed CAPTURE (Continuous Appendicular and Postural Tracking Using Retroreflector Embedding), a behavioral monitoring system that combines motion capture and deep learning to continuously track the 3D kinematics of a rat{\textquoteright}s head, trunk, and limbs for week-long timescales in freely behaving animals. CAPTURE realizes 10- to 100-fold gains in precision and robustness compared with existing convolutional network approaches to behavioral tracking. We demonstrate CAPTURE{\textquoteright}s ability to comprehensively profile the kinematics and sequential organization of natural rodent behavior, its variation across individuals, and its perturbation by drugs and disease, including identifying perseverative grooming states in a rat model of fragile X syndrome. CAPTURE significantly expands the range of behaviors and contexts that can be quantitatively investigated, opening the door to a new understanding of natural behavior and its neural basis.}, url = {https://www.sciencedirect.com/science/article/pii/S0896627320308941}, author = {Marshall, Jesse D and Aldarondo, Diego E and Dunn, Timothy W and Wang, William L and Berman, Gordon J and Bence P {\"O}lveczky} } @article {1389694, title = {The neurobiology of deep reinforcement learning}, journal = {Current Biology}, volume = {30}, year = {2020}, pages = {R629-R632}, abstract = {

To generate adaptive behaviors, animals must learn from their interactions with the environment. Describing the algorithms that govern this learning process and how they are implemented in the brain is a major goal of neuroscience. Careful and controlled observations of animal learning by Thorndike, Pavlov and others, now more than a century ago, identified intuitive rules by which animals (including humans) can learn from their experiences by associating sensory stimuli and motor actions with rewards. But going from explaining learning in simple paradigms to deciphering how complex problems are solved in rich and dynamic environments has proven difficult (Figure 1). Recently, this effort has received help from computer scientists and engineers hoping to emulate intelligent adaptive behaviors in machines. Inspired by the animal behavior literature, pioneers in artificial intelligence developed a rigorous and mathematically principled framework within which reward-based learning can be formalized and studied. Not only has the field of reinforcement learning become a boon to machine learning and artificial intelligence, it has also provided a theoretical foundation for biologists interested in deciphering how the brain implements reinforcement learning algorithms. The ability of reinforcement learning agents to solve complex, high-dimensional learning problems has been dramatically enhanced by using deep neural networks (deep reinforcement learning, Figure 1). Indeed, aided by ever-increasing computational resources, deep reinforcement learning algorithms can now outperform human experts on a host of well-defined complex tasks\ {\textellipsis}

}, url = {https://gershmanlab.com/pubs/GershmanOlveczky20.pdf}, author = {Gershman, Samuel J and Bence P {\"O}lveczky} } @article {1389693, title = {Discovering precise temporal patterns in large-scale neural recordings through robust and interpretable time warping}, journal = {Neuron}, volume = {105}, year = {2020}, pages = {246-259. e8}, abstract = { Though the temporal precision of neural computation has been studied intensively, a data-driven determination of this precision remains a fundamental challenge. Reproducible spike patterns may be obscured on single trials by uncontrolled temporal variability in behavior and cognition and may not be time locked to measurable signatures in behavior or local field potentials (LFP). To overcome these challenges, we describe a general-purpose time warping framework that reveals precise spike-time patterns in an unsupervised manner, even when these patterns are decoupled from behavior or are temporally stretched across single trials. We demonstrate this method across diverse systems: cued reaching in nonhuman primates, motor sequence production in rats, and olfaction in mice. This approach flexibly uncovers diverse dynamical firing patterns, including pulsatile responses to behavioral events, LFP-aligned oscillatory spiking, and even unanticipated patterns, such as 7\ Hz oscillations in rat motor cortex that are not time locked to measured behaviors or LFP.}, url = {https://www.sciencedirect.com/science/article/pii/S0896627319308943}, author = {Alex H Williams and Ben Poole and Niru Maheswaranathan and Ashesh K Dhawale and Tucker Fisher and Christopher D Wilson and David H Brann and Eric M Trautmann and Stephen Ryu and Roman Shusterman and Dmitry Rinberg and Bence P {\"O}lveczky and Krishna V Shenoy and Surya Ganguli} } @article {1235824, title = {Deep neuroethology of a virtual rodent}, journal = { arXiv}, year = {2019}, abstract = {

Parallel developments in neuroscience and deep learning have led to mutually productive exchanges, pushing our understanding of real and artificial neural networks in sensory and cognitive systems. However, this interaction between fields is less developed in the study of motor control. In this work, we develop a virtual rodent as a platform for the grounded study of motor activity in artificial models of embodied control. We then use this platform to study motor activity across contexts by training a model to solve four complex tasks. Using methods familiar to neuroscientists, we describe the behavioral representations and algorithms employed by different layers of the network using a neuroethological approach to characterize motor activity relative to the rodent{\textquoteright}s behavior and goals. We find that the model uses two classes of representations which respectively encode the task-specific behavioral strategies and task-invariant behavioral kinematics. These representations are reflected in the sequential activity and population dynamics of neural subpopulations. Overall, the virtual rodent facilitates grounded collaborations between deep reinforcement learning and motor neuroscience.

arXiv:1911.09451v1

}, url = {https://arxiv.org/abs/1911.09451}, author = {Merel, J and Aldarondo, D and Marshall, J and Tassa, Y and Wayne, G and {\"O}lveczky, B. P.} } @article {1229189, title = {Adaptive Regulation of Motor Variability.}, journal = {Current Biology}, volume = {pii: S0960-9822}, year = {2019}, pages = {31102-9}, abstract = {

Trial-to-trial movement variability can both drive motor learning and interfere with expert performance, suggesting benefits of regulating it in context-specific ways. Here we address whether and how the brain regulates motor variability as a function of performance by training rats to execute ballistic forelimb movements for reward. Behavioral datasets comprising millions of trials revealed that motor variability is regulated by two distinct processes. A fast process modulates variability as a function of recent trial outcomes, increasing it when performance is poor and vice versa. A slower process tunes the gain of the fast process based on the uncertainty in the task{\textquoteright}s reward landscape. Simulations demonstrated that this regulation strategy optimizes reward accumulation over a wide range of time horizons, while also promoting learning. Our results uncover a sophisticated algorithm implemented by the brain to adaptively regulate motor variability to improve task performance. VIDEO ABSTRACT.

PMID:31630947\  DOI:10.1016/j.cub.2019.08.052

}, url = {https://www.ncbi.nlm.nih.gov/pubmed/31630947}, author = {Dhawale, A. K. and Miyamoto, Y. R. and Smith, M. A. and {\"O}lveczky, B. P.} } @article {1229183, title = {What is cognition?}, journal = {Current Biology}, volume = {29}, year = {2019}, pages = {R608-R615}, abstract = {

Eleven authors with disparate relevant backgrounds give their view on what is meant by the word "cognition".

PMID: 31287972\ \  doi: 10.1016/j.cub.2019.05.044.

}, url = {https://www.sciencedirect.com/science/article/pii/S0960982219306141?via\%3Dihub}, author = {Bayne, T and Brainard, D and Byrne, RW and Chittka, L and Heyes, C and Mather, J and {\"O}lveczky, B. P. and Shadlen, M and Suddendorf, T and Webb, B} } @article {1133401, title = {The promise and perils of causal circuit manipulations}, journal = {Current Opinion in Neurobiology}, volume = {49}, year = {2018}, pages = {84-94}, abstract = {

The development of increasingly sophisticated methods for recording and manipulating neural activity is revolutionizing neuroscience. By probing how activity patterns in different types of neurons and circuits contribute to behavior, these tools can help inform mechanistic models of brain function and explain the roles of distinct circuit elements. However, in systems where functions are distributed over large networks, interpreting causality experiments can be challenging. Here we review common assumptions underlying circuit manipulations in behaving animals and discuss the strengths and limitations of different approaches.

}, url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5957484/}, author = {SBE Wolff and {\"O}lveczky, B. P.} } @article {1133400, title = {Flexibility in motor timing constrains the topology and dynamics of pattern generator circuits}, journal = {Nature Communications}, volume = {9}, year = {2018}, url = {https://www.nature.com/articles/s41467-018-03261-5}, author = {Pehlevan, C. and Ali, F. and {\"O}lveczky, B. P.} } @article {1094866, title = {Automated long-term recording and analysis of neural activity in behaving animals.}, journal = {Elife. Sep 8;6. pii: e27702}, year = {2017}, abstract = {Addressing how neural circuits underlie behavior is routinely done by measuring electrical activity from single neurons in experimental sessions. While such recordings yield snapshots of neural dynamics during specified tasks, they are ill-suited for tracking single-unit activity over longer timescales relevant for most developmental and learning processes, or for capturing neural dynamics across different behavioral states. Here we describe an automated platform for continuous long-term recordings of neural activity and behavior in freely moving rodents. An unsupervised algorithm identifies and tracks the activity of single units over weeks of recording, dramatically simplifying the analysis of large datasets. Months-long recordings from motor cortex and striatum made and analyzed with our system revealed remarkable stability in basic neuronal properties, such as firing rates and inter-spike interval distributions. Interneuronal correlations and the representation of different movements and behaviors were similarly stable. This establishes the feasibility of high-throughput long-term extracellular recordings in behaving animals.}, url = {https://www.ncbi.nlm.nih.gov/pubmed/28885141}, author = {Dhawale, A. K. and Poddar, R. and SB Wolff and VA Normand and E Kopelowitz and {\"O}lveczky, B. P.} } @article {1013071, title = {The Role of Variability in Motor Learning.}, journal = {Annu Rev Neurosci. 2017 May 10. doi: 10.1146/annurev-neuro-072116-031548}, year = {2017}, abstract = {

Trial-to-trial variability in the execution of movements and motor skills is ubiquitous and widely considered to be the unwanted consequence of a noisy nervous system. However, recent studies have suggested that motor variability may also be a feature of how sensorimotor systems operate and learn. This view, rooted in reinforcement learning theory, equates motor variability with purposeful exploration of motor space that, when coupled with reinforcement, can drive motor learning. Here we review studies that explore the relationship between motor variability and motor learning in both humans and animal models. We discuss neural circuit mechanisms that underlie the generation and regulation of motor variability and consider the implications that this work has for our understanding of motor learning. Expected final online publication date for the Annual Review of Neuroscience Volume 40 is July 8, 2017. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

PMID:\ 28489490
DOI:\ 10.1146/annurev-neuro-072116-031548

}, author = {Dhawale, A. K. and Smith, M. A. and {\"O}lveczky, B. P.} } @article {990291, title = {Rules and mechanisms for efficient two-stage learning in neural circuits}, journal = {eLife 2017 April }, volume = {6}, year = {2017}, pages = {e20944}, abstract = {

Trial-and-error learning requires evaluating variable actions and reinforcing successful variants. In songbirds, vocal exploration is induced by LMAN, the output of a basal ganglia-related circuit that also contributes a corrective bias to the vocal output. This bias is gradually consolidated in RA, a motor cortex analogue downstream of LMAN. We develop a new model of such two-stage learning. Using stochastic gradient descent, we derive how the activity in {\textquoteright}tutor{\textquoteright} circuits (e.g.,\ LMAN) should match plasticity mechanisms in {\textquoteright}student{\textquoteright} circuits (e.g.,\ RA) to achieve efficient learning. We further describe a reinforcement learning framework through which the tutor can build its teaching signal. We show that mismatches between the tutor signal and the plasticity mechanism can impair learning. Applied to birdsong, our results predict the temporal structure of the corrective bias from LMAN given a plasticity rule in RA. Our framework can be applied predictively to other paired brain areas showing two-stage learning.

PMID:\ 28374674

PMCID:\ PMC5380437
DOI:\ 10.7554/eLife.20944
}, url = {https://elifesciences.org/content/6/e20944}, author = {Tiberiu Te{\c s}ileanu and Bence {\"O}lveczky and Vijay Balasubramanian} } @article {742451, title = {Acute off-target effects of neural circuit manipulations}, journal = {Nature}, year = {2015}, pages = {doi:10.1038/nature16442}, abstract = {

Rapid and reversible manipulations of neural activity in behaving animals are transforming our understanding of brain function. An important assumption underlying much of this work is that evoked behavioural changes reflect the function of the manipulated circuits. We show that this assumption is problematic because it disregards indirect effects on the independent functions of downstream circuits. Transient inactivations of motor cortex in rats and nucleus interface (Nif) in songbirds severely degraded task-specific movement patterns and courtship songs, respectively, which are learned skills that recover spontaneously after permanent lesions of the same areas. We resolve this discrepancy in songbirds, showing that Nif silencing acutely affects the function of HVC, a downstream song control nucleus. Paralleling song recovery, the off-target effects resolved within days of Nif lesions, a recovery consistent with homeostatic regulation of neural activity in HVC. These results have implications for interpreting transient circuit manipulations and for understanding recovery after brain lesions.

}, url = {http://www.nature.com/nature/journal/vaop/ncurrent/full/nature16442.html}, author = {Otchy, T. M. and SBE Wolff and JY Rhee and Pehlevan, C. and Kawai, R. and A Kempf and SMH Gobes and BP. {\"O}lveczky} } @article {738231, title = {Automated long-term recording and analysis of neural activity in behaving animals}, journal = {Bioarxiv}, volume = {doi: http://dx.doi.org/10.1101/033266}, year = {2015}, abstract = {

Addressing how neural circuits underlie behavior is routinely done by measuring electrical activity from single neurons during experimental sessions. While such recordings yield snapshots of neural dynamics during specified tasks, they are ill-suited for tracking single-unit activity over longer timescales relevant for most developmental and learning processes, or for capturing neural dynamics outside of task context. Here we describe an automated platform for continuous long-term recordings of neural activity and behavior in freely moving animals. An unsupervised algorithm identifies and tracks the activity of single units over weeks of recording, dramatically simplifying the analysis of large datasets. Months-long recordings from motor cortex and striatum made and analyzed with our system revealed remarkable stability in basic neuronal properties, such as firing rates and inter-spike interval distributions. Interneuronal correlations and the representation of different movements and behaviors were similarly stable. This establishes the feasibility of high-throughput long-term extracellular recordings in behaving animals.

}, url = {http://biorxiv.org/content/early/2015/11/30/033266}, author = {Dhawale, A. K. and Poddar, R. and E Kopelowitz and V Normand and S Wolff and {\"O}lveczky, B. P.} } @article {587086, title = {Motor Cortex Is Required for Learning but Not for Executing a Motor Skill}, journal = {Neuron}, volume = {86}, number = {3}, year = {2015}, note = {

Kawai, RisaMarkman, TimothyPoddar, RajeshKo, RaymondFantana, Antoniu LDhawale, Ashesh KKampff, Adam ROlveczky, Bence PENG2015/04/22 06:00Neuron. 2015 May 6;86(3):800-812. doi: 10.1016/j.neuron.2015.03.024. Epub 2015 Apr 16.

}, month = {May 6}, pages = {800-812}, abstract = {

Motor cortex is widely believed to underlie the acquisition and execution of motor skills, but its contributions to these processes are not fully understood. One reason is that studies on motor skills often conflate motor cortex{\textquoteright}s established role in dexterous control with roles in learning and producing task-specific motor sequences. To dissociate these aspects, we developed a motor task for rats that trains spatiotemporally precise movement patterns without requirements for dexterity. Remarkably, motor cortex lesions had no discernible effect on the acquired skills, which were expressed in their distinct pre-lesion forms on the very first day of post-lesion training. Motor cortex lesions prior to training, however, rendered rats unable to acquire the stereotyped motor sequences required for the task. These results suggest a remarkable capacity of subcortical motor circuits to execute learned skills and a previously unappreciated role for motor cortex in "tutoring" these circuits during learning.

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Supplementary Videos:\ 
Video1,\ Video2,\ Video3,\ Video4,\ Video5.

}, isbn = {1097-4199 (Electronic)0896-6273 (Linking)}, author = {Kawai, R. and Markman, T. and Poddar, R. and Ko, R. and Fantana, A. L. and Dhawale, A. K. and Kampff, A. R. and {\"O}lveczky, B. P.} } @article {587081, title = {A neural circuit mechanism for regulating vocal variability during song learning in zebra finches}, journal = {eLife}, volume = {4}, year = {2015}, note = {

Garst-Orozco, JonathanBabadi, BaktashOlveczky, Bence PengR01 NS066408/NS/NINDS NIH HHS/Research Support, N.I.H., ExtramuralResearch Support, Non-U.S. Gov{\textquoteright}tEngland2014/12/17 06:00Elife. 2014 Dec 15;4:e03697. doi: 10.7554/eLife.03697.

}, pages = {e03697}, abstract = {

Motor skill learning is characterized by improved performance and reduced motor variability. The neural mechanisms that couple skill level and variability, however, are not known. The zebra finch, a songbird, presents a unique opportunity to address this question because production of learned song and induction of vocal variability are instantiated in distinct circuits that converge on a motor cortex analogue controlling vocal output. To probe the interplay between learning and variability, we made intracellular recordings from neurons in this area, characterizing how their inputs from the functionally distinct pathways change throughout song development. We found that inputs that drive stereotyped song-patterns are strengthened and pruned, while inputs that induce variability remain unchanged. A simple network model showed that strengthening and pruning of action-specific connections reduces the sensitivity of motor control circuits to variable input and neural {\textquoteright}noise{\textquoteright}. This identifies a simple and general mechanism for learning-related regulation of motor variability.

Link

}, keywords = {motor control, motor learning, motor variability, neuroscience, reinforcement learning, songbird, synaptic plasticity}, isbn = {2050-084X (Electronic)2050-084X (Linking)}, author = {Garst-Orozco, J. and Babadi, B. and {\"O}lveczky, B. P.} } @article {587091, title = {Learning precisely timed spikes}, journal = {Neuron}, volume = {82}, number = {4}, year = {2014}, note = {

Memmesheimer, Raoul-MartinRubin, RanOlveczky, Bence PSompolinsky, HaimengResearch Support, Non-U.S. Gov{\textquoteright}t2014/04/29 06:00Neuron. 2014 May 21;82(4):925-38. doi: 10.1016/j.neuron.2014.03.026. Epub 2014 Apr 24.

}, month = {May 21}, pages = {925-38}, abstract = {

To signal the onset of salient sensory features or execute well-timed motor sequences, neuronal circuits must transform streams of incoming spike trains into precisely timed firing. To address the efficiency and fidelity with which neurons can perform such computations, we developed a theory to characterize the capacity of feedforward networks to generate desired spike sequences. We find the maximum number of desired output spikes a neuron can implement to be 0.1-0.3 per synapse. We further present a biologically plausible learning rule that allows feedforward and recurrent networks to learn multiple mappings between inputs and desired spike sequences. We apply this framework to reconstruct synaptic weights from spiking activity and study the precision with which the temporal structure of ongoing behavior can be inferred from the spiking of premotor neurons. This work provides a powerful approach for characterizing the computational and learning capacities of single neurons and neuronal circuits.

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}, keywords = {Action Potentials/*physiology, Algorithms, Animals, Brain/*cytology, Humans, Learning/*physiology, Models, Neurological, Nerve Net/physiology, Neurons/*physiology, Nonlinear Dynamics, Synapses/physiology, Time Factors}, isbn = {1097-4199 (Electronic)0896-6273 (Linking)}, author = {Memmesheimer, R. M. and Rubin, R. and {\"O}lveczky, B. P. and Sompolinsky, H.} } @article {587101, title = {Neuroscience: Ordered randomness in fly love songs}, journal = {Nature}, volume = {507}, number = {7491}, year = {2014}, note = {

Olveczky, Bence PengCommentEngland2014/03/07 06:00Nature. 2014 Mar 13;507(7491):177-8. doi: 10.1038/nature13208. Epub 2014 Mar 5.

}, month = {Mar 13}, pages = {177-8}, abstract = {

PDF

}, keywords = {*Animal Communication, *Courtship, *Vibration, Animals, Drosophila melanogaster/*physiology, Female, Male, Wing/*physiology}, isbn = {1476-4687 (Electronic)0028-0836 (Linking)}, author = {{\"O}lveczky, B. P.} } @article {587166, title = {Temporal structure of motor variability is dynamically regulated and predicts motor learning ability}, journal = {Nat Neurosci}, volume = {17}, number = {2}, year = {2014}, note = {

Wu, Howard GMiyamoto, Yohsuke RGonzalez Castro, Luis NicolasOlveczky, Bence PSmith, Maurice AengR01 AG041878/AG/NIA NIH HHS/R01 NS066408/NS/NINDS NIH HHS/Research Support, N.I.H., ExtramuralResearch Support, Non-U.S. Gov{\textquoteright}t2014/01/15 06:00Nat Neurosci. 2014 Feb;17(2):312-21. doi: 10.1038/nn.3616. Epub 2014 Jan 12.

}, month = {Feb}, pages = {312-21}, abstract = {

Individual differences in motor learning ability are widely acknowledged, yet little is known about the factors that underlie them. Here we explore whether movement-to-movement variability in motor output, a ubiquitous if often unwanted characteristic of motor performance, predicts motor learning ability. Surprisingly, we found that higher levels of task-relevant motor variability predicted faster learning both across individuals and across tasks in two different paradigms, one relying on reward-based learning to shape specific arm movement trajectories and the other relying on error-based learning to adapt movements in novel physical environments. We proceeded to show that training can reshape the temporal structure of motor variability, aligning it with the trained task to improve learning. These results provide experimental support for the importance of action exploration, a key idea from reinforcement learning theory, showing that motor variability facilitates motor learning in humans and that our nervous systems actively regulate it to improve learning.

Nature Neuroscience.\ January 2014. (pdf). Write up in the Harvard Gazette\ here.\ News and Views from Nature Neuroscience\ (pdf).*Co-senior authors.

}, keywords = {*Individuality, *Reward, Adolescent, Adult, Female, Humans, Learning/*physiology, Male, Middle Aged, Movement/*physiology, Nonlinear Dynamics, Predictive Value of Tests, Psychomotor Performance/*physiology, Time Factors, Young Adult}, isbn = {1546-1726 (Electronic)1097-6256 (Linking)}, author = {Wu, H. G. and Miyamoto, Y. R. and Gonzalez Castro, L. N. and {\"O}lveczky, B. P. and Smith, M. A.} } @article {587066, title = {The basal ganglia is necessary for learning spectral, but not temporal, features of birdsong}, journal = {Neuron}, volume = {80}, number = {2}, year = {2013}, note = {

Ali, FarhanOtchy, Timothy MPehlevan, CengizFantana, Antoniu LBurak, YoramOlveczky, Bence PengR01 NS066408/NS/NINDS NIH HHS/T32 MH020017/MH/NIMH NIH HHS/Research Support, N.I.H., ExtramuralResearch Support, Non-U.S. Gov{\textquoteright}t2013/10/01 06:00Neuron. 2013 Oct 16;80(2):494-506. doi: 10.1016/j.neuron.2013.07.049. Epub 2013 Sep 26.

}, month = {Oct 16}, pages = {494-506}, abstract = {

Executing a motor skill requires the brain to control which muscles to activate at what times. How these aspects of control-motor implementation and timing-are acquired, and whether the learning processes underlying them differ, is not well understood. To address this, we used a reinforcement learning paradigm to independently manipulate both spectral and temporal features of birdsong, a complex learned motor sequence, while recording and perturbing activity in underlying circuits. Our results uncovered a striking dissociation in how neural circuits underlie learning in the two domains. The basal ganglia was required for modifying spectral, but not temporal, structure. This functional dissociation extended to the descending motor pathway, where recordings from a premotor cortex analog nucleus reflected changes to temporal, but not spectral, structure. Our results reveal a strategy in which the nervous system employs different and largely independent circuits to learn distinct aspects of a motor skill.

Neuron. 80(2):494-506. September 2013 (pdf). Write up in the Harvard Gazette\ here.\ Software package and users manual for implementing the CAF experiments described in the paper can be downloaded\ here.

}, keywords = {Animals, Basal Ganglia/*physiology, Finches/*physiology, Learning/*physiology, Motor Cortex/*physiology, Neural Pathways/physiology, Reinforcement (Psychology), Thalamus/*physiology, Time Factors, Vocalization, Animal/*physiology}, isbn = {1097-4199 (Electronic)0896-6273 (Linking)}, author = {Ali, F. and Otchy, T. M. and Pehlevan, C. and Fantana, A. L. and Burak, Y. and {\"O}lveczky, B. P.} } @article {587151, title = {A fully automated high-throughput training system for rodents}, journal = {PLoS One}, volume = {8}, number = {12}, year = {2013}, note = {

Poddar, RajeshKawai, RisaOlveczky, Bence PengResearch Support, Non-U.S. Gov{\textquoteright}t2013/12/19 06:00PLoS One. 2013 Dec 6;8(12):e83171. doi: 10.1371/journal.pone.0083171. eCollection 2013.

}, pages = {e83171}, abstract = {

Addressing the neural mechanisms underlying complex learned behaviors requires training animals in well-controlled tasks, an often time-consuming and labor-intensive process that can severely limit the feasibility of such studies. To overcome this constraint, we developed a fully computer-controlled general purpose system for high-throughput training of rodents. By standardizing and automating the implementation of predefined training protocols within the animal{\textquoteright}s home-cage our system dramatically reduces the efforts involved in animal training while also removing human errors and biases from the process. We deployed this system to train rats in a variety of sensorimotor tasks, achieving learning rates comparable to existing, but more laborious, methods. By incrementally and systematically increasing the difficulty of the task over weeks of training, rats were able to master motor tasks that, in complexity and structure, resemble ones used in primate studies of motor sequence learning. By enabling fully automated training of rodents in a home-cage setting this low-cost and modular system increases the utility of rodents for studying the neural underpinnings of a variety of complex behaviors.

PDF

}, keywords = {Animals, Humans, Learning/physiology, Motor Skills/physiology, Physical Conditioning, Animal/*instrumentation/*methods, Rats}, isbn = {1932-6203 (Electronic)1932-6203 (Linking)}, author = {Poddar, R. and Kawai, R. and {\"O}lveczky, B. P.} } @article {587141, title = {Design and assembly of an ultra-light motorized microdrive for chronic neural recordings in small animals}, journal = {J Vis Exp}, number = {69}, year = {2012}, note = {

Otchy, Timothy MOlveczky, Bence Peng1R01NS066408-01A1/NS/NINDS NIH HHS/T32 MH020017/MH/NIMH NIH HHS/Research Support, N.I.H., ExtramuralResearch Support, Non-U.S. Gov{\textquoteright}tVideo-Audio Media2012/11/22 06:00J Vis Exp. 2012 Nov 8;(69). pii: 4314. doi: 10.3791/4314.

}, abstract = {

The ability to chronically record from populations of neurons in freely behaving animals has proven an invaluable tool for dissecting the function of neural circuits underlying a variety of natural behaviors, including navigation(1) , decision making (2,3), and the generation of complex motor sequences(4,5,6). Advances in precision machining has allowed for the fabrication of light-weight devices suitable for chronic recordings in small animals, such as mice and songbirds. The ability to adjust the electrode position with small remotely controlled motors has further increased the recording yield in various behavioral contexts by reducing animal handling.(6,7) Here we describe a protocol to build an ultra-light motorized microdrive for long-term chronic recordings in small animals. Our design evolved from an earlier published version(7), and has been adapted for ease-of use and cost-effectiveness to be more practical and accessible to a wide array of researchers. This proven design (8,9,10,11) allows for fine, remote positioning of electrodes over a range of ~ 5 mm and weighs less than 750 mg when fully assembled. We present the complete protocol for how to build and assemble these drives, including 3D CAD drawings for all custom microdrive components.

November 2012 (pdf).

}, keywords = {*Electrodes, Implanted, Animals, Electrophysiology/*instrumentation/methods, Equipment Design, Mice, Neurons/*physiology, Songbirds}, isbn = {1940-087X (Electronic)1940-087X (Linking)}, author = {Otchy, T. M. and {\"O}lveczky, B. P.} } @article {587156, title = {Motor circuits are required to encode a sensory model for imitative learning}, journal = {Nat Neurosci}, volume = {15}, number = {10}, year = {2012}, note = {

Roberts, Todd FGobes, Sharon M HMurugan, MalavikaOlveczky, Bence PMooney, RichardengR01 DC002524/DC/NIDCD NIH HHS/R01 NS066408/NS/NINDS NIH HHS/R01DC02524/DC/NIDCD NIH HHS/Research Support, N.I.H., ExtramuralResearch Support, Non-U.S. Gov{\textquoteright}tResearch Support, U.S. Gov{\textquoteright}t, Non-P.H.S.2012/09/18 06:00Nat Neurosci. 2012 Oct;15(10):1454-9. doi: 10.1038/nn.3206. Epub 2012 Sep 16.

}, month = {Oct}, pages = {1454-9}, abstract = {

Premotor circuits help generate imitative behaviors and can be activated during observation of another animal{\textquoteright}s behavior, leading to speculation that these circuits participate in sensory learning that is important to imitation. Here we tested this idea by focally manipulating the brain activity of juvenile zebra finches, which learn to sing by memorizing and vocally copying the song of an adult tutor. Tutor song-contingent optogenetic or electrical disruption of neural activity in the pupil{\textquoteright}s song premotor nucleus HVC prevented song copying, indicating that a premotor structure important to the temporal control of birdsong also helps encode the tutor song. In vivo multiphoton imaging and neural manipulations delineated a pathway and a candidate synaptic mechanism through which tutor song information is encoded by premotor circuits. These findings provide evidence that premotor circuits help encode sensory information about the behavioral model before shaping and executing imitative behaviors.

Nature Neuroscience. 15(10):1454-9. October 2012 (PDF). *Co-senior authors.

}, keywords = {Animals, Brain/*physiology, Finches, Imitative Behavior/*physiology, Learning/*physiology, Microscopy, Fluorescence, Multiphoton/methods/psychology, Models, Neurological, Neural Pathways/physiology, Optogenetics/methods/psychology, Singing/*physiology}, isbn = {1546-1726 (Electronic)1097-6256 (Linking)}, author = {Roberts, T. F. and Gobes, S. M. and Murugan, M. and {\"O}lveczky, B. P.* and Mooney, R.*} } @article {587121, title = {A bird{\textquoteright}s eye view of neural circuit formation}, journal = {Curr Opin Neurobiol}, volume = {21}, number = {1}, year = {2011}, note = {

Olveczky, Bence PGardner, Timothy JengR01 NS066408/NS/NINDS NIH HHS/R01 NS066408-01A1/NS/NINDS NIH HHS/R01 NS066408-02/NS/NINDS NIH HHS/R01 NS66408/NS/NINDS NIH HHS/Research Support, N.I.H., ExtramuralResearch Support, Non-U.S. Gov{\textquoteright}tReviewEngland2010/10/15 06:00Curr Opin Neurobiol. 2011 Feb;21(1):124-31. doi: 10.1016/j.conb.2010.08.001. Epub 2010 Oct 11.

}, month = {Feb}, pages = {124-31}, abstract = {

Neural circuits underlying complex learned behaviors, such as speech in humans, develop under genetic constraints and in response to environmental influences. Little is known about the rules and mechanisms through which such circuits form. We argue that songbirds, with their discrete and well studied neural pathways underlying a complex and naturally learned behavior, provide a powerful model for addressing these questions. We briefly review current knowledge of how the song circuit develops during learning and discuss new possibilities for advancing the field given recent technological advances.

Feb 2011 (pdf).

}, keywords = {Animals, Learning/*physiology, Neural Pathways, Songbirds/*physiology, Vocalization, Animal/physiology}, isbn = {1873-6882 (Electronic)0959-4388 (Linking)}, author = {{\"O}lveczky, B. P. and Gardner, T. J.} } @article {587126, title = {Changes in the neural control of a complex motor sequence during learning}, journal = {J Neurophysiol}, volume = {106}, number = {1}, year = {2011}, note = {

Olveczky, Bence POtchy, Timothy MGoldberg, Jesse HAronov, DmitriyFee, Michale SengK99 NS067062/NS/NINDS NIH HHS/R01 NS66408/NS/NINDS NIH HHS/R01DC009183/DC/NIDCD NIH HHS/Research Support, N.I.H., ExtramuralResearch Support, Non-U.S. Gov{\textquoteright}t2011/05/06 06:00J Neurophysiol. 2011 Jul;106(1):386-97. doi: 10.1152/jn.00018.2011. Epub 2011 May 4.

}, month = {Jul}, pages = {386-97}, abstract = {

The acquisition of complex motor sequences often proceeds through trial-and-error learning, requiring the deliberate exploration of motor actions and the concomitant evaluation of the resulting performance. Songbirds learn their song in this manner, producing highly variable vocalizations as juveniles. As the song improves, vocal variability is gradually reduced until it is all but eliminated in adult birds. In the present study we examine how the motor program underlying such a complex motor behavior evolves during learning by recording from the robust nucleus of the arcopallium (RA), a motor cortex analog brain region. In young birds, neurons in RA exhibited highly variable firing patterns that throughout development became more precise, sparse, and bursty. We further explored how the developing motor program in RA is shaped by its two main inputs: LMAN, the output nucleus of a basal ganglia-forebrain circuit, and HVC, a premotor nucleus. Pharmacological inactivation of LMAN during singing made the song-aligned firing patterns of RA neurons adultlike in their stereotypy without dramatically affecting the spike statistics or the overall firing patterns. Removing the input from HVC, on the other hand, resulted in a complete loss of stereotypy of both the song and the underlying motor program. Thus our results show that a basal ganglia-forebrain circuit drives motor exploration required for trial-and-error learning by adding variability to the developing motor program. As learning proceeds and the motor circuits mature, the relative contribution of LMAN is reduced, allowing the premotor input from HVC to drive an increasingly stereotyped song.

May 2011 (pdf). * Corresponding author.

}, keywords = {*Animal Communication, Animals, Basal Ganglia/physiology, Finches/*physiology, Learning/*physiology, Male, Motor Activity/physiology, Motor Cortex/*physiology, Neural Pathways/physiology, Neurons/physiology, Prosencephalon/physiology}, isbn = {1522-1598 (Electronic)0022-3077 (Linking)}, author = {{\"O}lveczky, B. P.* and Otchy, T. M. and Goldberg, J. H. and Aronov, D. and Fee, M. S.} } @article {587096, title = {Motoring ahead with rodents}, journal = {Curr Opin Neurobiol}, volume = {21}, number = {4}, year = {2011}, note = {

Olveczky, Bence PengR01 NS66408/NS/NINDS NIH HHS/Research Support, N.I.H., ExtramuralResearch Support, Non-U.S. Gov{\textquoteright}tEngland2011/06/02 06:00Curr Opin Neurobiol. 2011 Aug;21(4):571-8. doi: 10.1016/j.conb.2011.05.002. Epub 2011 May 31.

}, month = {Aug}, pages = {571-8}, abstract = {

How neural circuits underlie the acquisition and control of learned motor behaviors has traditionally been explored in monkeys and, more recently, songbirds. The development of genetic tools for functional circuit analysis in rodents, the availability of transgenic animals with well characterized phenotypes, and the relative ease with which rats and mice can be trained to perform various motor tasks, make rodents attractive models for exploring the neural circuit mechanisms underlying the acquisition and production of learned motor skills. Here we discuss the advantages and drawbacks of this approach, review recent trends and results, and outline possible strategies for wider adoption of rodents as a model system for complex motor learning.

\ Aug 2011 (pdf)

}, keywords = {Animals, Animals, Genetically Modified, Brain/anatomy \& histology/physiology, Humans, Learning/*physiology, Motor Activity/genetics/*physiology, Motor Skills/*physiology, Neural Pathways/physiology, Rodentia/*physiology}, isbn = {1873-6882 (Electronic)0959-4388 (Linking)}, author = {{\"O}lveczky, B. P.} } @article {587071, title = {A retinal circuit that computes object motion}, journal = {J Neurosci}, volume = {28}, number = {27}, year = {2008}, note = {

Baccus, Stephen AOlveczky, Bence PManu, MihaiMeister, MarkusengResearch Support, N.I.H., ExtramuralResearch Support, Non-U.S. Gov{\textquoteright}t2008/07/04 09:00J Neurosci. 2008 Jul 2;28(27):6807-17. doi: 10.1523/JNEUROSCI.4206-07.2008.

}, month = {Jul 2}, pages = {6807-17}, abstract = {

Certain ganglion cells in the retina respond sensitively to differential motion between the receptive field center and surround, as produced by an object moving over the background, but are strongly suppressed by global image motion, as produced by the observer{\textquoteright}s head or eye movements. We investigated the circuit basis for this object motion sensitive (OMS) response by recording intracellularly from all classes of retinal interneurons while simultaneously recording the spiking output of many ganglion cells. Fast, transient bipolar cells respond linearly to motion in the receptive field center. The synaptic output from their terminals is rectified and then pooled by the OMS ganglion cell. A type of polyaxonal amacrine cell is driven by motion in the surround, again via pooling of rectified inputs, but from a different set of bipolar cell terminals. By direct intracellular current injection, we found that these polyaxonal amacrine cells selectively suppress the synaptic input of OMS ganglion cells. A quantitative model of these circuit elements and their interactions explains how an important visual computation is accomplished by retinal neurons and synapses.

J. Neuroscience. 28:6807-6817. July 2008 (PDF).

}, keywords = {Action Potentials/physiology, Amacrine Cells/physiology, Ambystoma, Animals, Computer Simulation, Motion Perception/*physiology, Nerve Net/cytology/*physiology, Neural Inhibition/physiology, Neural Pathways/cytology/physiology, Neurons/*physiology, Organ Culture Techniques, Retina/cytology/*physiology, Retinal Bipolar Cells/physiology, Retinal Ganglion Cells/physiology, Synapses/physiology, Synaptic Transmission/physiology, Visual Pathways/cytology/*physiology}, isbn = {1529-2401 (Electronic)0270-6474 (Linking)}, author = {Baccus, S. A. and {\"O}lveczky, B. P. and Manu, M. and Meister, M.} } @article {587116, title = {Retinal adaptation to object motion}, journal = {Neuron}, volume = {56}, number = {4}, year = {2007}, note = {

Olveczky, Bence PBaccus, Stephen AMeister, MarkusengR01 EY010020/EY/NEI NIH HHS/R01 EY010020-06/EY/NEI NIH HHS/R01 EY010020-07/EY/NEI NIH HHS/R01 EY010020-08A1/EY/NEI NIH HHS/R01 EY010020-09/EY/NEI NIH HHS/R01 EY010020-10/EY/NEI NIH HHS/R01 EY010020-11/EY/NEI NIH HHS/R01 EY014737/EY/NEI NIH HHS/R01 EY014737-01/EY/NEI NIH HHS/R01 EY014737-02/EY/NEI NIH HHS/R01 EY014737-03/EY/NEI NIH HHS/R01 EY014737-04/EY/NEI NIH HHS/R01 EY014737-05/EY/NEI NIH HHS/Research Support, N.I.H., ExtramuralResearch Support, Non-U.S. Gov{\textquoteright}t2007/11/23 09:00Neuron. 2007 Nov 21;56(4):689-700.

}, month = {Nov 21}, pages = {689-700}, abstract = {

Due to fixational eye movements, the image on the retina is always in motion, even when one views a stationary scene. When an object moves within the scene, the corresponding patch of retina experiences a different motion trajectory than the surrounding region. Certain retinal ganglion cells respond selectively to this condition, when the motion in the cell{\textquoteright}s receptive field center is different from that in the surround. Here we show that this response is strongest at the very onset of differential motion, followed by gradual adaptation with a time course of several seconds. Different subregions of a ganglion cell{\textquoteright}s receptive field can adapt independently. The circuitry responsible for differential motion adaptation lies in the inner retina. Several candidate mechanisms were tested, and the adaptation most likely results from synaptic depression at the synapse from bipolar to ganglion cell. Similar circuit mechanisms may act more generally to emphasize novel features of a visual stimulus.

Neuron. 56:698-700. Nov 2007 (pdf).

}, keywords = {Adaptation, Physiological/*physiology, Animals, Membrane Potentials/physiology, Motion Perception/*physiology, Neural Inhibition/physiology, Neural Pathways/cytology/*physiology, Organ Culture Techniques, Pattern Recognition, Visual/physiology, Photic Stimulation, Retina/cytology/*physiology, Retinal Bipolar Cells/physiology, Retinal Ganglion Cells/cytology/*physiology, Synapses/physiology, Synaptic Transmission/physiology, urodela, Visual Fields/*physiology}, isbn = {0896-6273 (Print)0896-6273 (Linking)}, author = {{\"O}lveczky, B. P. and Baccus, S. A. and Meister, M.} } @article {587106, title = {Vocal experimentation in the juvenile songbird requires a basal ganglia circuit}, journal = {PLoS Biol}, volume = {3}, number = {5}, year = {2005}, note = {

Olveczky, Bence PAndalman, Aaron SFee, Michale Seng2005/04/14 09:00PLoS Biol. 2005 May;3(5):e153. Epub 2005 Mar 29.

}, month = {May}, pages = {e153}, abstract = {

Songbirds learn their songs by trial-and-error experimentation, producing highly variable vocal output as juveniles. By comparing their own sounds to the song of a tutor, young songbirds gradually converge to a stable song that can be a remarkably good copy of the tutor song. Here we show that vocal variability in the learning songbird is induced by a basal-ganglia-related circuit, the output of which projects to the motor pathway via the lateral magnocellular nucleus of the nidopallium (LMAN). We found that pharmacological inactivation of LMAN dramatically reduced acoustic and sequence variability in the songs of juvenile zebra finches, doing so in a rapid and reversible manner. In addition, recordings from LMAN neurons projecting to the motor pathway revealed highly variable spiking activity across song renditions, showing that LMAN may act as a source of variability. Lastly, pharmacological blockade of synaptic inputs from LMAN to its target premotor area also reduced song variability. Our results establish that, in the juvenile songbird, the exploratory motor behavior required to learn a complex motor sequence is dependent on a dedicated neural circuit homologous to cortico-basal ganglia circuits in mammals.

PLoS Biol. 3(5): e153, May 2005\ (pdf).

}, keywords = {*Vocalization, Animal/drug effects, Acoustic Stimulation, Animals, Basal Ganglia/drug effects/*physiology, Finches/*physiology, Functional Laterality, Muscle, Skeletal/physiology, Stereotaxic Techniques, Tetrodotoxin/administration \& dosage/pharmacology}, isbn = {1545-7885 (Electronic)1544-9173 (Linking)}, author = {{\"O}lveczky, B. P. and Andalman, A. S. and Fee, M. S.} } @article {587161, title = {Genetically engineered mice with an additional class of cone photoreceptors: implications for the evolution of color vision}, journal = {Proc Natl Acad Sci U S A}, volume = {100}, number = {20}, year = {2003}, note = {

Smallwood, Philip MOlveczky, Bence PWilliams, Gary LJacobs, Gerald HReese, Benjamin EMeister, MarkusNathans, JeremyengEY002052/EY/NEI NIH HHS/Research Support, Non-U.S. Gov{\textquoteright}tResearch Support, U.S. Gov{\textquoteright}t, P.H.S.2003/09/23 05:00Proc Natl Acad Sci U S A. 2003 Sep 30;100(20):11706-11. Epub 2003 Sep 19.

}, month = {Sep 30}, pages = {11706-11}, abstract = {

Among eutherian mammals, only primates possess trichromatic color vision. In Old World primates, trichromacy was made possible by a visual pigment gene duplication. In most New World primates, trichromacy is based on polymorphic variation in a single X-linked gene that produces, by random X inactivation, a patchy mosaic of spectrally distinct cone photoreceptors in heterozygous females. In the present work, we have modeled the latter strategy in a nonprimate by replacing the X-linked mouse green pigment gene with one encoding the human red pigment. In the mouse retina, the human red pigment seems to function normally, and heterozygous female mice express the human red and mouse green pigments at levels that vary between animals. Multielectrode array recordings from heterozygous female retinas reveal significant variation in the chromatic sensitivities of retinal ganglion cells. The data are consistent with a model in which these retinal ganglion cells draw their inputs indiscriminately from a coarse-grained mosaic of red and green cones. These observations support the ideas that (i) chromatic signals could arise from stochastic variation in inputs drawn nonselectively from red and green cones and (ii) tissue mosaicism due to X chromosome inactivation could be one mechanism for driving the evolution of CNS diversity.

roc. Natl. Acad. Sci.\ 100(20): 11706-11711, Sept 2003. * Equal contribution\ (pdf).

}, keywords = {Action Potentials, Animals, Animals, Genetically Modified, Color Perception/genetics/*physiology, Humans, Mice, Photoreceptor Cells, Vertebrate/*physiology, Retinal Cone Photoreceptor Cells/*metabolism, Retinal Ganglion Cells/physiology}, isbn = {0027-8424 (Print)0027-8424 (Linking)}, author = {Smallwood, P. M. and {\"O}lveczky, B. P. and Williams, G. L. and Jacobs, G. H. and Reese, B. E. and Meister, M. and Nathans, J.} } @article {587111, title = {Segregation of object and background motion in the retina}, journal = {Nature}, volume = {423}, number = {6938}, year = {2003}, note = {

Olveczky, Bence PBaccus, Stephen AMeister, MarkusengResearch Support, U.S. Gov{\textquoteright}t, P.H.S.England2003/05/20 05:00Nature. 2003 May 22;423(6938):401-8. Epub 2003 May 11.

}, month = {May 22}, pages = {401-8}, abstract = {

An important task in vision is to detect objects moving within a stationary scene. During normal viewing this is complicated by the presence of eye movements that continually scan the image across the retina, even during fixation. To detect moving objects, the brain must distinguish local motion within the scene from the global retinal image drift due to fixational eye movements. We have found that this process begins in the retina: a subset of retinal ganglion cells responds to motion in the receptive field centre, but only if the wider surround moves with a different trajectory. This selectivity for differential motion is independent of direction, and can be explained by a model of retinal circuitry that invokes pooling over nonlinear interneurons. The suppression by global image motion is probably mediated by polyaxonal, wide-field amacrine cells with transient responses. We show how a population of ganglion cells selective for differential motion can rapidly flag moving objects, and even segregate multiple moving objects.

Nature\ 423 (6938): 401-8, 22 May 2003\ (PDF)(News and Views feat. Michael Jordan).

}, keywords = {*Motion, Action Potentials, Amacrine Cells/physiology, Animals, Dendrites/physiology, Eye Movements/physiology, Fixation, Ocular/physiology, Models, Neurological, Photic Stimulation, Rabbits, Retina/cytology/*physiology, Retinal Ganglion Cells/physiology, Urodela/*physiology}, isbn = {0028-0836 (Print)0028-0836 (Linking)}, author = {{\"O}lveczky, B. P. and Baccus, S. A. and Meister, M.} } @article {587076, title = {High microvascular endothelial water permeability in mouse lung measured by a pleural surface fluorescence method}, journal = {Biophys J}, volume = {74}, number = {4}, year = {1998}, note = {

Carter, E POlveczky, B PMatthay, M AVerkman, A SengDK35124/DK/NIDDK NIH HHS/DK43840/DK/NIDDK NIH HHS/HL51854/HL/NHLBI NIH HHS/etc.Research Support, Non-U.S. Gov{\textquoteright}tResearch Support, U.S. Gov{\textquoteright}t, P.H.S.1998/04/17Biophys J. 1998 Apr;74(4):2121-8.

}, month = {Apr}, pages = {2121-8}, abstract = {

Transport of water between the capillary and airspace compartments in lung encounters serial barriers: the alveolar epithelium, interstitium, and capillary endothelium. We previously reported a pleural surface fluorescence method to measure net capillary-to-airspace water transport. To measure the osmotic water permeability across the microvascular endothelial barrier in intact lung, the airspace was filled with a water-immiscible fluorocarbon. The capillaries were perfused via the pulmonary artery with solutions of specified osmolalites containing a high-molecular-weight fluorescent dextran. An increase in perfusate osmolality produced a prompt decrease in surface fluorescence due to dye dilution in the capillaries, followed by a slower return to initial fluorescence as capillary and lung interstitial osmolality equilibrate. A mathematical model was developed to determine the osmotic water permeability coefficient (Pf) of lung microvessels from the time course of pleural surface fluorescence. As predicted, the magnitude of the prompt change in surface fluorescence increased with decreased pulmonary artery perfusion rate and increased osmotic gradient size. With raffinose used to induce the osmotic gradient, Pf was 0.03 cm/s at 23 degrees C and was reduced 54\% by 0.5 mM HgCl2. Temperature dependence measurements gave an Arrhenius activation energy (Ea) of 5.4 kcal/mol (12-37 degrees C). The apparent Pf induced by the smaller osmolytes mannitol and glycine was 0.021 and 0.011 cm/s (23 degrees C). Immunoblot analysis showed approximately 1.4 x 10(12) aquaporin-1 water channels/cm2 of capillary surface, which accounted quantitatively for the high Pf. These results establish a novel method for measuring osmotically driven water permeability across microvessels in intact lung. The high Pf, low Ea, and mercurial inhibition indicate the involvement of molecular water channels in water transport across the lung endothelium.

}, keywords = {*Aquaporins, Animals, Aquaporin 1, Biological Transport, Active, Biophysical Phenomena, Biophysics, Body Water/*metabolism, Capillary Permeability, Endothelium, Vascular/*metabolism, In Vitro Techniques, Ion Channels/metabolism, Lung/*blood supply/*metabolism, Mice, Microscopy, Fluorescence, Models, Biological, Osmosis, Pleura/blood supply/metabolism}, isbn = {0006-3495 (Print)0006-3495 (Linking)}, author = {Carter, E. P. and {\"O}lveczky, B. P. and Matthay, M. A. and Verkman, A. S.} } @article {587136, title = {Monte Carlo analysis of obstructed diffusion in three dimensions: application to molecular diffusion in organelles}, journal = {Biophys J}, volume = {74}, number = {5}, year = {1998}, note = {

Olveczky, B PVerkman, A SengDK43840/DK/NIDDK NIH HHS/Research Support, U.S. Gov{\textquoteright}t, P.H.S.1998/05/20Biophys J. 1998 May;74(5):2722-30.

}, month = {May}, pages = {2722-30}, abstract = {

Molecular transport in the aqueous lumen of organelles involves diffusion in a confined compartment with complex geometry. Monte Carlo simulations of particle diffusion in three dimensions were carried out to evaluate the influence of organelle structure on diffusive transport and to relate experimental photobleaching data to intrinsic diffusion coefficients. Two organelle structures were modeled: a mitochondria-like long closed cylinder containing fixed luminal obstructions of variable number and size, and an endoplasmic reticulum-like network of interconnected cylinders of variable diameter and density. Trajectories were computed in each simulation for \>10(5) particles, generally for \>10(5) time steps. Computed time-dependent concentration profiles agreed quantitatively with analytical solutions of the diffusion equation for simple geometries. For mitochondria-like cylinders, significant slowing of diffusion required large or wide single obstacles, or multiple obstacles. In simulated spot photobleaching experiments, a approximately 25\% decrease in apparent diffusive transport rate (defined by the time to 75\% fluorescence recovery) was found for a single thin transverse obstacle occluding 93\% of lumen area, a single 53\%-occluding obstacle of width 16 lattice points (8\% of cylinder length), 10 equally spaced 53\% obstacles alternately occluding opposite halves of the cylinder lumen, or particle binding to walls (with mean residence time = 10 time steps). Recovery curve shape with obstacles showed long tails indicating anomalous diffusion. Simulations also demonstrated the utility of measurement of fluorescence depletion at a spot distant from the bleach zone. For a reticulum-like network, particle diffusive transport was mildly reduced from that in unobstructed three-dimensional space. In simulated photobleaching experiments, apparent diffusive transport was decreased by 39-60\% in reticular structures in which 90-97\% of space was occluded. These computations provide an approach to analyzing photobleaching data in terms of microscopic diffusive properties and support the paradigm that organellar barriers must be quite severe to seriously impede solute diffusion.

}, keywords = {*Models, Biological, Diffusion, Endoplasmic Reticulum/physiology, Fluorescence, Mitochondria/*physiology/ultrastructure, Models, Structural, Monte Carlo Method, Organelles/*physiology/ultrastructure, Signal Transduction}, isbn = {0006-3495 (Print)0006-3495 (Linking)}, author = {{\"O}lveczky, B. P. and Verkman, A. S.} } @article {587146, title = {Rapid diffusion of green fluorescent protein in the mitochondrial matrix}, journal = {J Cell Biol}, volume = {140}, number = {4}, year = {1998}, note = {

Partikian, AOlveczky, BSwaminathan, RLi, YVerkman, A SengDK35124/DK/NIDDK NIH HHS/DK43840/DK/NIDDK NIH HHS/HL42368/HL/NHLBI NIH HHS/etc.Research Support, Non-U.S. Gov{\textquoteright}tResearch Support, U.S. Gov{\textquoteright}t, P.H.S.1998/03/21J Cell Biol. 1998 Feb 23;140(4):821-9.

}, month = {Feb 23}, pages = {821-9}, abstract = {

It is thought that the high protein density in the mitochondrial matrix results in severely restricted solute diffusion and metabolite channeling from one enzyme to another without free aqueous-phase diffusion. To test this hypothesis, we measured the diffusion of green fluorescent protein (GFP) expressed in the mitochondrial matrix of fibroblast, liver, skeletal muscle, and epithelial cell lines. Spot photobleaching of GFP with a 100x objective (0.8-micron spot diam) gave half-times for fluorescence recovery of 15-19 ms with \>90\% of the GFP mobile. As predicted for aqueous-phase diffusion in a confined compartment, fluorescence recovery was slowed or abolished by increased laser spot size or bleach time, and by paraformaldehyde fixation. Quantitative analysis of bleach data using a mathematical model of matrix diffusion gave GFP diffusion coefficients of 2-3 x 10(-7) cm2/s, only three to fourfold less than that for GFP diffusion in water. In contrast, little recovery was found for bleaching of GFP in fusion with subunits of the fatty acid beta-oxidation multienzyme complex that are normally present in the matrix. Measurement of the rotation of unconjugated GFP by time-resolved anisotropy gave a rotational correlation time of 23.3 +/- 1 ns, similar to that of 20 ns for GFP rotation in water. A rapid rotational correlation time of 325 ps was also found for a small fluorescent probe (BCECF, approximately 0.5 kD) in the matrix of isolated liver mitochondria. The rapid and unrestricted diffusion of solutes in the mitochondrial matrix suggests that metabolite channeling may not be required to overcome diffusive barriers. We propose that the clustering of matrix enzymes in membrane-associated complexes might serve to establish a relatively uncrowded aqueous space in which solutes can freely diffuse.

}, keywords = {Animals, Anisotropy, Cell Line, CHO Cells, Cricetinae, Diffusion, Green Fluorescent Proteins, Humans, LLC-PK1 Cells, Luminescent Proteins/chemistry/genetics/*metabolism, Mitochondria/chemistry/*metabolism, Models, Biological, Photochemistry, Recombinant Fusion Proteins/chemistry/genetics/metabolism, Spectrometry, Fluorescence, Swine, Time Factors}, isbn = {0021-9525 (Print)0021-9525 (Linking)}, author = {Partikian, A. and {\"O}lveczky, B. and Swaminathan, R. and Li, Y. and Verkman, A. S.} } @article {587131, title = {Mapping fluorophore distributions in three dimensions by quantitative multiple angle-total internal reflection fluorescence microscopy}, journal = {Biophys J}, volume = {73}, number = {5}, year = {1997}, note = {

Olveczky, B PPeriasamy, NVerkman, A SengDK16095/DK/NIDDK NIH HHS/DK43840/DK/NIDDK NIH HHS/TW00704/TW/FIC NIH HHS/Research Support, U.S. Gov{\textquoteright}t, P.H.S.1997/11/25Biophys J. 1997 Nov;73(5):2836-47.

}, month = {Nov}, pages = {2836-47}, abstract = {

The decay of evanescent field intensity beyond a dielectric interface depends upon beam incident angle, enabling the 3-d distribution of fluorophores to be deduced from total internal reflection fluorescence microscopy (TIRFM) images obtained at multiple incident angles. Instrumentation was constructed for computer-automated multiple angle-TIRFM (MA-TIRFM) using a right angle F2 glass prism (n(r) 1.632) to create the dielectric interface. A laser beam (488 nm) was attenuated by an acoustooptic modulator and directed onto a specified spot on the prism surface. Beam incident angle was set using three microstepper motors controlling two rotatable mirrors and a rotatable optical flat. TIRFM images were acquired by a cooled CCD camera in approximately 0.5 degree steps for \>15 incident angles starting from the critical angle. For cell studies, cells were grown directly on the glass prisms (without refractive index-matching fluid) and positioned in the optical path. Images of the samples were acquired at multiple angles, and corrected for angle-dependent evanescent field intensity using "reference" images acquired with a fluorophore solution replacing the sample. A theory was developed to compute fluorophore z-distribution by inverse Laplace transform of angle-resolved intensity functions. The theory included analysis of multiple layers of different refractive index for cell studies, and the anisotropic emission from fluorophores near a dielectric interface. Instrument performance was validated by mapping the thickness of a film of dihexyloxacarbocyanine in DMSO/water (n(r) 1.463) between the F2 glass prism and a plano-convex silica lens (458 mm radius, n(r) 1.463); the MA-TIRFM map accurately reproduced the lens spherical surface. MA-TIRFM was used to compare with nanometer z-resolution the geometry of cell-substrate contact for BCECF-labeled 3T3 fibroblasts versus MDCK epithelial cells. These studies establish MA-TIRFM for measurement of submicroscopic distances between fluorescent probes and cell membranes.

}, keywords = {*Microscopy, Fluorescence/instrumentation/methods, 3T3 Cells, Animals, Anisotropy, Carbocyanines/analysis, Cell Line, Cell Membrane/chemistry/metabolism, Dogs, Fluoresceins/analysis, Fluorescent Dyes/*analysis, Kidney, Lasers, Mathematics, Mice, Refractometry}, isbn = {0006-3495 (Print)0006-3495 (Linking)}, author = {{\"O}lveczky, B. P. and Periasamy, N. and Verkman, A. S.} }