Honors & Awards
NSF Career Award, National Science Foundation (2019)
Investigator Award in Mathematical Modeling of Living Systems, Simons Foundation (2016)
McKnight Scholar Award, McKnight Endowment Fund for Neuroscience (2015)
Scholar Award in Human Cognition, James S. McDonnell Foundation (2014)
Outstanding Paper Award, Neural Information Processing Systems Foundation (2014)
Sloan Research Fellowship, Alfred P. Sloan Foundation (2013)
Terman Award, Stanford University (2012)
Career Award at the Scientific Interface, Burroughs Wellcome Foundation (2009)
Swartz Fellow in Computational Neuroscience, Swartz Foundation (2004)
Ph.D., UC Berkeley, Theoretical Physics (2004)
M.A., UC Berkeley, Physics (2000)
M.A., UC Berkeley, Mathematics (2004)
M.Eng., MIT, Electrical Engineering and Computer Science (1998)
B.S., MIT, Physics (1998)
B.S., MIT, Mathematics (1998)
B.S., MIT, Electrical Engineering and Computer Science (1998)
Current Research and Scholarly Interests
Theoretical / computational neuroscience
Independent Studies (7)
- Directed Investigation
BIOE 392 (Aut, Win, Spr, Sum)
- Directed Reading in Neurosciences
NEPR 299 (Aut, Win, Spr)
- Directed Studies in Applied Physics
APPPHYS 290 (Aut, Win, Spr, Sum)
- Graduate Research
NEPR 399 (Aut, Win, Spr, Sum)
- Independent Work
CS 199 (Aut)
- Practical Training
APPPHYS 291 (Sum)
PHYSICS 490 (Aut, Win)
- Directed Investigation
Prior Year Courses
- Artificial Intelligence, Entrepreneurship and Society in the 21st Century and Beyond
CS 28 (Aut)
- Introduction to Biophysics
APPPHYS 205, BIO 126, BIO 226 (Win)
- NeuroTech Training Seminar
NSUR 239, STATS 242 (Spr)
- Theoretical Neuroscience
APPPHYS 293, PSYCH 242 (Spr)
- Artificial Intelligence, Entrepreneurship and Society in the 21st Century and Beyond
Doctoral Dissertation Reader (AC)
Vasily Kruzhilin, Mark Plitt, Daniel Wennberg
Postdoctoral Faculty Sponsor
Sam Ocko, James Whittington
Doctoral Dissertation Advisor (AC)
Brandon Benson, Stanislav Fort, Sarah Harvey, Daniel Paul Kunin, Gabriel Mel, Aran Nayebi, Mansheej Paul
Doctoral Dissertation Co-Advisor (AC)
Feng Chen, Linnie Jiang, YoungJu Jo, Byungwoo Kang, Brett Larsen, Aiden Wang, Grace Woods
Master's Program Advisor
Tingting Gong, Zhaoheng Guo, Rochelle Radzyminski, Lauren Riddiford, Eun Sun Song, Aiden Wang, Jake Wisser
Postdoctoral Research Mentor
Graduate and Fellowship Programs
Distinct invivo dynamics of excitatory synapses onto cortical pyramidal neurons and parvalbumin-positive interneurons.
2021; 37 (6): 109972
Cortical function relies on the balanced activation of excitatory and inhibitory neurons. However, little is known about the organization and dynamics of shaft excitatory synapses onto cortical inhibitory interneurons. Here, we use the excitatory postsynaptic marker PSD-95, fluorescently labeled at endogenous levels, as a proxy for excitatory synapses onto layer 2/3 pyramidal neurons and parvalbumin-positive (PV+) interneurons in the barrel cortex of adult mice. Longitudinal invivo imaging under baseline conditions reveals that, although synaptic weights in both neuronal types are log-normally distributed, synapses onto PV+ neurons are less heterogeneous and more stable. Markov model analyses suggest that the synaptic weight distribution is set intrinsically by ongoing cell-type-specific dynamics, and substantial changes are due to accumulated gradual changes. Synaptic weight dynamics are multiplicative, i.e., changes scale with weights, although PV+ synapses also exhibit an additive component. These results reveal that cell-type-specific processes govern cortical synaptic strengths and dynamics.
View details for DOI 10.1016/j.celrep.2021.109972
View details for PubMedID 34758304
Embodied intelligence via learning and evolution.
2021; 12 (1): 5721
The intertwined processes of learning and evolution in complex environmental niches have resulted in a remarkable diversity of morphological forms. Moreover, many aspects of animal intelligence are deeply embodied in these evolved morphologies. However, the principles governing relations between environmental complexity, evolved morphology, and the learnability of intelligent control, remain elusive, because performing large-scale in silico experiments on evolution and learning is challenging. Here, we introduce Deep Evolutionary Reinforcement Learning (DERL): a computational framework which can evolve diverse agent morphologies to learn challenging locomotion and manipulation tasks in complex environments. Leveraging DERL we demonstrate several relations between environmental complexity, morphological intelligence and the learnability of control. First, environmental complexity fosters the evolution of morphological intelligence as quantified by the ability of a morphology to facilitate the learning of novel tasks. Second, we demonstrate a morphological Baldwin effect i.e., in our simulations evolution rapidly selects morphologies that learn faster, thereby enabling behaviors learned late in the lifetime of early ancestors to be expressed early in the descendants lifetime. Third, we suggest a mechanistic basis for the above relationships through the evolution of morphologies that are more physically stable and energy efficient, and can therefore facilitate learning and control.
View details for DOI 10.1038/s41467-021-25874-z
View details for PubMedID 34615862
A neural circuit state change underlying skilled movements.
In motor neuroscience, state changes are hypothesized to time-lock neural assemblies coordinating complex movements, but evidence for this remains slender. We tested whether a discrete change from more autonomous to coherent spiking underlies skilled movement by imaging cerebellar Purkinje neuron complex spikes in mice making targeted forelimb-reaches. As mice learned the task, millimeter-scale spatiotemporally coherent spiking emerged ipsilateral to the reaching forelimb, and consistent neural synchronization became predictive of kinematic stereotypy. Before reach onset, spiking switched from more disordered to internally time-locked concerted spiking and silence. Optogenetic manipulations of cerebellar feedback to the inferior olive bi-directionally modulated neural synchronization and reaching direction. A simple model explained the reorganization of spiking during reaching as reflecting a discrete bifurcation in olivary network dynamics. These findings argue that to prepare learned movements, olivo-cerebellar circuits enter a self-regulated, synchronized state promoting motor coordination. State changes facilitating behavioral transitions may generalize across neural systems.
View details for DOI 10.1016/j.cell.2021.06.001
View details for PubMedID 34214470
- Enhancing Associative Memory Recall and Storage Capacity Using Confocal Cavity QED PHYSICAL REVIEW X 2021; 11 (2)
Coupling of activity, metabolism and behaviour across the Drosophila brain.
Coordinated activity across networks of neurons is a hallmark of both resting and active behavioural states in many species1-5. These global patterns alter energy metabolism over seconds to hours, which underpins the widespread use of oxygen consumption and glucose uptake as proxies of neural activity6,7. However, whether changes in neural activity are causally related to metabolic flux in intact circuits on the timescales associated with behaviour is unclear. Here we combine two-photon microscopy of the fly brain with sensors that enable the simultaneous measurement of neural activity and metabolic flux, across both resting and active behavioural states. We demonstrate that neural activity drives changes in metabolic flux, creating a tight coupling between these signals that can be measured across brain networks. Using local optogenetic perturbation, we demonstrate that even transient increases in neural activity result in rapid and persistent increases in cytosolic ATP, which suggests that neuronal metabolism predictively allocates resources to anticipate the energy demands of future activity. Finally, our studies reveal that the initiation of even minimal behavioural movements causes large-scale changes in the pattern of neural activity and energy metabolism, which reveals a widespread engagement of the brain. As the relationship between neural activity and energy metabolism is probably evolutionarily ancient and highly conserved, our studies provide a critical foundation for using metabolic proxies to capture changes in neural activity.
View details for DOI 10.1038/s41586-021-03497-0
View details for PubMedID 33911283
Distance-tuned neurons drive specialized path integration calculations in medial entorhinal cortex.
2021; 36 (10): 109669
During navigation, animals estimate their position using path integration and landmarks, engaging many brain areas. Whether these areas follow specialized or universal cue integration principles remains incompletely understood. We combine electrophysiology with virtual reality to quantify cue integration across thousands of neurons in three navigation-relevant areas: primary visual cortex (V1), retrosplenial cortex (RSC), and medial entorhinal cortex (MEC). Compared with V1 and RSC, path integration influences position estimates more in MEC, and conflicts between path integration and landmarks trigger remapping more readily. Whereas MEC codes position prospectively, V1 codes position retrospectively, and RSC is intermediate between the two. Lowered visual contrast increases the influence of path integration on position estimates only in MEC. These properties are most pronounced in a population of MEC neurons, overlapping with grid cells, tuned to distance run in darkness. These results demonstrate the specialized role that path integration plays in MEC compared with other navigation-relevant cortical areas.
View details for DOI 10.1016/j.celrep.2021.109669
View details for PubMedID 34496249
- Discovering Precise Temporal Patterns in Large-Scale Neural Recordings through Robust and Interpretable Time Warping NEURON 2020; 105 (2): 246-+
- Statistical Mechanics of Deep Learning ANNUAL REVIEW OF CONDENSED MATTER PHYSICS, VOL 11, 2020 2020; 11: 501–28
Fundamental bounds on the fidelity of sensory cortical coding.
2020; 580 (7801): 100–105
How the brain processes information accurately despite stochastic neural activity is a longstanding question1. For instance, perception is fundamentally limited by the information that the brain can extract from the noisy dynamics of sensory neurons. Seminal experiments2,3 suggest that correlated noise in sensory cortical neural ensembles is what limits their coding accuracy4-6, although how correlated noise affects neural codes remains debated7-11. Recent theoretical work proposes that how a neural ensemble's sensory tuning properties relate statistically to its correlated noise patterns is a greater determinant of coding accuracy than is absolute noise strength12-14. However, without simultaneous recordings from thousands of cortical neurons with shared sensory inputs, it is unknown whether correlated noise limits coding fidelity. Here we present a 16-beam, two-photon microscope to monitor activity across the mouse primary visual cortex, along with analyses to quantify the information conveyed by large neural ensembles. We found that, in the visual cortex, correlated noise constrained signalling for ensembles with 800-1,300 neurons. Several noise components of the ensemble dynamics grew proportionally to the ensemble size and the encoded visual signals, revealing the predicted information-limiting correlations12-14. Notably, visual signals were perpendicular to the largest noise mode, which therefore did not limit coding fidelity. The information-limiting noise modes were approximately ten times smaller and concordant with mouse visual acuity15. Therefore, cortical design principles appear to enhance coding accuracy by restricting around 90% of noise fluctuations to modes that do not limit signalling fidelity, whereas much weaker correlated noise modes inherently bound sensory discrimination.
View details for DOI 10.1038/s41586-020-2130-2
View details for PubMedID 32238928
GluD2- and Cbln1-mediated competitive interactions shape the dendritic arbors of cerebellar Purkinje cells.
The synaptotrophic hypothesis posits that synapse formation stabilizes dendritic branches, but this hypothesis has not been causally tested in vivo in the mammalian brain. The presynaptic ligand cerebellin-1 (Cbln1) and postsynaptic receptor GluD2 mediate synaptogenesis between granule cells and Purkinje cells in the molecular layer of the cerebellar cortex. Here we show that sparse but not global knockout of GluD2 causes under-elaboration of Purkinje cell dendrites in the deep molecular layer and overelaboration in the superficial molecular layer. Developmental, overexpression, structure-function, and genetic epistasis analyses indicate that these dendrite morphogenesis defects result from a deficit in Cbln1/GluD2-dependent competitive interactions. A generative model of dendrite growth based on competitive synaptogenesis largely recapitulates GluD2 sparse and global knockout phenotypes. Our results support the synaptotrophic hypothesis at initial stages of dendrite development, suggest a second mode in which cumulative synapse formation inhibits further dendrite growth, and highlight the importance of competition in dendrite morphogenesis.
View details for DOI 10.1016/j.neuron.2020.11.028
View details for PubMedID 33352118
- Statistical mechanics of low-rank tensor decomposition JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT 2019; 2019 (12)
A deep learning framework for neuroscience.
2019; 22 (11): 1761–70
Systems neuroscience seeks explanations for how the brain implements a wide variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to design computational systems based on the tasks they will have to solve. In artificial neural networks, the three components specified by design are the objective functions, the learning rules and the architectures. With the growing success of deep learning, which utilizes brain-inspired architectures, these three designed components have increasingly become central to how we model, engineer and optimize complex artificial learning systems. Here we argue that a greater focus on these components would also benefit systems neuroscience. We give examples of how this optimization-based framework can drive theoretical and experimental progress in neuroscience. We contend that this principled perspective on systems neuroscience will help to generate more rapid progress.
View details for DOI 10.1038/s41593-019-0520-2
View details for PubMedID 31659335
A mathematical theory of semantic development in deep neural networks.
Proceedings of the National Academy of Sciences of the United States of America
An extensive body of empirical research has revealed remarkable regularities in the acquisition, organization, deployment, and neural representation of human semantic knowledge, thereby raising a fundamental conceptual question: What are the theoretical principles governing the ability of neural networks to acquire, organize, and deploy abstract knowledge by integrating across many individual experiences? We address this question by mathematically analyzing the nonlinear dynamics of learning in deep linear networks. We find exact solutions to this learning dynamics that yield a conceptual explanation for the prevalence of many disparate phenomena in semantic cognition, including the hierarchical differentiation of concepts through rapid developmental transitions, the ubiquity of semantic illusions between such transitions, the emergence of item typicality and category coherence as factors controlling the speed of semantic processing, changing patterns of inductive projection over development, and the conservation of semantic similarity in neural representations across species. Thus, surprisingly, our simple neural model qualitatively recapitulates many diverse regularities underlying semantic development, while providing analytic insight into how the statistical structure of an environment can interact with nonlinear deep-learning dynamics to give rise to these regularities.
View details for DOI 10.1073/pnas.1820226116
View details for PubMedID 31101713
- Shared Cortex-Cerebellum Dynamics in the Execution and Learning of a Motor Task CELL 2019; 177 (3): 669-+
Cortical layer-specific critical dynamics triggering perception.
Science (New York, N.Y.)
Perceptual experiences may arise from neuronal activity patterns in mammalian neocortex. We probed mouse neocortex during visual discrimination using a red-shifted channelrhodopsin (ChRmine, discovered through structure-guided genome mining) alongside multiplexed multiphoton-holography (MultiSLM), achieving control of individually-specified neurons spanning large cortical volumes with millisecond precision. Stimulating a critical number of stimulus-orientation-selective neurons drove widespread recruitment of functionally-related neurons, a process enhanced by (but not requiring) orientation-discrimination task learning. Optogenetic targeting of orientation-selective ensembles elicited correct behavioral discrimination. Cortical layer specific-dynamics were apparent, as emergent neuronal activity asymmetrically propagated from layer-2/3 to layer-5, and smaller layer-5 ensembles were as effective as larger layer-2/3 ensembles in eliciting orientation discrimination behavior. Population dynamics emerging after optogenetic stimulation both correctly predicted behavior and resembled natural neural representations of visual stimuli.
View details for DOI 10.1126/science.aaw5202
View details for PubMedID 31320556
A unified theory for the origin of grid cells through the lens of pattern formation
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019
View details for Web of Science ID 000535866901061
Universality and individuality in neural dynamics across large populations of recurrent networks
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019
View details for Web of Science ID 000535866907030
Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019
View details for Web of Science ID 000535866907036
From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019
View details for Web of Science ID 000535866900016
- A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs International Conference on Learning Representations (ICLR) 2019
- An analytic theory of generalization dynamics and transfer learning in deep linear networks International Conference on Learning Representations (ICLR) 2019
- Accurate estimation of neural population dynamics without spike sorting Neuron 2019; 103: 1-17
Emergent elasticity in the neural code for space.
Proceedings of the National Academy of Sciences of the United States of America
Upon encountering a novel environment, an animal must construct a consistent environmental map, as well as an internal estimate of its position within that map, by combining information from two distinct sources: self-motion cues and sensory landmark cues. How do known aspects of neural circuit dynamics and synaptic plasticity conspire to accomplish this feat? Here we show analytically how a neural attractor model that combines path integration of self-motion cues with Hebbian plasticity in synaptic weights from landmark cells can self-organize a consistent map of space as the animal explores an environment. Intriguingly, the emergence of this map can be understood as an elastic relaxation process between landmark cells mediated by the attractor network. Moreover, our model makes several experimentally testable predictions, including (i) systematic path-dependent shifts in the firing fields of grid cells toward the most recently encountered landmark, even in a fully learned environment; (ii) systematic deformations in the firing fields of grid cells in irregular environments, akin to elastic deformations of solids forced into irregular containers; and (iii) the creation of topological defects in grid cell firing patterns through specific environmental manipulations. Taken together, our results conceptually link known aspects of neurons and synapses to an emergent solution of a fundamental computational problem in navigation, while providing a unified account of disparate experimental observations.
View details for PubMedID 30482856
Inferring hidden structure in multilayered neural circuits.
PLoS computational biology
2018; 14 (8): e1006291
A central challenge in sensory neuroscience involves understanding how neural circuits shape computations across cascaded cell layers. Here we attempt to reconstruct the response properties of experimentally unobserved neurons in the interior of a multilayered neural circuit, using cascaded linear-nonlinear (LN-LN) models. We combine non-smooth regularization with proximal consensus algorithms to overcome difficulties in fitting such models that arise from the high dimensionality of their parameter space. We apply this framework to retinal ganglion cell processing, learning LN-LN models of retinal circuitry consisting of thousands of parameters, using 40 minutes of responses to white noise. Our models demonstrate a 53% improvement in predicting ganglion cell spikes over classical linear-nonlinear (LN) models. Internal nonlinear subunits of the model match properties of retinal bipolar cells in both receptive field structure and number. Subunits have consistently high thresholds, supressing all but a small fraction of inputs, leading to sparse activity patterns in which only one subunit drives ganglion cell spiking at any time. From the model's parameters, we predict that the removal of visual redundancies through stimulus decorrelation across space, a central tenet of efficient coding theory, originates primarily from bipolar cell synapses. Furthermore, the composite nonlinear computation performed by retinal circuitry corresponds to a boolean OR function applied to bipolar cell feature detectors. Our methods are statistically and computationally efficient, enabling us to rapidly learn hierarchical non-linear models as well as efficiently compute widely used descriptive statistics such as the spike triggered average (STA) and covariance (STC) for high dimensional stimuli. This general computational framework may aid in extracting principles of nonlinear hierarchical sensory processing across diverse modalities from limited data.
View details for PubMedID 30138312
Principles governing the integration of landmark and self-motion cues in entorhinal cortical codes for navigation.
To guide navigation, the nervous system integrates multisensory self-motion and landmark information. We dissected how these inputs generate spatial representations by recording entorhinal grid, border and speed cells in mice navigating virtual environments. Manipulating the gain between the animal's locomotion and the visual scene revealed that border cells responded to landmark cues while grid and speed cells responded to combinations of locomotion, optic flow and landmark cues in a context-dependent manner, with optic flow becoming more influential when it was faster than expected. A network model explained these results by revealing a phase transition between two regimes in which grid cells remain coherent with or break away from the landmark reference frame. Moreover, during path-integration-based navigation, mice estimated their position following principles predicted by our recordings. Together, these results provide a theoretical framework for understanding how landmark and self-motion cues combine during navigation to generate spatial representations and guide behavior.
View details for PubMedID 30038279
SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks.
A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in silico. Here we revisit the problem of supervised learning in temporally coding multilayer spiking neural networks. First, by using a surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based three-factor learning rule capable of training multilayer networks of deterministic integrate-and-fire neurons to perform nonlinear computations on spatiotemporal spike patterns. Second, inspired by recent results on feedback alignment, we compare the performance of our learning rule under different credit assignment strategies for propagating output errors to hidden units. Specifically, we test uniform, symmetric, and random feedback, finding that simpler tasks can be solved with any type of feedback, while more complex tasks require symmetric feedback. In summary, our results open the door to obtaining a better scientific understanding of learning and computation in spiking neural networks by advancing our ability to train them to solve nonlinear problems involving transformations between different spatiotemporal spike time patterns.
View details for PubMedID 29652587
Task-Driven Convolutional Recurrent Models of the Visual System
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018
View details for Web of Science ID 000461823305032
The emergence of multiple retinal cell types through efficient coding of natural movies
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018
View details for Web of Science ID 000461852003090
Statistical mechanics of low-rank tensor decomposition
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018
View details for Web of Science ID 000461852002072
- The emergence of multiple retinal cell types through efficient coding of natural movies Neural Information Processing Systems (NIPS) 2018
- Task-Driven Convolutional Recurrent Models of the Visual System Neural Information Processing Systems (NIPS) 2018
- Statistical mechanics of low-rank tensor decomposition Neural Information Processing Systems (NIPS) 2018
Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis.
Perceptions, thoughts, and actions unfold over millisecond timescales, while learned behaviors can require many days to mature. While recent experimental advances enable large-scale and long-term neural recordings with high temporal fidelity, it remains a formidable challenge to extract unbiased and interpretable descriptions of how rapid single-trial circuit dynamics change slowly over many trials to mediate learning. We demonstrate a simple tensor component analysis (TCA) can meet this challenge by extracting three interconnected, low-dimensional descriptions of neural data: neuron factors, reflecting cell assemblies; temporal factors, reflecting rapid circuit dynamics mediating perceptions, thoughts, and actions within each trial; and trial factors, describing both long-term learning and trial-to-trial changes in cognitive state. We demonstrate the broad applicability of TCA by revealing insights into diverse datasets derived from artificial neural networks, large-scale calcium imaging of rodent prefrontal cortex during maze navigation, and multielectrode recordings of macaque motor cortex during brain machine interface learning.
View details for PubMedID 29887338
- The emergence of spectral universality in deep networks Artificial Intelligence and Statistics (AISTATS) 2018
An International Laboratory for Systems and Computational Neuroscience
2017; 96 (6): 1213–18
The neural basis of decision-making has been elusive and involves the coordinated activity of multiple brain structures. This NeuroView, by the International Brain Laboratory (IBL), discusses their efforts to develop a standardized mouse decision-making behavior, to make coordinated measurements of neural activity across the mouse brain, and to use theory and analyses to uncover the neural computations that support decision-making.
View details for DOI 10.1016/j.neuron.2017.12.013
View details for Web of Science ID 000418900200005
View details for PubMedID 29268092
View details for PubMedCentralID PMC5752703
Cell types for our sense of location: where we are and where we are going
2017; 20 (11): 1474–82
Technological advances in profiling cells along genetic, anatomical and physiological axes have fomented interest in identifying all neuronal cell types. This goal nears completion in specialized circuits such as the retina, while remaining more elusive in higher order cortical regions. We propose that this differential success of cell type identification may not simply reflect technological gaps in co-registering genetic, anatomical and physiological features in the cortex. Rather, we hypothesize it reflects evolutionarily driven differences in the computational principles governing specialized circuits versus more general-purpose learning machines. In this framework, we consider the question of cell types in medial entorhinal cortex (MEC), a region likely to be involved in memory and navigation. While MEC contains subsets of identifiable functionally defined cell types, recent work employing unbiased statistical methods and more diverse tasks reveals unsuspected heterogeneity and adaptivity in MEC firing patterns. This suggests MEC may operate more as a generalist circuit, obeying computational design principles resembling those governing other higher cortical regions.
View details for PubMedID 29073649
A Multiplexed, Heterogeneous, and Adaptive Code for Navigation in Medial Entorhinal Cortex
2017; 94 (2): 375-?
Medial entorhinal grid cells display strikingly symmetric spatial firing patterns. The clarity of these patterns motivated the use of specific activity pattern shapes to classify entorhinal cell types. While this approach successfully revealed cells that encode boundaries, head direction, and running speed, it left a majority of cells unclassified, and its pre-defined nature may have missed unconventional, yet important coding properties. Here, we apply an unbiased statistical approach to search for cells that encode navigationally relevant variables. This approach successfully classifies the majority of entorhinal cells and reveals unsuspected entorhinal coding principles. First, we find a high degree of mixed selectivity and heterogeneity in superficial entorhinal neurons. Second, we discover a dynamic and remarkably adaptive code for space that enables entorhinal cells to rapidly encode navigational information accurately at high running speeds. Combined, these observations advance our current understanding of the mechanistic origins and functional implications of the entorhinal code for navigation. VIDEO ABSTRACT.
View details for DOI 10.1016/j.neuron.2017.03.025
View details for Web of Science ID 000399451400020
View details for PubMedID 28392071
The temporal paradox of Hebbian learning and homeostatic plasticity.
Current opinion in neurobiology
2017; 43: 166-176
Hebbian plasticity, a synaptic mechanism which detects and amplifies co-activity between neurons, is considered a key ingredient underlying learning and memory in the brain. However, Hebbian plasticity alone is unstable, leading to runaway neuronal activity, and therefore requires stabilization by additional compensatory processes. Traditionally, a diversity of homeostatic plasticity phenomena found in neural circuits is thought to play this role. However, recent modelling work suggests that the slow evolution of homeostatic plasticity, as observed in experiments, is insufficient to prevent instabilities originating from Hebbian plasticity. To remedy this situation, we suggest that homeostatic plasticity is complemented by additional rapid compensatory processes, which rapidly stabilize neuronal activity on short timescales.
View details for DOI 10.1016/j.conb.2017.03.015
View details for PubMedID 28431369
A saturation hypothesis to explain both enhanced and impaired learning with enhanced plasticity.
Across many studies, animals with enhanced synaptic plasticity exhibit either enhanced or impaired learning, raising a conceptual puzzle: how enhanced plasticity can yield opposite learning outcomes? Here we show that recent history of experience can determine whether mice with enhanced plasticity exhibit enhanced or impaired learning in response to the same training. Mice with enhanced cerebellar LTD, due to double knockout (DKO) of MHCI H2-K(b)/H2-D(b) (K(b)D(b-/-)), exhibited oculomotor learning deficits. However, the same mice exhibited enhanced learning after appropriate pre-training. Theoretical analysis revealed that synapses with history-dependent learning rules could recapitulate the data, and suggested that saturation may be a key factor limiting the ability of enhanced plasticity to enhance learning. Moreover, optogenetic stimulation designed to saturate LTD produced the same impairment in WT as observed in DKO mice. Overall, our results suggest that recent history of activity and the threshold for synaptic plasticity conspire to effect divergent learning outcomes.
View details for DOI 10.7554/eLife.20147
View details for PubMedID 28234229
Social Control of Hypothalamus-Mediated Male Aggression.
2017; 95 (4): 955–70.e4
How environmental and physiological signals interact to influence neural circuits underlying developmentally programmed social interactions such as male territorial aggression is poorly understood. We have tested the influence of sensory cues, social context, and sex hormones on progesterone receptor (PR)-expressing neurons in the ventromedial hypothalamus (VMH) that are critical for male territorial aggression. We find that these neurons can drive aggressive displays in solitary males independent of pheromonal input, gonadal hormones, opponents, or social context. By contrast, these neurons cannot elicit aggression in socially housed males that intrude in another male's territory unless their pheromone-sensing is disabled. This modulation of aggression cannot be accounted for by linear integration of environmental and physiological signals. Together, our studies suggest that fundamentally non-linear computations enable social context to exert a dominant influence on developmentally hard-wired hypothalamus-mediated male territorial aggression.
View details for PubMedID 28757304
View details for PubMedCentralID PMC5648542
- On the expressive power of deep neural networks International Conference on Machine Learning (ICML) 2017
- Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice Neural Information Processing Systems (NIPS) 2017
- Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net Neural Information Processing Systems (NIPS) 2017
- Continual Learning with Intelligent Synapses International Conference on Machine Learning (ICML) 2017
- Deep information propagation International Conference on Learning Representations (ICLR) 2017
- Statistical Mechanics of Optimal Convex Inference in High Dimensions PHYSICAL REVIEW X 2016; 6 (3)
Direction Selectivity in Drosophila Emerges from Preferred-Direction Enhancement and Null-Direction Suppression.
journal of neuroscience
2016; 36 (31): 8078-8092
Across animal phyla, motion vision relies on neurons that respond preferentially to stimuli moving in one, preferred direction over the opposite, null direction. In the elementary motion detector of Drosophila, direction selectivity emerges in two neuron types, T4 and T5, but the computational algorithm underlying this selectivity remains unknown. We find that the receptive fields of both T4 and T5 exhibit spatiotemporally offset light-preferring and dark-preferring subfields, each obliquely oriented in spacetime. In a linear-nonlinear modeling framework, the spatiotemporal organization of the T5 receptive field predicts the activity of T5 in response to motion stimuli. These findings demonstrate that direction selectivity emerges from the enhancement of responses to motion in the preferred direction, as well as the suppression of responses to motion in the null direction. Thus, remarkably, T5 incorporates the essential algorithmic strategies used by the Hassenstein-Reichardt correlator and the Barlow-Levick detector. Our model for T5 also provides an algorithmic explanation for the selectivity of T5 for moving dark edges: our model captures all two- and three-point spacetime correlations relevant to motion in this stimulus class. More broadly, our findings reveal the contribution of input pathway visual processing, specifically center-surround, temporally biphasic receptive fields, to the generation of direction selectivity in T5. As the spatiotemporal receptive field of T5 in Drosophila is common to the simple cell in vertebrate visual cortex, our stimulus-response model of T5 will inform efforts in an experimentally tractable context to identify more detailed, mechanistic models of a prevalent computation.Feature selective neurons respond preferentially to astonishingly specific stimuli, providing the neurobiological basis for perception. Direction selectivity serves as a paradigmatic model of feature selectivity that has been examined in many species. While insect elementary motion detectors have served as premiere experimental models of direction selectivity for 60 years, the central question of their underlying algorithm remains unanswered. Using in vivo two-photon imaging of intracellular calcium signals, we measure the receptive fields of the first direction-selective cells in the Drosophila visual system, and define the algorithm used to compute the direction of motion. Computational modeling of these receptive fields predicts responses to motion and reveals how this circuit efficiently captures many useful correlations intrinsic to moving dark edges.
View details for DOI 10.1523/JNEUROSCI.1272-16.2016
View details for PubMedID 27488629
View details for PubMedCentralID PMC4971360
- An equivalence between high dimensional Bayes optimal inference and M-estimation Neural Information Processing Systems (NIPS) 2016
Deep Learning Models of the Retinal Response to Natural Scenes.
Advances in neural information processing systems
2016; 29: 1369–77
A central challenge in sensory neuroscience is to understand neural computations and circuit mechanisms that underlie the encoding of ethologically relevant, natural stimuli. In multilayered neural circuits, nonlinear processes such as synaptic transmission and spiking dynamics present a significant obstacle to the creation of accurate computational models of responses to natural stimuli. Here we demonstrate that deep convolutional neural networks (CNNs) capture retinal responses to natural scenes nearly to within the variability of a cell's response, and are markedly more accurate than linear-nonlinear (LN) models and Generalized Linear Models (GLMs). Moreover, we find two additional surprising properties of CNNs: they are less susceptible to overfitting than their LN counterparts when trained on small amounts of data, and generalize better when tested on stimuli drawn from a different distribution (e.g. between natural scenes and white noise). An examination of the learned CNNs reveals several properties. First, a richer set of feature maps is necessary for predicting the responses to natural scenes compared to white noise. Second, temporally precise responses to slowly varying inputs originate from feedforward inhibition, similar to known retinal mechanisms. Third, the injection of latent noise sources in intermediate layers enables our model to capture the sub-Poisson spiking variability observed in retinal ganglion cells. Fourth, augmenting our CNNs with recurrent lateral connections enables them to capture contrast adaptation as an emergent property of accurately describing retinal responses to natural scenes. These methods can be readily generalized to other sensory modalities and stimulus ensembles. Overall, this work demonstrates that CNNs not only accurately capture sensory circuit responses to natural scenes, but also can yield information about the circuit's internal structure and function.
View details for PubMedID 28729779
- Exponential expressivity in deep neural networks through transient chaos Neural Information Processing Systems (NIPS) 2016: 3360–3368
- Role of the site of synaptic competition and the balance of learning forces for Hebbian encoding of probabilistic Markov sequences FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2015; 9
- On simplicity and complexity in the brave new world of large-scale neuroscience CURRENT OPINION IN NEUROBIOLOGY 2015; 32: 148-155
Environmental Boundaries as an Error Correction Mechanism for Grid Cells
2015; 86 (3): 827-839
Medial entorhinal grid cells fire in periodic, hexagonally patterned locations and are proposed to support path-integration-based navigation. The recursive nature of path integration results in accumulating error and, without a corrective mechanism, a breakdown in the calculation of location. The observed long-term stability of grid patterns necessitates that the system either performs highly precise internal path integration or implements an external landmark-based error correction mechanism. To distinguish these possibilities, we examined grid cells in behaving rodents as they made long trajectories across an open arena. We found that error accumulates relative to time and distance traveled since the animal last encountered a boundary. This error reflects coherent drift in the grid pattern. Further, interactions with boundaries yield direction-dependent error correction, suggesting that border cells serve as a neural substrate for error correction. These observations, combined with simulations of an attractor network grid cell model, demonstrate that landmarks are crucial to grid stability.
View details for DOI 10.1016/j.neuron.2015.03.039
View details for Web of Science ID 000354069800021
View details for PubMedID 25892299
Evidence for a causal inverse model in an avian cortico-basal ganglia circuit
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
2014; 111 (16): 6063-6068
Learning by imitation is fundamental to both communication and social behavior and requires the conversion of complex, nonlinear sensory codes for perception into similarly complex motor codes for generating action. To understand the neural substrates underlying this conversion, we study sensorimotor transformations in songbird cortical output neurons of a basal-ganglia pathway involved in song learning. Despite the complexity of sensory and motor codes, we find a simple, temporally specific, causal correspondence between them. Sensory neural responses to song playback mirror motor-related activity recorded during singing, with a temporal offset of roughly 40 ms, in agreement with short feedback loop delays estimated using electrical and auditory stimulation. Such matching of mirroring offsets and loop delays is consistent with a recent Hebbian theory of motor learning and suggests that cortico-basal ganglia pathways could support motor control via causal inverse models that can invert the rich correspondence between motor exploration and sensory feedback.
View details for DOI 10.1073/pnas.1317087111
View details for Web of Science ID 000334694000074
View details for PubMedID 24711417
- Fast large scale optimization by unifying stochastic gradient and quasi-Newton methods International Conference on Machine Learning (ICML) 2014
- Exact solutions to the nonlinear dynamics of learning in deep neural networks International Conference on Learning Representations (ICLR) 2014
- Identifying and attacking the saddle point problem in high-dimensional non-convex optimization Neural Information Processing Systems (NIPS) 2014
Investigating the role of firing-rate normalization and dimensionality reduction in brain-machine interface robustness.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
2013; 2013: 293-298
The intraday robustness of brain-machine interfaces (BMIs) is important to their clinical viability. In particular, BMIs must be robust to intraday perturbations in neuron firing rates, which may arise from several factors including recording loss and external noise. Using a state-of-the-art decode algorithm, the Recalibrated Feedback Intention Trained Kalman filter (ReFIT-KF)  we introduce two novel modifications: (1) a normalization of the firing rates, and (2) a reduction of the dimensionality of the data via principal component analysis (PCA). We demonstrate in online studies that a ReFIT-KF equipped with normalization and PCA (NPC-ReFIT-KF) (1) achieves comparable performance to a standard ReFIT-KF when at least 60% of the neural variance is captured, and (2) is more robust to the undetected loss of channels. We present intuition as to how both modifications may increase the robustness of BMIs, and investigate the contribution of each modification to robustness. These advances, which lead to a decoder achieving state-of-the-art performance with improved robustness, are important for the clinical viability of BMI systems.
View details for DOI 10.1109/EMBC.2013.6609495
View details for PubMedID 24109682
A Hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models
FRONTIERS IN NEURAL CIRCUITS
Mirror neurons are neurons whose responses to the observation of a motor act resemble responses measured during production of that act. Computationally, mirror neurons have been viewed as evidence for the existence of internal inverse models. Such models, rooted within control theory, map-desired sensory targets onto the motor commands required to generate those targets. To jointly explore both the formation of mirrored responses and their functional contribution to inverse models, we develop a correlation-based theory of interactions between a sensory and a motor area. We show that a simple eligibility-weighted Hebbian learning rule, operating within a sensorimotor loop during motor explorations and stabilized by heterosynaptic competition, naturally gives rise to mirror neurons as well as control theoretic inverse models encoded in the synaptic weights from sensory to motor neurons. Crucially, we find that the correlational structure or stereotypy of the neural code underlying motor explorations determines the nature of the learned inverse model: random motor codes lead to causal inverses that map sensory activity patterns to their motor causes; such inverses are maximally useful, by allowing the imitation of arbitrary sensory target sequences. By contrast, stereotyped motor codes lead to less useful predictive inverses that map sensory activity to future motor actions. Our theory generalizes previous work on inverse models by showing that such models can be learned in a simple Hebbian framework without the need for error signals or backpropagation, and it makes new conceptual connections between the causal nature of inverse models, the statistical structure of motor variability, and the time-lag between sensory and motor responses of mirror neurons. Applied to bird song learning, our theory can account for puzzling aspects of the song system, including necessity of sensorimotor gating and selectivity of auditory responses to bird's own song (BOS) stimuli.
View details for DOI 10.3389/fncir.2013.00106
View details for Web of Science ID 000320922000001
View details for PubMedID 23801941
- Statistical mechanics of complex neural systems and high dimensional data JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT 2013
- A memory frontier for complex synapses Neural Information Processing Systems (NIPS) 2013
- Learning hierarchical category structure in deep neural networks Proceedings of the Cognitive Science Society 2013: 1271–1276
- Vocal learning with inverse models Principles of Neural Coding CRC Press. 2013
Spatial Information Outflow from the Hippocampal Circuit: Distributed Spatial Coding and Phase Precession in the Subiculum
JOURNAL OF NEUROSCIENCE
2012; 32 (34): 11539-11558
Hippocampal place cells convey spatial information through a combination of spatially selective firing and theta phase precession. The way in which this information influences regions like the subiculum that receive input from the hippocampus remains unclear. The subiculum receives direct inputs from area CA1 of the hippocampus and sends divergent output projections to many other parts of the brain, so we examined the firing patterns of rat subicular neurons. We found a substantial transformation in the subicular code for space from sparse to dense firing rate representations along a proximal-distal anatomical gradient: neurons in the proximal subiculum are more similar to canonical, sparsely firing hippocampal place cells, whereas neurons in the distal subiculum have higher firing rates and more distributed spatial firing patterns. Using information theory, we found that the more distributed spatial representation in the subiculum carries, on average, more information about spatial location and context than the sparse spatial representation in CA1. Remarkably, despite the disparate firing rate properties of subicular neurons, we found that neurons at all proximal-distal locations exhibit robust theta phase precession, with similar spiking oscillation frequencies as neurons in area CA1. Our findings suggest that the subiculum is specialized to compress sparse hippocampal spatial codes into highly informative distributed codes suitable for efficient communication to other brain regions. Moreover, despite this substantial compression, the subiculum maintains finer scale temporal properties that may allow it to participate in oscillatory phase coding and spike timing-dependent plasticity in coordination with other regions of the hippocampal circuit.
View details for DOI 10.1523/JNEUROSCI.5942-11.2012
View details for Web of Science ID 000308140500004
View details for PubMedID 22915100
Compressed Sensing, Sparsity, and Dimensionality in Neuronal Information Processing and Data Analysis
ANNUAL REVIEW OF NEUROSCIENCE, VOL 35
2012; 35: 485-508
The curse of dimensionality poses severe challenges to both technical and conceptual progress in neuroscience. In particular, it plagues our ability to acquire, process, and model high-dimensional data sets. Moreover, neural systems must cope with the challenge of processing data in high dimensions to learn and operate successfully within a complex world. We review recent mathematical advances that provide ways to combat dimensionality in specific situations. These advances shed light on two dual questions in neuroscience. First, how can we as neuroscientists rapidly acquire high-dimensional data from the brain and subsequently extract meaningful models from limited amounts of these data? And second, how do brains themselves process information in their intrinsically high-dimensional patterns of neural activity as well as learn meaningful, generalizable models of the external world from limited experience?
View details for DOI 10.1146/annurev-neuro-062111-150410
View details for Web of Science ID 000307960400024
View details for PubMedID 22483042
- Short-term memory in neuronal networks through dynamical compressed sensing Neural Information Processing Systems (NIPS) 2010
Feedforward to the Past: The Relation between Neuronal Connectivity, Amplification, and Short-Term Memory
2009; 61 (4): 499-501
Two studies in this issue of Neuron challenge widely held assumptions about the role of positive feedback in recurrent neuronal networks. Goldman shows that such feedback is not necessary for memory maintenance in a neural integrator, and Murphy and Miller show that it is not necessary for amplification of orientation patterns in V1. Both suggest that seemingly recurrent networks can be feedforward in disguise.
View details for DOI 10.1016/j.neuron.2009.02.006
View details for Web of Science ID 000263816300004
View details for PubMedID 19249270
Memory traces in dynamical systems
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
2008; 105 (48): 18970-18975
To perform nontrivial, real-time computations on a sensory input stream, biological systems must retain a short-term memory trace of their recent inputs. It has been proposed that generic high-dimensional dynamical systems could retain a memory trace for past inputs in their current state. This raises important questions about the fundamental limits of such memory traces and the properties required of dynamical systems to achieve these limits. We address these issues by applying Fisher information theory to dynamical systems driven by time-dependent signals corrupted by noise. We introduce the Fisher Memory Curve (FMC) as a measure of the signal-to-noise ratio (SNR) embedded in the dynamical state relative to the input SNR. The integrated FMC indicates the total memory capacity. We apply this theory to linear neuronal networks and show that the capacity of networks with normal connectivity matrices is exactly 1 and that of any network of N neurons is, at most, N. A nonnormal network achieving this bound is subject to stringent design constraints: It must have a hidden feedforward architecture that superlinearly amplifies its input for a time of order N, and the input connectivity must optimally match this architecture. The memory capacity of networks subject to saturating nonlinearities is further limited, and cannot exceed square root N. This limit can be realized by feedforward structures with divergent fan out that distributes the signal across neurons, thereby avoiding saturation. We illustrate the generality of the theory by showing that memory in fluid systems can be sustained by transient nonnormal amplification due to convective instability or the onset of turbulence.
View details for DOI 10.1073/pnas.0804451105
View details for Web of Science ID 000261489100065
View details for PubMedID 19020074
One-dimensional dynamics of attention and decision making in LIP
2008; 58 (1): 15-25
Where we allocate our visual spatial attention depends upon a continual competition between internally generated goals and external distractions. Recently it was shown that single neurons in the macaque lateral intraparietal area (LIP) can predict the amount of time a distractor can shift the locus of spatial attention away from a goal. We propose that this remarkable dynamical correspondence between single neurons and attention can be explained by a network model in which generically high-dimensional firing-rate vectors rapidly decay to a single mode. We find direct experimental evidence for this model, not only in the original attentional task, but also in a very different task involving perceptual decision making. These results confirm a theoretical prediction that slowly varying activity patterns are proportional to spontaneous activity, pose constraints on models of persistent activity, and suggest a network mechanism for the emergence of robust behavioral timing from heterogeneous neuronal populations.
View details for DOI 10.1016/j.neuron.2008.01.038
View details for Web of Science ID 000254946200006
View details for PubMedID 18400159
Function constrains network architecture and dynamics: A case study on the yeast cell cycle Boolean network
PHYSICAL REVIEW E
2007; 75 (5)
We develop a general method to explore how the function performed by a biological network can constrain both its structural and dynamical network properties. This approach is orthogonal to prior studies which examine the functional consequences of a given structural feature, for example a scale free architecture. A key step is to construct an algorithm that allows us to efficiently sample from a maximum entropy distribution on the space of Boolean dynamical networks constrained to perform a specific function, or cascade of gene expression. Such a distribution can act as a "functional null model" to test the significance of any given network feature, and can aid in revealing underlying evolutionary selection pressures on various network properties. Although our methods are general, we illustrate them in an analysis of the yeast cell cycle cascade. This analysis uncovers strong constraints on the architecture of the cell cycle regulatory network as well as significant selection pressures on this network to maintain ordered and convergent dynamics, possibly at the expense of sacrificing robustness to structural perturbations.
View details for DOI 10.1103/PhysRevE.75.051907
View details for Web of Science ID 000246890100094
View details for PubMedID 17677098
- E10 Orbifolds Journal of High Energy Physics 2005; 06 (057)
Twisted six dimensional gauge theories on tori, matrix models, and integrable systems
JOURNAL OF HIGH ENERGY PHYSICS
View details for Web of Science ID 000225279400057
- Holographic protection of chronology in universes of the Godel type PHYSICAL REVIEW D 2003; 67 (10)