NYU ECNU Joint Institute of Brain and Cognitive science

Shanghai, China

NYU ECNU Joint Institute of Brain and Cognitive science

Shanghai, China
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Wang X.-J.,New York University | Wang X.-J.,NYU ECNU Joint Institute of Brain and Cognitive Science | Kennedy H.,French Institute of Health and Medical Research | Kennedy H.,University of Lyon
Current Opinion in Neurobiology | Year: 2016

Louis Henry Sullivan, the father of skyscrapers, famously stated 'Form ever follows function'. In this short review, we will focus on the relationship between form (structure) and function (dynamics) in the brain. We summarize recent advances on the quantification of directed- and weighted-mesoscopic connectivity of mammalian cortex, the exponential distance rule for mesoscopic and microscopic circuit wiring, a spatially embedded random model of inter-areal cortical networks, and a large-scale dynamical circuit model of money's cortex that gives rise to a hierarchy of timescales. These findings demonstrate that inter-areal cortical networks are dense (hence such concepts as 'small-world' need to be refined when applied to the brain), spatially dependent (therefore purely topological approach of graph theory has limited applicability) and heterogeneous (consequently cortical areas cannot be treated as identical 'nodes'). © 2016 Elsevier Ltd.

Wei W.,New York University | Wang X.-J.,New York University | Wang X.-J.,NYU ECNU Joint Institute of Brain and Cognitive Science
Neural Computation | Year: 2016

Ramping neuronal activity refers to spiking activity with a rate that increases quasi-linearly over time. It has been observed in multiple cortical areas and is correlated with evidence accumulation processes or timing. In this work,we investigated the downstream effect of ramping neuronal activity through synapses that display short-term facilitation (STF) or depression (STD).We obtained an analytical result for a synapse driven by deterministic linear ramping input that exhibits pure STF or STD and numerically investigated the general case when a synapse displays both STF and STD.We show that the analytical deterministic solution gives an accurate description of the averaging synaptic activation of many inputs converging onto a postsynaptic neuron, even when fluctuations in the ramping input are strong. Activation of a synapsewith STF shows an initial cubical increase with time, followed by a linear ramping similar to a synapse without STF. Activation of a synapse with STD grows in time to a maximum before falling and reaching a plateau, and this steady state is independent of the slope of the ramping input. For a synapse displaying both STF and STD, an increase in the depression time constant from a value much smaller than the facilitation time constant τF to a value much larger than τF leads to a transition from facilitation dominance to depression dominance. Therefore, our work provides insights into the impact of ramping neuronal activity on downstream neurons through synapses that display short-term plasticity. In a perceptual decision-making process, ramping activity has been observed in the parietal and prefrontal cortices, with a slope that decreases with task difficulty. Our work predicts that neurons downstream from such a decision circuit could instead display a firing plateau independent of the task difficulty, provided that the synaptic connection is endowed with short-term depression. © 2016 Massachusetts Institute of Technology.

Chaisangmongkon W.,Yale University | Chaisangmongkon W.,King Mongkut's University of Technology Thonburi | Swaminathan S.K.,University of Chicago | Freedman D.J.,University of Chicago | And 4 more authors.
Neuron | Year: 2017

Decision making involves dynamic interplay between internal judgements and external perception, which has been investigated in delayed match-to-category (DMC) experiments. Our analysis of neural recordings shows that, during DMC tasks, LIP and PFC neurons demonstrate mixed, time-varying, and heterogeneous selectivity, but previous theoretical work has not established the link between these neural characteristics and population-level computations. We trained a recurrent network model to perform DMC tasks and found that the model can remarkably reproduce key features of neuronal selectivity at the single-neuron and population levels. Analysis of the trained networks elucidates that robust transient trajectories of the neural population are the key driver of sequential categorical decisions. The directions of trajectories are governed by network self-organized connectivity, defining a “neural landscape” consisting of a task-tailored arrangement of slow states and dynamical tunnels. With this model, we can identify functionally relevant circuit motifs and generalize the framework to solve other categorization tasks. © 2017

Seo H.,Yale University | Cai X.,Yale University | Cai X.,NYU ECNU Joint Institute of Brain and Cognitive science | Donahue C.H.,Yale University | And 2 more authors.
Science | Year: 2014

Although human and animal behaviors are largely shaped by reinforcement and punishment, choices in social settings are also influenced by information about the knowledge and experience of other decision-makers. During competitive games, monkeys increased their payoffs by systematically deviating from a simple heuristic learning algorithm and thereby countering the predictable exploitation by their computer opponent. Neurons in the dorsomedial prefrontal cortex (dmPFC) signaled the animal's recent choice and reward history that reflected the computer's exploitative strategy. The strength of switching signals in the dmPFC also correlated with the animal's tendency to deviate from the heuristic learning algorithm. Therefore, the dmPFC might provide control signals for overriding simple heuristic learning algorithms based on the inferred strategies of the opponent.

Engel T.A.,Yale University | Engel T.A.,Stanford University | Chaisangmongkon W.,Yale University | Freedman D.J.,University of Chicago | And 2 more authors.
Nature Communications | Year: 2015

The ability to categorize stimuli into discrete behaviourally relevant groups is an essential cognitive function. To elucidate the neural mechanisms underlying categorization, we constructed a cortical circuit model that is capable of learning a motion categorization task through reward-dependent plasticity. Here we show that stable category representations develop in neurons intermediate to sensory and decision layers if they exhibit choice-correlated activity fluctuations (choice probability). In the model, choice probability and task-specific interneuronal correlations emerge from plasticity of top-down projections from decision neurons. Specific model predictions are confirmed by analysis of single-neuron activity from the monkey parietal cortex, which reveals a mixture of directional and categorical tuning, and a positive correlation between category selectivity and choice probability. Beyond demonstrating a circuit mechanism for categorization, the present work suggests a key role of plastic top-down feedback in simultaneously shaping both neural tuning and correlated neural variability. © 2015 Macmillan Publishers Limited. All rights reserved.

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