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Computation Through neural population dynamics

ANNUAL REVIEW OF NEUROSCIENCE, VOL 43 2020年第1期43卷 249-275页

作者： Vyas, Saurabh Golub, Matthew D. Sussillo, David Shenoy, Krishna V. Stanford Univ Dept Bioengn Stanford CA 94305 USA Stanford Univ Dept Elect Engn Stanford CA 94305 USA Stanford Univ Wu Tsai Neurosci Inst Stanford CA 94305 USA Google Inc Google AI Stanford CA 94305 USA Stanford Univ BioX Inst Neurosci Program Dept Neurobiol Stanford CA 94305 USA Stanford Univ Howard Hughes Med Inst Stanford CA 94305 USA

Significant experimental, computational, and theoretical work has identified rich structure within the coordinated activity of interconnected neural populations. An emerging challenge now is to uncover the nature of the associated computations, how they are implemented, and what role they play in driving behavior. We term this computation through neural population dynamics. If successful, this framework will reveal general motifs of neural population activity and quantitatively describe how neural population dynamics implement computations necessary for driving goal-directed behavior. Here, we start with a mathematical primer on dynamical systems theory and analytical tools necessary to apply this perspective to experimental data. Next, we highlight some recent discoveries resulting from successful application of dynamical systems. We focus on studies spanning motor control, timing, decision-making, and working memory. Finally, we briefly discuss promising recent lines of investigation and future directions for the computation through neural population dynamics framework.

关键词： neural computation neural population dynamics dynamical systems state spaces

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Recurrent neural Network Learning of Performance and Intrinsic population dynamics from Sparse neural Data 29th

Recurrent Neural Network Learning of Performance and Intrins...

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29th International Conference on Artificial neural Networks (ICANN)

作者： Salatiello, Alessandro Giese, Martin A. Univ Clin Tubingen Sect Computat Sensomotor Dept Cognit Neurol Ctr Integrat Neurosci Tubingen Germany Univ Clin Tubingen Hertie Inst Clin Brain Res Tubingen Germany Int Max Planck Res Sch Intelligent Syst Tubingen Germany

ISBN: (纸本)9783030616090;9783030616083

Recurrent neural Networks (RNNs) are popular models of brain function. The typical training strategy is to adjust their input-output behavior so that it matches that of the biological circuit of interest. Even though this strategy ensures that the biological and artificial networks perform the same computational task, it does not guarantee that their internal activity dynamics match. This suggests that the trained RNNs might end up performing the task employing a different internal computational mechanism. In this work, we introduce a novel training strategy that allows learning not only the input-output behavior of an RNN but also its internal network dynamics. We test the proposed method by training an RNN to simultaneously reproduce internal dynamics and output signals of a physiologically-inspired neural model of motor cortical and muscle activity dynamics. Remarkably, we show that the reproduction of the internal dynamics is successful even when the training algorithm relies on the activities of a small subset of neurons sampled from the biological network. Furthermore, we show that training the RNNs with this method significantly improves their generalization performance. Overall, our results suggest that the proposed method is suitable for building powerful functional RNN models, which automatically capture important computational properties of the biological circuit of interest from sparse neural recordings.

关键词： Recurrent neural networks neural population dynamics Full-FORCE learning Motor control Muscle synergies

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Inferring population dynamics in macaque cortex

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JOURNAL OF neural ENGINEERING 2023年第5期20卷 056041-056041页

作者： Meghanath, Ganga Jimenez, Bryan Makin, Joseph G. Purdue Univ Elmore Family Sch Elect & Comp Engn W Lafayette IN 47907 USA

Objective. The proliferation of multi-unit cortical recordings over the last two decades, especially in macaques and during motor-control tasks, has generated interest in neural 'population dynamics': the time evolution of neural activity across a group of neurons working together. A good model of these dynamics should be able to infer the activity of unobserved neurons within the same population and of the observed neurons at future times. Accordingly, Pandarinath and colleagues have introduced a benchmark to evaluate models on these two (and related) criteria: four data sets, each consisting of firing rates from a population of neurons, recorded from macaque cortex during movement-related tasks. Approach. Since this is a discriminative-learning task, we hypothesize that general-purpose architectures based on recurrent neural networks (RNNs) trained with masking can outperform more 'bespoke' models. To capture long-distance dependencies without sacrificing the autoregressive bias of recurrent networks, we also propose a novel, hybrid architecture ('TERN') that augments the RNN with self-attention, as in transformer networks. Main results. Our RNNs outperform all published models on all four data sets in the benchmark. The hybrid architecture improves performance further still. Pure transformer models fail to achieve this level of performance, either in our work or that of other groups. Significance. We argue that the autoregressive bias imposed by RNNs is critical for achieving the highest levels of performance, and establish the state of the art on the neural latents benchmark. We conclude, however, by proposing that the benchmark be augmented with an alternative evaluation of latent dynamics that favors generative over discriminative models like the ones we propose in this report.

关键词： neural population dynamics motor cortex neural latents benchmark multielectrode arrays spiking data RNNs self-attention

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Invariant neural dynamics drive commands to control different movements

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CURRENT BIOLOGY 2023年第14期33卷 2962-+页

作者： Athalye, Vivek R. Khanna, Preeya Gowda, Suraj Orsborn, Amy L. Costa, Rui M. Carmena, Jose M. Columbia Univ Zuckerman Mind Brain Behav Inst Dept Neurosci New York NY 10027 USA Columbia Univ Zuckerman Mind Brain Behav Inst Dept Neurol New York NY 10027 USA Univ Calif San Francisco Dept Neurol San Francisco CA 94158 USA Univ Washington Dept Bioengn Seattle WA 98195 USA Univ Washington Dept Elect & Comp Engn Seattle WA 98195 USA Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA Univ Calif Berkeley Helen Wills Neurosci Inst Berkeley CA 94720 USA Univ Calif Berkeley UC Berkeley UCSF Joint Grad Program Bioengn Berkeley CA 94720 USA

It has been proposed that the nervous system has the capacity to generate a wide variety of movements because it reuses some invariant code. Previous work has identified that dynamics of neural population activity are similar during different movements, where dynamics refer to how the instantaneous spatial pattern of population activity changes in time. Here, we test whether invariant dynamics of neural populations are actually used to issue the commands that direct movement. Using a brain -machine interface (BMI) that transforms rhesus macaques' motor-cortex activity into commands for a neuroprosthetic cursor, we discovered that the same command is issued with different neural-activity patterns in different movements. However, these different patterns were predictable, as we found that the transitions between activity patterns are governed by the same dynamics across movements. These invariant dynamics are low dimensional, and critically, they align with the BMI, so that they predict the specific component of neural activity that actually issues the next command. We introduce a model of optimal feedback control (OFC) that shows that invariant dynamics can help transform movement feedback into commands, reducing the input that the neural population needs to control movement. Altogether our results demonstrate that invariant dynamics drive commands to control a variety of movements and show how feedback can be integrated with invariant dynamics to issue generalizable commands.

关键词： neural population dynamics motor cortex motor control brain-machine interfaces neuroprosthetics optimal feedback control motor commands movement representations dynamical systems

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Parallel movement planning is achieved via an optimal preparatory state in motor cortex

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CELL REPORTS 2023年第2期42卷 112136页

作者： Meirhaeghe, Nicolas Riehle, Alexa Brochier, Thomas Aix Marseille Univ Inst Neurosci Timone INT UMR 7289 CNRS F-13005 Marseille France Julich Res Ctr Inst Neurosci & Med INM 6 D-52428 Julich Germany

How do patterns of neural activity in the motor cortex contribute to the planning of a movement? A recent theory developed for single movements proposes that the motor cortex acts as a dynamical system whose initial state is optimized during the preparatory phase of the movement. This theory makes important yet untested predictions about preparatory dynamics in more complex behavioral settings. Here, we analyze preparatory activity in non-human primates planning not one but two movements simultaneously. As pre-dicted by the theory, we find that parallel planning is achieved by adjusting preparatory activity within an optimal subspace to an intermediate state reflecting a trade-off between the two movements. The theory quantitatively accounts for the relationship between this intermediate state and fluctuations in the animals' behavior down at the trial level. These results uncover a simple mechanism for planning multiple movements in parallel and further point to motor planning as a controlled dynamical process.

关键词： neural population dynamics motor planning motor cortex non-human primate dynamical system theory representational theory Research topic(s)CP: Neuroscience

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Identification of Switching Linear dynamics in Distributed neural populations 11

Identification of Switching Linear Dynamics in Distributed N...

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11th International IEEE EMBS Conference on neural Engineering (IEEE/EMBS NER)

作者： Osuna-Orozco, Rodrigo Santacruz, Samantha R. Univ Texas Austin Dept Biomed Engn Austin TX 78712 USA

ISBN: (纸本)9781665462921

Modern techniques have enabled recording large numbers of anatomically distributed neural populations at high spatial and temporal resolution. These populations interact in a complex, often nonlinear and nonstationary, way. Thus, there is a need for robust and versatile methods that can capture these complex dynamics while remaining adequately constrained by the observed data. Recently, there has been increasing interest in Switching linear dynamical systems for modeling nonlinear neural dynamics in a piecewise linear fashion, especially when abrupt transitions occur that may be related to distinct behaviors. We present a computational and experimental study characterizing switching linear dynamics for interacting neural populations. First, we motivate the use of the time-varying autoregression with low rank tensors (TVART) method by recovering the linearized dynamics about the stable attractors of a simple model of two interacting neural populations. Then, we identify the time-varying dynamics of the power feature time series derived from local field potentials recorded from a non-human primate animal subject. Robust variation in the dynamics where concurrent with the onset of the task behavior. Finally, we consider the variations of dynamics in the resting state and demonstrate that low rank time-varying dynamics can reduce the prediction error when compared to a full rank stationary model.

关键词： Switching Linear Dynamical Systems Time-Varying Autoregression neural population dynamics Local Field Potentials

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Dynamic Structure of Functional Networks During Reaching

Dynamic Structure of Functional Networks During Reaching

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作者： Sundiang, Marina Janelle Morales The University of Chicago

学位级别：Ph.D., Doctor of Philosophy

Individual neurons are interconnected, resulting in coordinated patterns of activity and emergent population dynamics that underlie complex behavior. I investigated the time-varying co-activity of neural populations, summarized as functional networks (FN), and their relation to motor behavior. First, I found that the structure of FNs in the primary motor cortex of macaques performing an instructed-delay reaching task was specific to the instructed reach, and that FNs constructed from trials with closer reach directions are also closer in network space. I extended this analysis by computing co-activity within a short interval across time to construct temporal FNs. This revealed that reach-specific differences in FNs emerge shortly after instruction and, consequently, become decodable for reach direction. In fact, FNs provide an additional source of information about behavior beyond what is carried by firing rates alone. Next, in the sensorimotor cortex of a marmoset performing a self-initiated virtual prey capture task, FNs at specific task events (such as trial start, target presentation, etc.) can be discriminated based on its nearest neighbors using either a low-dimensional metric (distance in an embedding space) or a network alignment score. Partitioning temporal FNs based on structure showed FN states that corresponded to specific behaviors such as arm extension or licking, and that successful trials involve a stereotyped sequence of these states. These results suggest that FNs can provide novel insight on the statistical regularities of neural co-activity that produce neural population dynamics during voluntary goal-directed reaching behavior

关键词： Functional networks Motor control Non-human primate Primary motor cortex neural population dynamics

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The roles of internal dynamics and proprioceptive feedback in motor cortex during movement execution

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NEUROCOMPUTING 2025年 629卷

作者： Jiang, Hongru Bu, Xiangdong Zheng, Zhiyan Tang, Huajin Pan, Xiaochuan Chen, Yao Shanghai Jiao Tong Univ Sch Biomed Engn Shanghai 200240 Peoples R China Zhejiang Univ Coll Comp Sci & Technol State Key Lab Brain Machine Intelligence Hangzhou 310027 Peoples R China East China Univ Sci & Technol Sch Math Shanghai 200237 Peoples R China

The motor cortex controls arm movements by sending commands to lower motor centers. The synaptic connections within the motor cortex (internal dynamics) are vital to generate motor commands during movement execution. However, recent studies suggest that proprioception also contribute to motor cortex processing. To investigate the contributions of internal dynamics and proprioceptive feedback to movement execution, we built a recurrent neural network model of the motor cortex;the model receives proprioceptive feedback from a virtual arm performing a delayed-reach task. We found that both internal dynamics and proprioceptive feedback contribute to the resemblance to the real motor cortex data. We then dissected their contributions by disrupting them separately. Internal dynamics dominate the generation of neural population activity, while proprioceptive feedback modulates neural responses and controls movement deceleration. Additionally, proprioceptive feedback improves the network's robustness in noisy initial conditions. We further investigated the relative importance of the components in proprioceptive feedback and found that hand velocity is most important. Our findings could inform the development of neural prosthetics that can replicate the sensorimotor control of the biological system.

关键词： Motor control Motor cortex Proprioceptive feedback Inhibitory stabilized network neural population dynamics

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Distinct neural adaptations to time demand in the striatum and the hippocampus

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CURRENT BIOLOGY 2024年第1期34卷 156-170.e7页

作者： Rolando, Felipe Kononowicz, Tadeusz W. Duhamel, Jean-Rene Doyere, Valerie Wirth, Sylvia Univ Lyon 1 Inst Sci Cognit Marc Jeannerod CNRS 67 Blvd Pinel F-69500 Bron France Univ Paris Saclay Inst Neurosci Paris Saclay NeuroPSI CNRS F-91400 Saclay France Polish Acad Sci Inst Psychol Ul Jaracza 1 PL-00378 Warsaw Poland

How do neural codes adjust to track time across a range of resolutions, from milliseconds to multi -seconds, as a function of the temporal frequency at which events occur? To address this question, we studied timemodulated cells in the striatum and the hippocampus, while macaques categorized three nested intervals within the sub -second or the supra -second range (up to 1, 2, 4, or 8 s), thereby modifying the temporal resolution needed to solve the task. Time -modulated cells carried more information for intervals with explicit timing demand, than for any other interval. The striatum, particularly the caudate, supported the most accurate temporal prediction throughout all time ranges. Strikingly, its temporal readout adjusted non -linearly to the time range, suggesting that the striatal resolution shifted from a precise millisecond to a coarse multi -second range as a function of demand. This is in line with monkey's behavioral latencies, which indicated that they tracked time until 2 s but employed a coarse categorization strategy for durations beyond. By contrast, the hippocampus discriminated only the beginning from the end of intervals, regardless of the range. We propose that the hippocampus may provide an overall poor signal marking an event's beginning, whereas the striatum optimizes neural resources to process time throughout an interval adapting to the ongoing timing necessity.

关键词： ongoing timing neural population dynamics temporal range adaptation striatum hippocampus time cells macaques

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Simultaneous, cortex-wide dynamics of up to 1 million neurons reveal unbounded scaling of dimensionality with neuron number

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NEURON 2024年第10期112卷 1694-1709.e5页

作者： Manley, Jason Lu, Sihao Barber, Kevin Demas, Jeffrey Kim, Hyewon Meyer, David Traub, Francisca Martinez Vaziri, Alipasha Rockefeller Univ Lab Neurotechnol & Biophys New York NY 10065 USA Rockefeller Univ Kavli Neural Syst Inst New York NY 10065 USA

The brain's remarkable properties arise from the collective activity of millions of neurons. Widespread application of dimensionality reduction to multi-neuron recordings implies that neural dynamics can be approximated by low-dimensional "latent"signals reflecting neural computations. However, can such low-dimensional representations truly explain the vast range of brain activity, and if not, what is the appropriate resolution and scale of recording to capture them? Imaging neural activity at cellular resolution and nearsimultaneously across the mouse cortex, we demonstrate an unbounded scaling of dimensionality with neuron number in populations up to 1 million neurons. Although half of the neural variance is contained within sixteen dimensions correlated with behavior, our discovered scaling of dimensionality corresponds to an ever-increasing number of neuronal ensembles without immediate behavioral or sensory correlates. The activity patterns underlying these higher dimensions are fine grained and cortex wide, highlighting that largescale, cellular-resolution recording is required to uncover the full substrates of neuronal computations.

关键词： volumetric calcium imaging two-photon microscopy large-scale imaging light beads microscopy spontaneous cortical dynamics spontaneous behavior dimensionality reduction neural decoding neural population dynamics neural manifolds

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