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      Inferring single-trial neural population dynamics using sequential auto-encoders

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          Abstract

          Neuroscience is experiencing a revolution in which simultaneous recording of many thousands of neurons is revealing population dynamics that are not apparent from single-neuron responses. This structure is typically extracted from trial-averaged data, but deeper understanding requires studying single-trial phenomena, which is challenging due to incomplete sampling of the neural population, trial-to-trial variability, and fluctuations in action potential timing. We introduce Latent Factor Analysis via Dynamical Systems (LFADS), a deep learning method to infer latent dynamics from single-trial neural spiking data. LFADS uses a nonlinear dynamical system to infer the dynamics underlying observed spiking activity and to extract ‘de-noised’ single-trial firing rates. When applied to a variety of monkey and human motor cortical datasets, LFADS predicts observed behavioral variables with unprecedented accuracy, extracts precise estimates of neural dynamics on single trials, infers perturbations to those dynamics that correlate with behavioral choices, and combines data from non-overlapping recording sessions spanning months to improve inference of underlying dynamics.

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          Most cited references44

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          Neural population dynamics during reaching

          Most theories of motor cortex have assumed that neural activity represents movement parameters. This view derives from an analogous approach to primary visual cortex, where neural activity represents patterns of light. Yet it is unclear how well that analogy holds. Single-neuron responses in motor cortex appear strikingly complex, and there is marked disagreement regarding which movement parameters are represented. A better analogy might be with other motor systems, where a common principle is rhythmic neural activity. We found that motor cortex responses during reaching contain a brief but strong oscillatory component, something quite unexpected for a non-periodic behavior. Oscillation amplitude and phase followed naturally from the preparatory state, suggesting a mechanistic role for preparatory neural activity. These results demonstrate unexpected yet surprisingly simple structure in the population response. That underlying structure explains many of the confusing features of individual-neuron responses.
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            Generating coherent patterns of activity from chaotic neural networks.

            Neural circuits display complex activity patterns both spontaneously and when responding to a stimulus or generating a motor output. How are these two forms of activity related? We develop a procedure called FORCE learning for modifying synaptic strengths either external to or within a model neural network to change chaotic spontaneous activity into a wide variety of desired activity patterns. FORCE learning works even though the networks we train are spontaneously chaotic and we leave feedback loops intact and unclamped during learning. Using this approach, we construct networks that produce a wide variety of complex output patterns, input-output transformations that require memory, multiple outputs that can be switched by control inputs, and motor patterns matching human motion capture data. Our results reproduce data on premovement activity in motor and premotor cortex, and suggest that synaptic plasticity may be a more rapid and powerful modulator of network activity than generally appreciated.
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              Choice-specific sequences in parietal cortex during a virtual-navigation decision task

              The posterior parietal cortex (PPC) plays an important role in many cognitive behaviors; however, the neural circuit dynamics underlying PPC function are not well understood. Here we optically imaged the spatial and temporal activity patterns of neuronal populations in mice performing a PPC-dependent task that combined a perceptual decision and memory-guided navigation in a virtual environment. Individual neurons had transient activation staggered relative to one another in time, forming a sequence of neuronal activation spanning the entire length of a task trial. Distinct sequences of neurons were triggered on trials with opposite behavioral choices and defined divergent, choice-specific trajectories through a state space of neuronal population activity. Cells participating in the different sequences and at distinct time points in the task were anatomically intermixed over microcircuit length scales (< 100 micrometers). During working memory decision tasks the PPC may therefore perform computations through sequence-based circuit dynamics, rather than long-lived stable states, implemented using anatomically intermingled microcircuits.
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                Author and article information

                Journal
                101215604
                32338
                Nat Methods
                Nat. Methods
                Nature methods
                1548-7091
                1548-7105
                6 August 2018
                17 September 2018
                October 2018
                17 March 2019
                : 15
                : 10
                : 805-815
                Affiliations
                [1 ]Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Department of Neurosurgery, Emory University, Atlanta, Georgia, USA
                [2 ]Department of Neurosurgery, Stanford University, Stanford, California, USA
                [3 ]Department of Electrical Engineering, Stanford University, Stanford, California, USA
                [4 ]Stanford Neurosciences Institute, Stanford University, Stanford, California, USA
                [5 ]Neurosciences Graduate Program, Stanford University, Stanford, California, USA
                [6 ]Department of Neurobiology, Department of Bioengineering, Bio-X Program, Stanford University, Stanford, California, USA
                [7 ]Google AI, Google Inc. Mountain View, California, USA
                [8 ]Department of Electrical Engineering, University of California, Los Angeles, California, USA
                [9 ]Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA
                [10 ]Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, California, USA
                [11 ]Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Department of VA Medical Center, Providence, Rhode Island, USA
                [12 ]Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
                [13 ]School of Engineering, Brown Institute for Brain Science, Brown University, Providence, Rhode Island, USA
                [14 ]Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Department of Physiology and Cellular Biophysics, Columbia University, New York, New York, USA
                [15 ]Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
                Author notes
                [^]

                work done while at.

                Author Contributions

                C.P., D.J.O., and D.S. designed the study, performed analyses, and wrote the manuscript with input from all authors. D.S. and L.F.A. developed the algorithmic approach. C.P., J.C., and R.J. contributed to algorithmic development and analysis of synthetic data. D.J.O., S.D.S., J.C.K., E.M.T., M.T.K., S.I.R., and K.V.S. performed research with monkeys. C.P., L.R.H., K.V.S., and J.M.H. performed research with human research participants. All authors contributed to revising the manuscript.

                [* ]correspondence should be addressed to David Sussillo sussillo@ 123456google.com and Chethan Pandarinath chethan@ 123456gatech.edu .
                Article
                NIHMS1500948
                10.1038/s41592-018-0109-9
                6380887
                30224673
                d3e6cfb3-79b3-4c52-bf35-d52d13749664

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                Life sciences

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