28 research outputs found
Capturing cross-session neural population variability through self-supervised identification of consistent neuron ensembles
Targeted Neural Dynamical Modeling
Latent dynamics models have emerged as powerful tools for modeling and
interpreting neural population activity. Recently, there has been a focus on
incorporating simultaneously measured behaviour into these models to further
disentangle sources of neural variability in their latent space. These
approaches, however, are limited in their ability to capture the underlying
neural dynamics (e.g. linear) and in their ability to relate the learned
dynamics back to the observed behaviour (e.g. no time lag). To this end, we
introduce Targeted Neural Dynamical Modeling (TNDM), a nonlinear state-space
model that jointly models the neural activity and external behavioural
variables. TNDM decomposes neural dynamics into behaviourally relevant and
behaviourally irrelevant dynamics; the relevant dynamics are used to
reconstruct the behaviour through a flexible linear decoder and both sets of
dynamics are used to reconstruct the neural activity through a linear decoder
with no time lag. We implement TNDM as a sequential variational autoencoder and
validate it on simulated recordings and recordings taken from the premotor and
motor cortex of a monkey performing a center-out reaching task. We show that
TNDM is able to learn low-dimensional latent dynamics that are highly
predictive of behaviour without sacrificing its fit to the neural data
A Unified, Scalable Framework for Neural Population Decoding
Our ability to use deep learning approaches to decipher neural activity would
likely benefit from greater scale, in terms of both model size and datasets.
However, the integration of many neural recordings into one unified model is
challenging, as each recording contains the activity of different neurons from
different individual animals. In this paper, we introduce a training framework
and architecture designed to model the population dynamics of neural activity
across diverse, large-scale neural recordings. Our method first tokenizes
individual spikes within the dataset to build an efficient representation of
neural events that captures the fine temporal structure of neural activity. We
then employ cross-attention and a PerceiverIO backbone to further construct a
latent tokenization of neural population activities. Utilizing this
architecture and training framework, we construct a large-scale multi-session
model trained on large datasets from seven nonhuman primates, spanning over 158
different sessions of recording from over 27,373 neural units and over 100
hours of recordings. In a number of different tasks, we demonstrate that our
pretrained model can be rapidly adapted to new, unseen sessions with
unspecified neuron correspondence, enabling few-shot performance with minimal
labels. This work presents a powerful new approach for building deep learning
tools to analyze neural data and stakes out a clear path to training at scale.Comment: Accepted at NeurIPS 202
Inferring brain-wide interactions using data-constrained recurrent neural network models
Behavior arises from the coordinated activity of numerous anatomically and functionally distinct brain regions. Modern experimental tools allow unprecedented access to large neural populations spanning many interacting regions brain-wide. Yet, understanding such large-scale datasets necessitates both scalable computational models to extract meaningful features of inter-region communication and principled theories to interpret those features. Here, we introduce Current-Based Decomposition (CURBD), an approach for inferring brain-wide interactions using data-constrained recurrent neural network models that directly reproduce experimentally-obtained neural data. CURBD leverages the functional interactions inferred by such models to reveal directional currents between multiple brain regions. We first show that CURBD accurately isolates inter-region currents in simulated networks with known dynamics. We then apply CURBD to multi-region neural recordings obtained from mice during running, macaques during Pavlovian conditioning, and humans during memory retrieval to demonstrate the widespread applicability of CURBD to untangle brain-wide interactions underlying behavior from a variety of neural datasets
A spinal cord neuroprosthesis for locomotor deficits due to Parkinson’s disease
People with late-stage Parkinson’s disease (PD) often suffer from debilitating locomotor deficits that are resistant to currently available therapies. To alleviate these deficits, we developed a neuroprosthesis operating in closed loop that targets the dorsal root entry zones innervating lumbosacral segments to reproduce the natural spatiotemporal activation of the lumbosacral spinal cord during walking. We first developed this neuroprosthesis in a non-human primate model that replicates locomotor deficits due to PD. This neuroprosthesis not only alleviated locomotor deficits but also restored skilled walking in this model. We then implanted the neuroprosthesis in a 62-year-old male with a 30-year history of PD who presented with severe gait impairments and frequent falls that were medically refractory to currently available therapies. We found that the neuroprosthesis interacted synergistically with deep brain stimulation of the subthalamic nucleus and dopaminergic replacement therapies to alleviate asymmetry and promote longer steps, improve balance and reduce freezing of gait. This neuroprosthesis opens new perspectives to reduce the severity of locomotor deficits in people with PD
A neural population mechanism for rapid learning
This article is a preprint and has not been peer-reviewedLong-term learning of language, mathematics, and motor skills likely requires plastic changes in the cortex, but behavior often requires faster changes, sometimes based even on single errors. Here, we show evidence of one mechanism by which the brain can rapidly develop new motor output, seemingly without altering the functional connectivity between or within cortical areas. We recorded simultaneously from hundreds of neurons in the premotor (PMd) and primary motor (M1) cortices, and computed models relating these neural populations throughout adaptation to reaching movement perturbations. We found a signature of learning in the ″null subspace″ of PMd with respect to M1. Earlier experiments have shown that null subspace activity allows the motor cortex to alter preparatory activity without directly influencing M1. In our experiments, the null subspace planning activity evolved with the adaptation, yet the ″potent mapping″ that captures information sent to M1 was preserved. Our results illustrate a population-level mechanism within the motor cortices to adjust the output from one brain area to its downstream structures that could be exploited throughout the brain for rapid, on-line behavioral adaptation. [ENG]The authors would like to thank Dr. Sara Solla for her helpful discussions in preparing these analyses. M.G.P. received funding from NIH NINDST32 HD07418 and NIH NINDSF31 NS092356. J.A.G. received funding from the European Commission (FP7-PEOPLE-2013-IOF-627384). This project was additionally funded by NIH NINDSNS053603 and NIH NINDSNS074044.Peer reviewe
Neural Manifolds for the Control of Movement
The analysis of neural dynamics in several brain cortices has consistently uncovered low-dimensional manifolds that capture a significant fraction of neural variability. These neural manifolds are spanned by specific patterns of correlated neural activity, the “neural modes.” We discuss a model for neural control of movement in which the time-dependent activation of these neural modes is the generator of motor behavior. This manifold-based view of motor cortex may lead to a better understanding of how the brain controls movement.This work was supported in part by Grant NS053603 from the National Institute of Neurological Disorder and Stroke (L.E.M.), by Grant NS094748 from the National Institute of Neurological Disorder and Stroke (S.A.S.), by Grant FP7-PEOPLE-2013-IOF-627384 from the Commission of the European Union (J.A.G.), and by Grant F31-NS092356 from the National Institute of Neurological Disorder and Stroke and Grant T32-HD07418 from the National Center for Medical Rehabilitation Research (M.G.P.). We thank Carolina Massumoto for the monkey illustration.Peer reviewe