Neural Co-Processors for Restoring Brain Function: Results from a
Cortical Model of Grasping
- URL: http://arxiv.org/abs/2210.11478v2
- Date: Mon, 20 Mar 2023 22:25:33 GMT
- Title: Neural Co-Processors for Restoring Brain Function: Results from a
Cortical Model of Grasping
- Authors: Matthew J. Bryan (1), Linxing Preston Jiang (1), Rajesh P N Rao (1)
((1) Neural Systems Laboratory, Paul G. Allen School of Computer Science &
Engineering, University of Washington)
- Abstract summary: We propose "neural co-processors" which use artificial neural networks and deep learning to learn optimal closed-loop stimulation policies.
The co-processor adapts the stimulation policy as the biological circuit itself adapts to the stimulation, achieving a form of brain-device co-adaptation.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Objective: A major challenge in designing closed-loop brain-computer
interfaces is finding optimal stimulation patterns as a function of ongoing
neural activity for different subjects and objectives. Approach: To achieve
goal-directed closed-loop neurostimulation, we propose "neural co-processors"
which use artificial neural networks and deep learning to learn optimal
closed-loop stimulation policies, shaping neural activity and bridging injured
neural circuits for targeted repair and rehabilitation. The co-processor adapts
the stimulation policy as the biological circuit itself adapts to the
stimulation, achieving a form of brain-device co-adaptation. Here we use
simulations to lay the groundwork for future in vivo tests of neural
co-processors. We leverage a cortical model of grasping, to which we applied
various forms of simulated lesions, allowing us to develop the critical
learning algorithms and study adaptations to non-stationarity. Main results:
Our simulations show the ability of a neural co-processor to learn a
stimulation policy using a supervised learning approach, and to adapt that
policy as the underlying brain and sensors change. Our co-processor
successfully co-adapted with the simulated brain to accomplish the
reach-and-grasp task after a variety of lesions were applied, achieving
recovery towards healthy function. Significance: Our results provide the first
proof-of-concept demonstration of a co-processor for adaptive
activity-dependent closed-loop neurostimulation, optimizing for a
rehabilitation goal. While a gap remains between simulations and applications,
our results provide insights on how co-processors may be developed for learning
complex adaptive stimulation policies for a variety of neural rehabilitation
and neuroprosthetic applications.
Related papers
- Enhancing learning in artificial neural networks through cellular heterogeneity and neuromodulatory signaling [52.06722364186432]
We propose a biologically-informed framework for enhancing artificial neural networks (ANNs)
Our proposed dual-framework approach highlights the potential of spiking neural networks (SNNs) for emulating diverse spiking behaviors.
We outline how the proposed approach integrates brain-inspired compartmental models and task-driven SNNs, bioinspiration and complexity.
arXiv Detail & Related papers (2024-07-05T14:11:28Z) - A Differentiable Approach to Multi-scale Brain Modeling [3.5874544981360987]
We present a multi-scale differentiable brain modeling workflow utilizing BrainPy, a unique differentiable brain simulator.
At the single-neuron level, we implement differentiable neuron models and employ gradient methods to optimize their fit to electrophysiological data.
On the network level, we incorporate connectomic data to construct biologically constrained network models.
arXiv Detail & Related papers (2024-06-28T07:41:31Z) - Coprocessor Actor Critic: A Model-Based Reinforcement Learning Approach For Adaptive Brain Stimulation [5.734832905595746]
Coprocessor Actor Critic is a novel, model-based reinforcement learning (MBRL) approach for learning neural coprocessor policies for brain stimulation.
We show that our approach overcomes the limitations of traditional MFRL methods in terms of sample efficiency and task success.
arXiv Detail & Related papers (2024-06-10T18:23:03Z) - MindBridge: A Cross-Subject Brain Decoding Framework [60.58552697067837]
Brain decoding aims to reconstruct stimuli from acquired brain signals.
Currently, brain decoding is confined to a per-subject-per-model paradigm.
We present MindBridge, that achieves cross-subject brain decoding by employing only one model.
arXiv Detail & Related papers (2024-04-11T15:46:42Z) - Hebbian Learning based Orthogonal Projection for Continual Learning of
Spiking Neural Networks [74.3099028063756]
We develop a new method with neuronal operations based on lateral connections and Hebbian learning.
We show that Hebbian and anti-Hebbian learning on recurrent lateral connections can effectively extract the principal subspace of neural activities.
Our method consistently solves for spiking neural networks with nearly zero forgetting.
arXiv Detail & Related papers (2024-02-19T09:29:37Z) - Brain-Inspired Machine Intelligence: A Survey of
Neurobiologically-Plausible Credit Assignment [65.268245109828]
We examine algorithms for conducting credit assignment in artificial neural networks that are inspired or motivated by neurobiology.
We organize the ever-growing set of brain-inspired learning schemes into six general families and consider these in the context of backpropagation of errors.
The results of this review are meant to encourage future developments in neuro-mimetic systems and their constituent learning processes.
arXiv Detail & Related papers (2023-12-01T05:20:57Z) - Learning to Act through Evolution of Neural Diversity in Random Neural
Networks [9.387749254963595]
In most artificial neural networks (ANNs), neural computation is abstracted to an activation function that is usually shared between all neurons.
We propose the optimization of neuro-centric parameters to attain a set of diverse neurons that can perform complex computations.
arXiv Detail & Related papers (2023-05-25T11:33:04Z) - Contrastive-Signal-Dependent Plasticity: Forward-Forward Learning of
Spiking Neural Systems [73.18020682258606]
We develop a neuro-mimetic architecture, composed of spiking neuronal units, where individual layers of neurons operate in parallel.
We propose an event-based generalization of forward-forward learning, which we call contrastive-signal-dependent plasticity (CSDP)
Our experimental results on several pattern datasets demonstrate that the CSDP process works well for training a dynamic recurrent spiking network capable of both classification and reconstruction.
arXiv Detail & Related papers (2023-03-30T02:40:28Z) - Backprop-Free Reinforcement Learning with Active Neural Generative
Coding [84.11376568625353]
We propose a computational framework for learning action-driven generative models without backpropagation of errors (backprop) in dynamic environments.
We develop an intelligent agent that operates even with sparse rewards, drawing inspiration from the cognitive theory of planning as inference.
The robust performance of our agent offers promising evidence that a backprop-free approach for neural inference and learning can drive goal-directed behavior.
arXiv Detail & Related papers (2021-07-10T19:02:27Z) - Brain Co-Processors: Using AI to Restore and Augment Brain Function [2.3986080077861787]
We introduce brain co-processors, devices that combine decoding and encoding in a unified framework using artificial intelligence (AI)
Brain co-processors can be used for a range of applications, from inducing Hebbian plasticity for rehabilitation after brain injury to reanimating paralyzed limbs and enhancing memory.
We describe a new framework for developing brain co-processors based on artificial neural networks, deep learning and reinforcement learning.
arXiv Detail & Related papers (2020-12-06T21:06:28Z) - Deep Reinforcement Learning for Neural Control [4.822598110892847]
We present a novel methodology for control of neural circuits based on deep reinforcement learning.
We map neural circuits and their connectome into a grid-world like setting and infers the actions needed to achieve aimed behavior.
Our framework successfully infers neuropeptidic currents and synaptic architectures for control of chemotaxis.
arXiv Detail & Related papers (2020-06-12T17:41:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.