Correlation-based model of artificially induced plasticity in motor cortex by a bidirectional brain-computer interface.

Guillaume Lajoie,John F Kalaska,Eberhard E Fetz,Adrienne L Fairhall,Nedialko I Krouchev

doi:10.1371/journal.pcbi.1005343

Abstract

Experiments show that spike-triggered stimulation performed with Bidirectional Brain-Computer-Interfaces (BBCI) can artificially strengthen connections between separate neural sites in motor cortex (MC). When spikes from a neuron recorded at one MC site trigger stimuli at a second target site after a fixed delay, the connections between sites eventually strengthen. It was also found that effective spike-stimulus delays are consistent with experimentally derived spike-timing-dependent plasticity (STDP) rules, suggesting that STDP is key to drive these changes. However, the impact of STDP at the level of circuits, and the mechanisms governing its modification with neural implants remain poorly understood. The present work describes a recurrent neural network model with probabilistic spiking mechanisms and plastic synapses capable of capturing both neural and synaptic activity statistics relevant to BBCI conditioning protocols. Our model successfully reproduces key experimental results, both established and new, and offers mechanistic insights into spike-triggered conditioning. Using analytical calculations and numerical simulations, we derive optimal operational regimes for BBCIs, and formulate predictions concerning the efficacy of spike-triggered conditioning in different regimes of cortical activity.

Highlights

The cerebral cortex contains interacting neurons whose functional connections are modified through repeated patterns of activation
We develop a computational network model with plastic synapses that serves as a predictive tool for Bidirectional Brain-Computer-Interfaces (BBCI) protocols
We show how the efficacy of BBCIs is influenced by cortical activity statistics and we propose state-based stimulation strategies for driving artificially-induced synaptic plasticity

Summary

Introduction

The cerebral cortex contains interacting neurons whose functional connections are modified through repeated patterns of activation. [1,2,3,4,5]) Functional relationships between such neural populations are known to change over time, reinforcing relevant pathways [6,7,8,9]. These changes are the result of plasticity mechanisms acting on myriad synaptic connections between cortical neurons. Most of them are relatively weak but can potentiate under the right conditions It is not always clear what such conditions might be, or how one can interact with them for experimental or clinical purposes. While recent and ongoing work elucidates various plasticity mechanisms at the level of individual synapses, it is still unknown how these combine to shape the recurrently connected circuits that support population-level neural computations

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