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

TL;DR

This paper presents a neural network model that explains how bidirectional brain-computer interfaces can induce plasticity in the motor cortex by mimicking spike-timing dependent plasticity, supported by experimental data and analytical insights.

Contribution

The work introduces a probabilistic spiking neural network model that captures BBCI-induced plasticity mechanisms and predicts optimal stimulation regimes.

Findings

Model reproduces experimental plasticity results

Identifies spike timing conditions for strengthening connections

Predicts effective stimulation parameters for BBCI

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). What are the neuronal mechanisms responsible for these changes and how does targeted stimulation by a BBCI shape population-level synaptic connectivity? 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. 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 are strengthened for spike-stimulus delays consistent with experimentally derived spike time dependent plasticity (STDP) rules. However, the relationship between STDP mechanisms at the level of networks, and their modification with neural implants remains poorly understood. Using our model, we successfully reproduces key experimental results and use analytical derivations, along with novel experimental data. We then derive optimal operational regimes for BBCIs, and formulate predictions concerning the efficacy of spike-triggered stimulation in different regimes of cortical activity.