Paper Info
Title
Correlation-based model of artificially induced plasticity in motor cortex by a bidirectional brain-computer interface.
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.
Year
DOI
Venue
2017
10.1371/journal.pcbi.1005343
PLOS COMPUTATIONAL BIOLOGY
Field
DocType
Volume
Synapse,Brain implant,Neuroscience,Biology,Brain–computer interface,Recurrent neural network,Synaptic plasticity,Artificial intelligence,Neuroplasticity,Artificial neural network,Genetics,Plasticity
Journal
13
Issue
ISSN
Citations 
2
1553-734X
1
PageRank 
References 
Authors
0.35
9
5
Name
Order
Citations
PageRank
Guillaume Lajoie186.67
Nedialko Krouchev230.98
John F Kalaska371.29
Adrienne L. Fairhall413316.10
Eberhard E. Fetz54514.94