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Experiments show that spike-triggered stimulation performed withBidirectional Brain-Computer-Interfaces (BBCI) can artificially strengthenconnections between separate neural sites in motor cortex (MC). What are theneuronal mechanisms responsible for these changes and how does targetedstimulation by a BBCI shape population-level synaptic connectivity? The presentwork describes a recurrent neural network model with probabilistic spikingmechanisms and plastic synapses capable of capturing both neural and synapticactivity statistics relevant to BBCI conditioning protocols. When spikes from aneuron recorded at one MC site trigger stimuli at a second target site after afixed delay, the connections between sites are strengthened for spike-stimulusdelays consistent with experimentally derived spike time dependent plasticity(STDP) rules. However, the relationship between STDP mechanisms at the level ofnetworks, and their modification with neural implants remains poorlyunderstood. Using our model, we successfully reproduces key experimentalresults and use analytical derivations, along with novel experimental data. Wethen derive optimal operational regimes for BBCIs, and formulate predictionsconcerning the efficacy of spike-triggered stimulation in different regimes ofcortical activity.

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