Rewiring the brain: correlation-based model of artificial synaptic
plasticity by a brain-computer interface
Experiments on macaque monkeys reveal that neurons in Motor Cortex (MC)
display a variety of activities correlated to their co-activated muscles
and the motor task being performed. Generally, MC neurons with
overlapping muscle fields are spatially grouped together and may have
enhanced synaptic connections as opposed to more distant neurons.
Such connections are believed to be simultaneously a source and a
consequence of correlated neural activity among MC neurons, mediated by
Spike-Time-Dependent Plasticity (STDP) mechanisms. Consistent with this
paradigm, spike-triggered stimulation performed with Bidirectional
Brain-Computer-Interfaces (BBCI) can artificially strengthen synaptic
connections between distant MC sites and even between MC and spinal cord
sites, with changes that last several days. Here, a neural implant is
triggered by spikes from an MC site and electrically stimulates a
secondary target site after a set delay, the value of which is critical
in determining the efficacy of the procedure and consistent with
experimentally derived STDP windows. As the development of BBCIs
progresses, with applications ranging from a science-oriented tool to
clinical treatments, it is crucial to develop a theoretical
understanding of the interaction between neural implants, recurrent
neural activity from cortical sites, and the plasticity mechanisms that
modulate synaptic strengths.
In parallel with ongoing experiments, we are developing a recurrent
network model with probabilistic spiking mechanisms and plastic synapses
(STDP) capable of capturing both neural and synaptic activity statistics
relevant to BBCI protocols. This model successfully reproduces key
experimental results and we use analytical derivations to predict
optimal operational regimes for BBCIs. We make experimental predictions
concerning the efficacy of spike-triggered stimulation in different
regimes of cortical activity such as awake behaving states or sleep.
Importantly, this work provides a theoretical framework which is
intended as a design testbed for next-generations applications of BBCI.