Abstract:
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.