Manipulation of neuronal activity by an artificial spiking neural network implemented on a closed-loop brain-computer interface in non-human primates
Jonathan Mishler, Richy Yun, Steve Perlmutter, Rajesh P N Rao, Eberhard Fetz
TL;DR
Researchers used a brain-computer interface to connect real neurons in monkeys with artificial networks, allowing them to control and predict brain activity.
Contribution
A novel hybrid biological-artificial neural system using a closed-loop brain-computer interface to modulate neural activity in non-human primates.
Findings
Altered closed-loop dynamics in the cortex depend on connectivity between neurons and artificial units and the timing of stimulation.
Closed-loop dynamics can be reliably modeled from open-loop measurements.
The system can modulate biological neural activity through artificial networks and microstimulation.
Abstract
Objective. Closed-loop brain-computer interfaces can be used to bridge, modulate, or repair damaged connections within the brain to restore functional deficits. Towards this goal, we demonstrate that small artificial spiking neural networks can be bidirectionally interfaced with single neurons (SNs) in the neocortex of non-human primates (NHPs) to create artificial connections between the SNs to manipulate their activity in predictable ways. Approach. Spikes from a small group of SNs were recorded from primary motor cortex of two awake NHPs during rest. The SNs were then interfaced with a small network of integrate-and-fire units (IFUs) that were programmed on a custom clBCI. Spikes from the SNs evoked excitatory and/or inhibitory postsynaptic potentials in the IFUs, which themselves spiked when their membrane potentials exceeded a predetermined threshold. Spikes from the IFUs triggered…
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Taxonomy
TopicsEEG and Brain-Computer Interfaces · Advanced Memory and Neural Computing · Neural dynamics and brain function