title: Spatio-temporal electrical stimuli shape behavior of an embodied cortical network in a goal-directed learning task.
abstract: We developed an adaptive training algorithm, whereby an in vitro neocortical network learned to modulate its dynamics and achieve pre-determined activity states within tens of minutes through the application of patterned training stimuli using a multi-electrode array. A priori knowledge of functional connectivity was not necessary. Instead, effective training sequences were continuously discovered and refined based on real-time feedback of performance. The short-term neural dynamics in response to training became engraved in the network, requiring progressively fewer training stimuli to achieve successful behavior in a movement task. After 2 h of training, plasticity remained significantly greater than the baseline for 80 min (p-value<0.01). Interestingly, a given sequence of effective training stimuli did not induce significant plasticity (p-value=0.82) or desired behavior, when replayed to the network and no longer contingent on feedback. Our results encourage an in vivo investigation of how targeted multi-site artificial stimulation of the brain, contingent on the activity of the body or even of the brain itself could treat neurological disorders by gradually shaping functional connectivity.
authors: Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan.
authors: Douglas J
authors: DJ
authors: Bakkum
authors: Zenas C
authors: ZC
authors: Chao
authors: Steve M
authors: SM
authors: Potter
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000818:Animals; D032701:Biomimetics / Q000379:methods*; D002478:Cells, Cultured; D002540:Cerebral Cortex / Q000502:physiology*; D004558:Electric Stimulation / Q000379:methods*; D006040:Goals; D007858:Learning / Q000502:physiology*; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D051381:Rats; D013647:Task Performance and Analysis