Learning in Silicon: a Neuromorphic Model of the Hippocampus

LEARNING IN SILICON: A NEUROMORPHIC MODEL OF THE HIPPOCAMPUS John Vernon Arthur Supervisor: Kwabena Boahen The human brain is the most complex computing structure in the known universe; it excels at many tasks that digital computers perform poorly, such as learning input patterns and later retreiving themwith only a part of the original patterns as input, realizing associative memory. Our brains perform these feats rapidly and with unmatched energy efficiency, using only about 10W, far less than a typical light bulb. To explore neurobiological processing, neuromorphic engineers use existing silicon technology to duplicate neural structure and function down to the level of ion channels, efficiently morphing brain-like computation into mixed analog and digital integrated circuits. In this dissertation, we present a neuromorphic model of the hippocampus, a brain region critical in associative memory. We model hippocampal rhythmicity for the first time in a neuromorphic model by developing a new class of silicon neurons that synchronize by using shunting inhibition (conductance-based) with a synaptic rise-time. Synaptic rise-time promotes synchrony by delaying the effect of inhibition, providing an opportune period for neurons to spike together. Shunting inhibition, through its voltage dependence, inhibits neurons that spike out of phase more strongly (delaying the spike further), pushing them into phase (in the next cycle). In addition, we use these neurons to implement associative memory in a recurrent network that uses binary-weighted synpases with spike timing-dependent plasticity (STDP) to