Title :
Synchrony in Silicon: The Gamma Rhythm
Author :
Arthur, John V. ; Boahen, Kwabena A.
Author_Institution :
Stanford Univ., Stanford
Abstract :
In this paper, we present a network of silicon in-terneurons that synchronize in the gamma frequency range (20-80 Hz). The gamma rhythm strongly influences neuronal spike timing within many brain regions, potentially playing a crucial role in computation. Yet it has largely been ignored in neuromorphic systems, which use mixed analog and digital circuits to model neurobiology in silicon. Our neurons 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 interneu-rons to spike together. Shunting inhibition, through its voltage dependence, inhibits interneurons that spike out of phase more strongly (delaying the spike further), pushing them into phase (in the next cycle). We characterize the interneuron, which consists of soma (cell body) and synapse circuits, fabricated in a 0.25- mum complementary metal-oxide-semiconductor (CMOS). Further, we show that synchronized interneurons (population of 256) spike with a period that is proportional to the synaptic rise time. We use these interneurons to entrain model excitatory principal neurons and to implement a form of object binding.
Keywords :
CMOS analogue integrated circuits; mixed analogue-digital integrated circuits; CMOS; complementary metal-oxide-semiconductor; gamma rhythm; mixed analog-digital circuits; neuromorphic systems; object binding; principal neurons; silicon neurobiology; synapse circuits; Binding; conductance-based neuron circuit; delay model of synchrony; inhibitory interneuron; neuromorphic engineering; shunting inhibition; synaptic rise time; Action Potentials; Animals; Cerebral Cortex; Computer Simulation; Cortical Synchronization; Dendrites; Electronics, Medical; Electrophysiology; Excitatory Postsynaptic Potentials; Humans; Interneurons; Models, Neurological; Neural Inhibition; Neural Networks (Computer); Neural Pathways; Neuronal Plasticity; Procainamide; Pyramidal Cells; Silicon; Synapses;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2007.900238