Jun Igarashi

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The cerebellum plays an essential role in adaptive motor control. Once we are able to build a cerebellar model that runs in realtime, which means that a computer simulation of 1 s in the simulated world completes within 1 s in the real world, the cerebellar model could be used as a realtime adaptive neural controller for physical hardware such as humanoid(More)
Real-time simulation of a biologically realistic spiking neural network is necessary for evaluation of its capacity to interact with real environments. However, the real-time simulation of such a neural network is difficult due to its high computational costs that arise from two factors: (1) vast network size and (2) the complicated dynamics of biologically(More)
Taste buds endure extreme changes in temperature, pH, osmolarity, so on. Even though taste bud cells are replaced in a short span, they contribute to consistent taste reception. Each taste bud consists of about 50 cells whose networks are assumed to process taste information, at least preliminarily. In this article, we describe a neural network model(More)
We aim to investigate the precise mechanisms underlying Parkinsonian symptoms by large-scale simulation of realistic neural network models including the basal ganglia (BG), thalamus, the motor cortex and spinal cord. Parkinson 's disease is a mid-or late-age degenerative disorder of the central nervous system. The symptoms include akinesia, resting tremor,(More)
In this paper, we present analog VLSI implementation of a resonate-and-fire neuron (RFN) model, and then consider noise effects on its performance of signal detection. The RFN circuit is a silicon spiking neuron that has second-order membrane dynamics and exhibits dynamic behavior, such as fast subthreshold oscillation, coincidence detection, frequency(More)
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