Jun Igarashi

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Sequential motor behavior requires a progression of discrete preparation and execution states. However, the organization of state-dependent activity in neuronal ensembles of motor cortex is poorly understood. Here, we recorded neuronal spiking and local field potential activity from rat motor cortex during reward-motivated movement and observed robust(More)
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)
Brain-scale networks exhibit a breathtaking heterogeneity in the dynamical properties and parameters of their constituents. At cellular resolution, the entities of theory are neurons and synapses and over the past decade researchers have learned to manage the heterogeneity of neurons and synapses with efficient data structures. Already early parallel(More)
We investigated successive firing of the stellate cells within a theta cycle, which replicates the phase coding of place information, using a network model of the entorhinal cortex layer II with loop connections. Layer II of the entorhinal cortex (ECII) sends signals to the hippocampus, and the hippocampus sends signals back to layer V of the entorhinal(More)
NEST is a widely used tool to simulate biological spiking neural networks. Here we explain the improvements, guided by a mathematical model of memory consumption, that enable us to exploit for the first time the computational power of the K supercomputer for neuroscience. Multi-threaded components for wiring and simulation combine 8 cores per MPI process to(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)
Spike-timing-dependent synaptic plasticity (STDP) is a simple and effective learning rule for sequence learning. However, synapses being subject to STDP rules are readily influenced in noisy circumstances because synaptic conductances are modified by pre- and postsynaptic spikes elicited within a few tens of milliseconds, regardless of whether those spikes(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)
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 implemented a large-scale cerebel-lar cortical model composed of more than 100,000 spiking neuron units on a Graphics Processing Unit (GPU). We carried out computer simulations of the model in realtime. We adopted the model to online learning of timing for a humanoid robot. 1 Introduction A Graphics Processing Unit (GPU) is a hardware accelerator for(More)