We study a reinforcement learning for temporal coding with neural network consisting of stochastic spiking neurons. In neural networks, information can be coded by characteristics of the timing of each neuronal firing, including the order of firing or the relative phase differences of firing. We derive the learning rule for this network and show that the network consisting of Hodgkin-Huxley neu...

Neural networks composed of excitable neurons with noise generate rich nonlinear dynamics with spatiotemporal structures of neuronal spikes. Among various spatiotemporal patterns of spikes, synchronous firing has been studied most extensively both with physiological experimentation and with theoretical analysis. In this paper, we consider nonlinear neurodynamics in terms of synchronous firing a...

Neurons in the ventral tegmental area (VTA) synthesize several major neurotransmitters, including dopamine (DA), GABA, and glutamate. To classify VTA single-unit neural activity from freely moving rats, we used hierarchical agglomerative clustering and probability distributions as quantitative methods. After many parameters were examined, a firing rate of 10 Hz emerged as a transition frequency...

Neural population activity often exhibits rich variability and temporal structure. This variability is thought to arise from single-neuron stochasticity, neural dynamics on short time-scales, as well as from modulations of neural firing properties on long time-scales, often referred to as “non-stationarity”. To better understand the nature of co-variability in neural circuits and their impact o...

We propose an algorithm for simultaneously estimating state transitions among neural states, the number of neural states, and nonstationary firing rates using a switching state space model (SSSM). This algorithm enables us to detect state transitions on the basis of not only the discontinuous changes of mean firing rates but also discontinuous changes in temporal profiles of firing rates, e.g.,...

We demonstrate the mathematical equivalence of two commonly used forms of firing rate model equations for neural networks. In addition, we show that what is commonly interpreted as the firing rate in one form of model may be better interpreted as a low-pass-filtered firing rate, and we point out a conductance-based firing rate model.

We propose an algorithm for simultaneously estimating state transitions among neural states, the number of neural states, and nonstationary firing rates using a switching state space model (SSSM). This algorithm enables us to detect state transitions on the basis of not only the discontinuous changes of mean firing rates but also discontinuous changes in temporal profiles of firing rates, e.g.,...