In this paper, we propose an analog CMOS circuit which achieves spiking neural networks with spike-timing dependent synaptic plasticity (STDP). In particular, we propose a STDP circuit with symmetric function for the first time, and also we demonstrate associative memory operation in a Hopfield-type feedback network with STDP learning. In our spiking neuron model, analog information expressing processing results is given by the relative timing of spike firing events. It is well known that a biological neuron changes its synaptic weights by STDP, which provides learning rules depending on relative timing between asynchronous spikes. Therefore, STDP can be used for spiking neural systems with learning function. The measurement results of fabricated chips using TSMC 0.25 µm CMOS process technology demonstrate that our spiking neuron circuit can construct feedback networks and update synaptic weights based on relative timing between asynchronous spikes by a symmetric or an asymmetric STDP circuits.

Subjects' episodic memory performance is not simply reflected by eye movements. We use a 'theta phase coding' model of the hippocampus to predict subjects' memory performance from their eye movements. Results demonstrate the ability of the model to predict subjects' memory performance. These studies provide a novel approach to computational modeling in the human-machine interface.

So far, neuronal dendrites have been characterized as electrically passive cables. However, recent physiological findings have revealed complex dynamics due to active conductances distributed over dendrites. In particular, the voltage-gated calcium and calcium-activated conductances are essential for producing diverse neuronal dynamics and synaptic plasticity. In this paper, we investigate the functional significance of the dendritic calcium-activated dynamics by computer simulations. First, the dendritic calcium-activated responses are modeled in a discrete compartmental form based on the physiological findings. Second, the basic stimulus-response characteristics of the single compartment dendrite model are investigated. The model is shown to reproduce the neuronal responses qualitatively. Third, the spatio-temporal dynamics of the dendrite shafts are modeled by longitudinally connecting 10 single compartments with coupling constants which are responsible for the dendrite thickness. The thick dendrite models, corresponding to proximal dendrites, respond in a spatially cooperative manner to a localized constant or periodic current stimulation. In contrast, the highly activated compartments are forced to be localized in the neighborhood of the stimulation-site in the fine dendrite models corresponding to distal dendrites. These results suggest that dendritic activities are spatially cooperated in a site-dependent manner.