In this paper, we define a wireless sensor network with multiple types of sensors as a wireless heterogeneous sensor network (WHSN), and propose an efficient query dissemination scheme (EDT) in the WHSN. The EDT based on total dominant pruning can forward queries to only the nodes with data requested by the user, thereby reducing unnecessary packet transmission. We show that the EDT is suitable for the WHSN environment through a variety of simulations.

The maturation of inhibitory transmission through γ-aminobutyric acid (GABA) is required to induce ocular dominance (OD) plasticity in the visual cortex. However, only circuits that are mediated by specific GABAA receptors can selectively elicit OD plasticity, implying a role of local circuits involved in GABA inhibition in this process. In this study, in order to theoretically examine the effects of such local pathways associated with cortical inhibition on the induction of OD plasticity, we compared synaptic modification dynamics regulated by feedforward inhibition and those regulated by feedback inhibition. Feedforward inhibition facilitated competitive interactions between different groups of inputs conveying correlated activities, which were required for the emergence of experience-dependent plasticity. Conversely, feedback inhibition suppressed competitive interactions and prevented synapses from reflecting past sensory experience. Our results suggest that the balance between feedforward and feedback inhibition regulates the timing and level of cortical plasticity by modulating competition among synapses. This result suggests an importance of activity-dependent competition in experience-dependent OD plasticity, which is in line with the results of previous experiments.

This paper introduces a new hybrid CMOS-Nano circuit for efficient implementation of spiking neurons and spike-timing dependent plasticity (STDP) rule. In our spiking neural architecture, the STDP rule has been implemented by using neuron circuits which generate two-part spikes and send them in both forward and backward directions along their axons and dendrites, simultaneously. The two-part spikes form STDP windows and also they carry temporal information relating to neuronal activities. However, to reduce power consumption, we take the circuitry of two-part spike generation out of the neuron circuit and use the regular shaped pulses, after the training has been performed. Furthermore, the performance of the rule as spike-timing correlation learning and character recognition in a two layer winner-take-all (WTA) network of integrate-and-fire neurons and memristive synapses is demonstrated as a case example.

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.

Recent physiological studies on synaptic plasticity have shown that synaptic weights change depending on fine timing of presynaptic and postsynaptic spikes. Here, we show that a phenomenon similar to stochastic resonance with respect to background noise is observed on spike-timing dependent synaptic plasticity (STDP) that can contribute to stable propagation of precisely timed spikes in a multi-layered feedforward neural network.