The hardware implementation of an artificial neuron is the key problem design neuromorphic chips which are new promising architectural solutions for massively parallel computing. In this paper analog circuit presented to be used as a building element spiking networks. performed at transistor level based on Leaky Integrate-and-Fire model. simulated using EDA tool verify design. Signal waveforms ...

We describe a set of preliminary experiments to evolve spiking neural controllers for a vision-based mobile robot. All the evolutionary experiments are carried out on physical robots without human intervention. After discussing how to implement and interface these neurons with a physical robot, we show that evolution finds relatively quickly functional spiking controllers capable of navigating ...

Overview: We model energy constraints in a network of spiking neurons, while exploring general questions of resource limitation on network function abstractly. Background: Metabolic states like dietary ketosis or hypoglycemia have a large impact on brain function and disease outcomes. Glia provide metabolic support for neurons, among other functions. Yet, in computational models of glia-neuron ...

This brief designs an artificial neuron circuit consisting of inductor and N-type locally active memristor (LAM). When the presented is excited by DC or sine voltage signal, it can pick up most significant features a biological neuron: all-or-none, spiking oscillation, chaotic behaviours. Local activity edge chaos theories are applied to analyze electrical characteristics circuit. Furthermore, ...

Different varieties of artificial neural networks have proved their power in several pattern recognition problems, particularly feedforward neural networks. Nevertheless, these kinds of neural networks require of several neurons and layers in order to success when they are applied to solve non-linear problems. In this paper is shown how a spiking neuron can be applied to solve different linear ...

We describe an improved spiking silicon neuron (SN) [6] that approximates the dynamics of ionic currents of a real nerve cell. The improved version has less circuitry and fewer parameters than previous circuits thereby improving the spiking characteristics. We describe the differential equations governing the revised circuits and use them to explain the spiking properties of the SN. We also des...

Analysis of the neural code for sensory-motor latency in smooth pursuit eye movements reveals general principles of neural variation and the specific origin of motor latency. The trial-by-trial variation in neural latency in MT comprises a shared component expressed as neuron-neuron latency correlations and an independent component that is local to each neuron. The independent component arises ...

Spike sequences recorded from four cortical areas of an awake behaving monkey were examined to explore characteristics that vary among neurons. We found that a measure of the local variation of interspike intervals, L(V), is nearly the same for every spike sequence for any given neuron, while it varies significantly among neurons. The distributions of L(V) values for neuron ensembles in three o...

The integration of excitatory inputs in dendrites is non-linear: multiple excitatory inputs can produce a local depolarization departing from the arithmetic sum of each input’s response taken separately. If this depolarization is bigger than the arithmetic sum, the dendrite is spiking; if the depolarization is smaller, the dendrite is saturating. Decomposing a dendritic tree into independent de...