System of signals propagation from one neuron to other represent event of very complex electrochemical mechanism. In this work, we studied the dynamics of modified FitzHugh-Nagumo multi neuron model with sinusoidal external stimulation. In this paper, the coupled model is established on the basis of spiking neuron model, and then the relation of bidirectional coupling strength of the gap juncti...

Spiking neural networks (SNNs) are considered a crucial research direction to address the “storage wall” and “power challenges faced by traditional artificial intelligence computing. However, developing SNN chips based on CMOS (complementary metal oxide semiconductor) circuits remains challenge. Although memristor process technology is best alternative synapses, it still undergoing refinement. ...

This paper presents analysis and simulation of a CMOS based cortical neuron circuit at 0.25μm technology node. The spiking and bursting patterns generated after the simulation of the circuit are studied. Also, the effect of variation of the (W/L) ratios of transistors on the spiking pattern is analyzed in this paper. The results obtained by simulation and the variations in parameters shows that...

Silicon neuron is electrical circuit that is analogous to biological neurons. Most spiking silicon neurons comprise analog circuit technology. We propose a new concept of spiking silicon neuron that is composed of only digital circuit technology. The system equations were designed by a mathematical-model-based design method that we proposed for analog silicon neurons in previous works. This all...

Spiking neural P systems with anti-spikes (ASN P systems, for short) are a variant of spiking neural P systems, which are inspired by inhibitory impulses/spikes or inhibitory synapses. The typical feature of ASN P systems is when a neuron contains both spikes and anti-spikes, the spikes and anti-spikes will immediately annihilate each other in a maximal way. In this work, we consider a restrict...

It is shown how, through the use of a single layer of spiking neurons, even more so with a single neuron, it is possible to make the classification of patterns, either a binary function as the XOR function or a database with tens of characteristics. Izhikevich model is used for modeling the behavior of the used spiking neurons. Mainly intends to exploit the use of a single neuron to perform cla...

Astrocyte cells form the largest cell population in brain and can influence neuron behavior. These provide appropriate feedback control regulating neuronal activities Central Nervous System (CNS). This paper presents a set of equations as model to describe interactions between neurons astrocyte. A VHDL–AMS-based tripartite synapse that includes pre-synaptic neuron, synaptic terminal, post-synap...

Since the properties of transistors are well matched to the properties of ionic channels in nerve membranes, Mead has advocated the use of analog VLSI for the construction of arti cial neural systems [1]. The current state of the art in realistic single-neuron designs is the silicon neuron of Mahowald and Douglas [2], which exhibits the spiking, refractory, and adaptation characteristics of cor...

The izhikevich neuron model is well known for mimicking almost all dynamics of the biological neurons like Hodgkin-Huxley neuron models with much less hardware resources. Despite its versatility and biological plausibility, izhikevich neuron model is still not suited for a large scale neural network simulation due to its complexity compared to the simpler neuron models like integrate-and-fire m...

In this paper the electronic circuit implementation of a fuzzy neuron model with a fuzzy Gupta integrator is presented. This neuron model simulates the performance and the fuzzy response of a fast-spiking biological neuron. The fuzzy neuron response is analyzed for two classical (non-fuzzy) input signals, the results are spike trains with relative and absolute refractory period and an axonal de...