A two-variable silicon neuron circuit based on the Izhikevich model

Abstract

A two-variable silicon neuron circuit based on the Izhikevich model which is diffi cult in simulations using digital computers. In addition, it can be compact if it is implemented into an analog very large-scale integrated (aVLSI) circuit. For those reasons, it is expected that the silicon neuron will be applied for real-time systems such as hybrid systems, medical devices, and robots.Silicon neurons have been developed by two different basic approaches. The fi rst is to reproduce only signifi cant neuronal behavior, which is called the phenomenological method. Silicon neuron circuits designed by this method 7 tend to be simple, but can only reproduce a few fi ring patterns because of their oversimplifi ed dynamics. The second approach is to reproduce the neuronal dynamics precisely by solving the ionic conductance model, which is called the conductance-based method. Silicon neurons designed by this method 8 can reproduce various fi ring patterns, but their circuitry is large and complex.Recently, Kohno and Aihara 9 proposed another method, the mathematical-structure-based approach, which allows us to design simple circuits with rich neuronal dynamics by reproducing the mathematical structure of the original models. By using this method, Nagamatsu et al. 10 proposed a silicon neuron circuit (in this article, we will call it the "previous circuit"), which reproduces the mathematical structure of the Izhikevich model. 11 This silicon neuron circuit reconstructs the dynamics of the Izhikevich model with a simple circuit whose average power consumption is about 15 nW. However, in the circuit that implements the jump of state in the Izhikevich model (we call this circuit the "reset circuit"), the voltage variation is relatively large in comparison to the other parts of the circuit operated in the sub-threshold region. Furthermore, the input current that triggers the spiking of the silicon neuron needs to be small, which is likely to be diffi cult to produce.Here, we propose an improved circuit with a new reset circuit in which the voltage variation is about 0.4 V, which is lower than that of the previous circuit by about 2.25 V. In addition, we scaled up the input current by altering part of the circuit. These modifi cations are expected to lower the diffi culty of aVLSI implementation in this circuit. AbstractThe silicon neuron is an analog electronic circuit that reproduces the dynamics of a neuron. It is a useful element for artifi cial neural networks that work in real time. Silicon neuron circuits have to be simple, and at the same time they must be able to realize rich neuronal dynamics in order to reproduce the various activities of neural networks with compact, low-power consumption, and an easy-toconfi gure circuit. We have been developing a silicon neuron circuit based on the Izhikevich model, which has rich dynamics in spite of its simplicity. In our previous work, we proposed a simple silicon neuron circuit with low power consumption by reconstructing the mathematical structure in the Izhikevich model usin...

Silicon neuron design based on phase reduction analysis

“…In accordance with the criteria, as a case study, we demonstrate how to tune circuit parameters to obtain a desirable PRC of a resonate-and-fire neuron (RFN) circuit as an SiN [3]. Based on the results, we consider a possible extension of our framework to design a class of the generalized integrate-and-fire neuron (GIFN) circuits [4]- [6].…”

Dynamical System Approach for Edge Detection Using Coupled FitzHugh–Nagumo Neurons

“…Constructing such a network using integrated circuit technology is a component of the hardware design procedure. The design of the single element in equation (6) is addressed by the extensively researched topic of "silicon neurons" [4], [29], and the coupling structure has also been thoroughly studied by the "Cellular Neural Network" [30], [15] community. We provide a simplified version of the circuit in equation (28) by eliminating the couplings of the membrane potentials from the adjacent neurons.…”

“…For each noisy image, the value of the threshold η in equation (29) is set at three different values -0.05, 0.15 and 0.25. All of the measures are obtained by averaging over 100 simulations, and the results are presented in Fig.…”

Dynamical systems design of silicon neurons using phase reduction method

“…Silicon neurons (SiNs) [2,[14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] are one of the most fundamental elements (building blocks) constituting neuromorphic systems for spike-based computation as well as synaptic circuitries. Most of the conventional design approaches for SiNs are based on two major principles.…”