A generalized analog implementation of piecewise linear neuron models using CCII building blocks.

This paper presents a set of reconfigurable analog implementations of piecewise linear spiking neuron models using second generation current conveyor (CCII) building blocks. With the same topology and circuit elements, without W/L modification which is impossible after circuit fabrication, these circuits can produce different behaviors, similar to the biological neurons, both for a single neuron as well as a network of neurons just by tuning reference current and voltage sources. The models are investigated, in terms of analog implementation feasibility and costs, targeting large scale hardware implementations. Results show that, in order to gain the best performance, area and accuracy; these models can be compromised. Simulation results are presented for different neuron behaviors with CMOS 350 nm technology.

An analog implementation of biologically plausible neurons using CCII building blocks.

This study presents an analog implementation of the spiking neurons based on a piecewise-linear model. This model is a variation of the Izhikevich model, which is capable of reproducing different dynamic behaviors. The proposed circuit utilizes second generation current conveyors (CCII) building blocks. With the same topology and circuit values, this circuit can produce a wide variety of neuron behaviors just by tuning the reference current and voltage sources. In addition, since CCII can be considered as a building block for programmable analog arrays, based on the proposed circuit different neuron types can be implemented on programmable analog platforms. Simulation results are presented for different neuron behaviors with CMOS 350 nm ±1.5 V technology using HSPICE.