A neuromime system for neural circuit analysis

A neuromime in VLSI

Using conventional very large scale integration (VLSI) technology, a flexible and comprehensive neuromime circuit has been implemented in silicon for the purpose of modeling nerve networks from living organisms. Based on the "integrate-and-fire" model of neuronal threshold, the circuit was fabricated in two-micron CMOS with double-level metal. It occupies 0.6 square millimeters of die area, and requires only a few passive biasing components off-chip. The neuromime circuit offers many continuously variable parameters, including excitatory and inhibitory sensitivity and persistence, refractory duration and strength, and overall speed of operation. The circuit offers free and continuous access to waveforms for presynaptic membrane potential, postsynaptic membrane potential, and threshold potential. As such, it is amenable to many secondary behavioral characteristics, such as postinhibitory rebound, fatigue, facilitation, and accommodation. Being power-efficient, compact, and noise immune, it is ideal for assembly into networks and interfacing to biological counterparts.

A parametric examination of VLSI-based neuronal models of cyclic and reciprocal inhibition

As a prelude to a silicon implementation of a live locomotory network, the phenomena of tonic excitation in single cells, reciprocal inhibition in pairs of cells, and recurrent cyclic inhibition in rings of five cells were recreated and subjected to parametric tests of oscillatory range and stability. Various networks were constructed from comprehensive very large scale integration (VLSI)-based artificial neurons and their parametric stability with respect to cellular threshold, refraction, and synaptic weight observed. Circuit tests demonstrated that all three oscillator topologies operated over a broad range of cellular and network frequencies. It was also noted that cells of reciprocal oscillators must possess some measure of short-term synaptic plasticity while those in the cyclically inhibited networks did not. This suggests that the two oscillator types utilize different temporal mechanisms. In parametric tests, tonic cells were sensitive to threshold, refraction, and synaptic weight. Cyclic networks were found to be sensitive to cell threshold, yet less so to refraction. Conversely, the reciprocal circuits were found to be sensitive to refraction, yet less so to cell threshold. This complementary relationship suggests a stability advantage for biological oscillatory networks that incorporate both types.

A synaptic modification algorithm in consideration of the generation of rhythmic oscillation in a ring neural network

In consideration of the generation of bursts of nerve impulses (that is, rhythmic oscillation in impulse density) in the ring neural network, a synaptic modification algorithm is newly proposed. Rhythmic oscillation generally occurs in the regular ring network with feedback inhibition and in fact such signals can be observed in the real nervous system. Since, however, various additional connections can cause a disturbance which easily extinguishes the rhythmic oscillation in the network, some function for maintaining the rhythmic oscillation is to be expected to exist in the synapses if such signals play an important part in the nervous system. Our preliminary investigation into the rhythmic oscillation in the regular ring network has led to the selection of the parameters, that is, the average membrane potential (AMP) and the average impulse density (AID) in the synaptic modification algorithm, where the decrease of synaptic strength is supposed to be essential. This synaptic modification algorithm using AMP and AID enables both the rhythmic oscillation and the nonoscillatory state to be dealt with in the algorithm without distinction. Simulation demonstrates cases in which the algorithm catches and holds the rhythmic oscillation in the disturbed ring network where the rhythmic oscillation was previously extinguished.