Analog VLSI and neural systems
Analog VLSI and neural systems
Dynamical cell assembly hypothesis—theoretical possibility of spatio-temporal coding in the cortex
Neural Networks - 1996 Special issue: four major hypotheses in neuroscience
Pulsed neural networks
Spikes: exploring the neural code
Spikes: exploring the neural code
Analog Integrated Circuits and Signal Processing
The Handbook of Brain Theory and Neural Networks
The Handbook of Brain Theory and Neural Networks
Analogue VLSI Leaky Integrate-and-Fire Neurons and Their Use in a Sound Analysis System
Analog Integrated Circuits and Signal Processing
On the Performance of Pulsed and Spiking Neurons
Analog Integrated Circuits and Signal Processing
Analog Integrated Circuits and Signal Processing
Self-organizing dual coding based on spike-time-dependent plasticity
Neural Computation
Biophysics of Computation: Information Processing in Single Neurons (Computational Neuroscience Series)
Dynamic range and sensitivity adaptation in a silicon spiking neuron
IEEE Transactions on Neural Networks
Integrated pulse stream neural networks: results, issues, and pointers
IEEE Transactions on Neural Networks
A low-power biologically-inspired chaotic oscillator with process and temperature tolerance
Analog Integrated Circuits and Signal Processing
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We propose an asynchronous spiking chaotic neuron circuit for temporal coding networks based on coincidence detection and spatio-temporal chaotic dynamics. In the proposed circuit, we take into account the coincidence detection among input spikes, continuous-time interspike intervals, relative and absolute refractoriness, an analog internal state value, a continuous output function, an output spike generation delay, and synaptic weight. We implement the neuron circuit with a 0.5@mm CMOS semiconductor process by means of analog circuitry. Moreover, we make most of the model parameters externally controllable so that we can control the behavior of the neuron. As a consequence, by properly setting its circuit parameters, the neuron can function as either a coincidence detector or an integrator. We show the measurement results from the prototype chip. In particular, we show coincidence detection of the input spikes in a short time-window. In addition, we illustrate complex responses of the neuron circuit, providing bifurcation diagrams of the internal state value and the interspike intervals of the output spikes. The proposed neuron circuit is useful for exploring a possible spatio-temporal coding paradigm by constructing a neural network based on the coincidence detection and the spatio-temporal chaotic dynamics.