Methods in Neuronal Modeling: From synapses to networks
Methods in Neuronal Modeling: From synapses to networks
Analog VLSI and neural systems
Analog VLSI and neural systems
Multiplying with synapses and neurons
Single neuron computation
The effect of synchronized inputs at the single neuron level
Neural Computation
Neural Computation
Switched-capacitor neuromorphs with wide-range variable dynamics
IEEE Transactions on Neural Networks
An analog memory circuit for spiking silicon neurons
Neural Computation
Antidromic Spikes Drive Hebbian Learning in an Artificial Dendritic Tree
Analog Integrated Circuits and Signal Processing - Special issue on Learning on Silicon
Neuromorphic Synapses for Artificial Dendrites
Analog Integrated Circuits and Signal Processing
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A dendritic tree, as part of a silicon neuromorph, was modeled in VLSI as a multibranched, passive cable structure with multiple synaptic sites that either depolarize or hyperpolarize local “membrane patches,” thereby raising or lowering the probability of spike generation of an integrate-and-fire “soma.” As expected from previous theoretical analyses, contemporaneous synaptic activation at widely separated sites on the artificial tree resulted in near-linear summation, as did neighboring excitatory and inhibitory activations. Activation of synapses of the same type close in time and space produced local saturation of potential, resulting in spike train processing capabilities not possible with linear summation alone. The resulting sublinear synaptic summation, as well as being physiologically plausible, is sufficient for a variety of spike train processing functions. With the appropriate arrangement of synaptic inputs on its dendritic tree, a neuromorph was shown to discriminate input pulse intervals and patterns, pulse train frequencies, and detect correlation between input trains.