The book of GENESIS (2nd ed.): exploring realistic neural models with the GEneral NEural SImulation System
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
A VLSI-Based Model of Azimuthal Echolocation in the Big Brown Bat
Autonomous Robots
Analog VLSI models of range-tuned neurons in the bat echolocation system
EURASIP Journal on Applied Signal Processing
Solving graph algorithms with networks of spiking neurons
IEEE Transactions on Neural Networks
A new synaptic plasticity rule for networks of spiking neurons
IEEE Transactions on Neural Networks
Simplicity and efficiency of integrate-and-fire neuron models
Neural Computation
Recognition of partially occluded and rotated images with a network of spiking neurons
IEEE Transactions on Neural Networks
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This letter introduces a biologically inspired very simple spiking neuron model. The model retains only crucial aspects of biological neurons: a network of time-delayed weighted connections to other neurons, a threshold-based generation of action potentials, action potential frequency proportional to stimulus intensity, and interneuron communication that occurs with time-varying potentials that last longer than the associated action potentials. The key difference between this model and existing spiking neuron models is its great simplicity: it is basically a collection of linear and discontinuous functions with no differential equations to solve. The model's ability to operate in a complex network was tested by using it as a basis of a network implementing a hypothetical echolocation system. The system consists of an emitter and two receivers. The outputs of the receivers are connected to a network of spiking neurons (using the proposed model) to form a detection grid that acts as a map of object locations in space. The network uses differences in the arrival times of the signals to determine the azimuthal angle of the source and time of flight to calculate the distance. The activation patterns observed indicate that for a network of spiking neurons, which uses only time delays to determine source locations, the spatial discrimination varies with the number and relative spacing of objects. These results are similar to those observed in animals that use echolocation.