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Spiking neural network

Spiking neural networks (SNNs) are artificial neural networks that more closely mimic natural neural networks. In addition to neuronal and synaptic state, SNNs incorporate the concept of time into their operating model. The idea is that neurons in the SNN do not fire at each propagation cycle (as it happens with typical multi-layer perceptron networks), but rather fire only when a membrane potential – an intrinsic quality of the neuron related to its membrane electrical charge – reaches a specific value. When a neuron fires, it generates a signal that travels to other neurons which, in turn, increase or decrease their potentials in accordance with this signal. Spiking neural networks (SNNs) are artificial neural networks that more closely mimic natural neural networks. In addition to neuronal and synaptic state, SNNs incorporate the concept of time into their operating model. The idea is that neurons in the SNN do not fire at each propagation cycle (as it happens with typical multi-layer perceptron networks), but rather fire only when a membrane potential – an intrinsic quality of the neuron related to its membrane electrical charge – reaches a specific value. When a neuron fires, it generates a signal that travels to other neurons which, in turn, increase or decrease their potentials in accordance with this signal. In the context of spiking neural networks, the current activation level (modeled as a differential equation) is normally considered to be the neuron's state, with incoming spikes pushing this value higher, eventually either firing or decaying. Various coding methods exist for interpreting the outgoing spike train as a real-value number, relying on either the frequency of spikes, or the interval between spikes, to encode information. Artificial neural networks are usually fully connected, receiving input from every neuron in the previous layer and signalling every neuron in the subsequent layer. Although these networks have achieved breakthroughs in many fields, they are biologically inaccurate and do not mimic the operation mechanism of neurons in the brain of a living thing. The first scientific model of a spiking neuron was proposed by Alan Lloyd Hodgkin and Andrew Huxley in 1952. This model describes how action potentials are initiated and propagated. Spikes, however, are not generally transmitted directly between neurons. Communication requires the exchange of chemical substances in the synaptic gap, called neurotransmitters. The complexity and variability of biological models have resulted in various neuron models, such as the integrate-and-fire (1907?), FitzHugh–Nagumo model (1961–1962) and Hindmarsh–Rose model (1984).

[ "Artificial neural network", "Neuron", "rank order coding", "Liquid state machine", "neuromorphic hardware", "neural engineering framework", "spike timing dependent synaptic plasticity" ]
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