5/8/2023 0 Comments Train simulator demo chipThe most commonly used model for an SNN neuron is the Leaky Integrate-and-fire threshold model. Threshold models generate an impulse at a certain thresholdĪlthough all these methods try to describe biological neurons, the devil is in detail, so SNN neurons built based on these models might slightly differ. Conductance-based models describe how action potentials in neurons are initiated and propagatedĢ. There are two basic groups of methods used to model an SNN neuron. SNN neurons are actually built on the mathematical descriptions of biological neurons. Over time the value of the neuron will smoothly return to its average. Thus, the neuron will experience the analog of a biological neuron’s refractory period. After this, the value of the neuron will instantly drop below its average.If the value in a neuron exceeds some threshold, the neuron will send a single impulse to each downstream neuron connected to the initial one.The value in a neuron can change based on the mathematical model of a neuron, for example, if a neuron receives a spike from the upstream neuron, the value might increase or decrease.At every moment of time each neuron has some value that is analogous to the electrical potential of biological neurons.SNN receives a series of spikes as input and produces a series of spikes as the output (a series of spikes is usually referred to as spike trains). This is why instead of working with continuously changing in time values used in ANN, SNN operates with discrete events that occur at certain points of time. SNN tries to more closely mimic a biological neural network. The key difference between a traditional ANN and SNN is the information propagation approach.
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