Skip to content

Spiking Neural Networks (SNNs) with PyTorch where Backpropagation Engenders Spike-Timing-Dependent Plasticity (STDP)

Notifications You must be signed in to change notification settings

solanki1993/Spiking-Neural-Networks-SNNs-with-PyTorch-

Repository files navigation

Spiking-Neural-Networks-SNNs-with-PyTorch-

Spiking Neural Networks (SNNs) with PyTorch where Backpropagation Engenders Spike-Timing-Dependent Plasticity (STDP).

This project is towards bridging the gap between deep learning and the human brain.

Hebbian learning naturally takes place during the backpropagation of SNNs. Backpropagation in Spiking Neural Networks (SNNs) engenders Spike-Timing-Dependent Plasticity (STDP)-like Hebbian learning behavior.

Spike-timing-dependent plasticity Spike-timing-dependent plasticity (STDP) is a biological process that adjusts the strength of connections between neurons in the brain. The process adjusts the connection strengths based on the relative timing of a particular neuron's output and input action potentials (or spikes). The STDP process partially explains the activity-dependent development of nervous systems, especially with regard to long-term potentiation and long-term depression.

Hebbian theory Hebbian theory is a neuroscientific theory claiming that an increase in synaptic efficacy arises from a presynaptic cell's repeated and persistent stimulation of a postsynaptic cell. It is an attempt to explain synaptic plasticity, the adaptation of brain neurons during the learning process. It was introduced by Donald Hebb in his 1949 book The Organization of Behavior.[1] The theory is also called Hebb's rule, Hebb's postulate, and cell assembly theory. Hebb states it as follows:

Let us assume that the persistence or repetition of a reverberatory activity (or "trace") tends to induce lasting cellular changes that add to its stability.[…] When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A's efficiency, as one of the cells firing B, is increased.[1]

The theory is often summarized as "Cells that fire together wire together."[2] This summary, however, should not be taken too literally. Hebb emphasized that cell A needs to "take part in firing" cell B, and such causality can occur only if cell A fires just before, not at the same time as, cell B. This important aspect of causation in Hebb's work foreshadowed what is now known about spike-timing-dependent plasticity, which requires temporal precedence.[3] [...]

About

Spiking Neural Networks (SNNs) with PyTorch where Backpropagation Engenders Spike-Timing-Dependent Plasticity (STDP)

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published