sparse-gradient-plasticity

You can apply sparse gradient plasticity to spiking neural networks by using a lightweight numpy implementation that includes initialization, a sparsity mask, a forward method, and an update method to process pre- and post-synaptic signals.

Sparse gradient plasticity is a learning rule that applies truly sparse, general gradient-based updates to synaptic weights during online learning. It works by using a sparsity mask to constrain which connections are updated, avoiding full dense gradient calculations.

Yes, you can use numpy for spiking neural network online adaptation tasks because the implementation is a lightweight numpy-based module. You initialize it with pre-neuron, post-neuron, and sparsity parameters, then run an online update loop with synaptic signals.

No, you do not need to manually derive gradients for online learning experiments. This sparse gradient plasticity rule handles general gradient-based synaptic updates automatically, letting you focus on the online adaptation task itself.

The best way to start plasticity research with sparse connections is to initialize the SparseGradientPlasticity object with your pre-synaptic and post-synaptic neuron counts plus desired sparsity, then run an online update loop feeding your signals to the forward and update methods.