We try to improve discontinuous output change in. SpikeProp. The problem is that small variants in in- put cause significant change in output. We first show.

In this paper we improve the input-output relationship of SpikeProp network[1], one type of spiking neural networks. Though the standard SpikeProp networks ...

We try to improve discontinuous output change in SpikeProp. The problem is that small variants in input cause significant change in output.

In this paper we improve the input-output relationship of SpikeProp network[1], one type of spiking neural networks. Though the standard SpikeProp networks ...

We try to improve discontinuous output change in SpikeProp. The problem is that small variants in input cause significant change in output.

This method enables training deep convolutional SNNs directly (with input spike events) using spike-based backpropagation.

Jul 17, 2020 · With a careful choice of the pseudo derivative for handling the discontinuous dynamics of spiking neurons, one can apply BPTT also to RSNNs, and ...

The purpose of the adjustment is to narrow the difference so that the actual output spike can converge to the corresponding desired one. The other learning ...

The pooling layers are responsible for reducing the sensitivity of the output to slight input-shift and distortions and increasing the reception field for later ...

Jan 14, 2024 · We propose reference spikes as new plastic parameters for spiking neural networks. The number and timings of reference spikes are modifiable by learning rules.