SpikingJelly使用助手-SNN Development Framework

Example

The assistant can guide users in converting a pre-trained CNN for MNIST classification into an SNN. This involves using the `ann2snn` module and selecting appropriate conversion methods, such as MaxNorm or RobustNorm, to ensure the SNN's performance closely matches the original ANN.

Scenario

A researcher wants to deploy a power-efficient version of a CNN on a neuromorphic chip. The assistant provides step-by-step guidance on converting the ANN to an SNN, choosing conversion parameters, and validating the SNN's accuracy on the MNIST dataset.

Example

The assistant offers detailed explanations and code examples for creating SNNs using SpikingJelly's neuron models and layers. It could illustrate how to build a network with Convolutional and Fully Connected layers for Fashion-MNIST classification.

Scenario

A student is working on a project to classify fashion images using SNNs. The assistant helps them design the network architecture, select neuron models like LIF neurons, and implement the network using SpikingJelly.

Example

It provides instructions on simulating SNNs over multiple time steps and employing learning algorithms like STDP for training. An example could be training an SNN on the DVS Gesture dataset using event-based data.

Scenario

An engineer wants to create a gesture recognition system with an SNN. The assistant explains how to process event-based data, simulate the SNN for real-time gesture recognition, and apply STDP for unsupervised learning.