Rockpool

About Rockpool

The basics

Installing Rockpool and contributing
A simple introduction to spiking neurons
Introduction to Rockpool
👋 Hello MNIST with Rockpool
Working with time series data
Standard modules
🐡 The sharp points of Rockpool 🐚

In depth

🛠 Low-level Module API
⏱ High-level TimedModule API
[𝝺] Low-level functional API
🏃🏽‍♀️ Training a Rockpool network with Jax
👩🏽‍🔬 Advanced Jax training topics
🔥 Building Rockpool modules with Torch
👩🏼‍🚒 Training a Rockpool network with Torch
How To: Configure and perform constrained optimization in Rockpool

Tutorials

∇ Gradient descent training of a rate-based recurrent network
⚡️ Training a spiking network with Jax
⚡️ Training a spiking network with Torch
🐰 Easter with Rockpool 🥚
👹 Adversarial training
🔊 Training an audio classification task using Torch 🔥
WaveSense: Training a Spiking Neural Network with Temporal Convolutions

Xylo™ inference processors

🐝 Overview of the Xylo family
🐝⚡️ Quick-start with Xylo SNN core
Training a spiking network to deploy to the Xylo digital SNN

Xylo™ Audio

🐝🎵 Introduction to Xylo-Audio
Using the analog frontend model

Xylo™ IMU

🐝💨 Introduction to Xylo-IMU
∿ The Xylo™ IMU preprocessing interface
🧑‍🏫 Specifying IMU preprocessing parameters

DYNAP-SE2 mixed-signal processor

Overview of Dynap-SE2
Quick Start with Dynap-SE2
DynapSim Neuron Model
Training a spiking network to deploy to Dynap-SE2

Advanced topics

Computational graphs in Rockpool
Graph mapping
Rockpool Parameter handling
Typehints in Rockpool
Performance benchmarks for LIF layers in Rockpool
Full API summary for Rockpool
Change log

Developer documentation

UML diagrams for Rockpool
Using the backend management system
Dynap-SE2 developer notes
Notes for developers

Rockpool

Full API summary for Rockpool
rockpool
rockpool.nn
rockpool.nn.losses
rockpool.nn.losses.mse_loss
View page source

rockpool.nn.losses.mse_loss

Classes

MSELoss()

This is a wrapper around the PyTorch MSE loss.

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