cazala/synaptic

★ 6,921JavaScriptAudience · developerComplexity · 3/5Setup · easy

Mindmap

mindmap
  root((Synaptic))
    What it does
      Neural network builder
      Browser and Node.js
      Custom architectures
    Network types
      Multilayer perceptron
      LSTM
      Hopfield network
    Training
      Trainer class
      XOR benchmark
      Sequence recall test
    Use cases
      Browser ML demos
      Sequence learning
      Image filter learning

mindmap root((Synaptic)) What it does Neural network builder Browser and Node.js Custom architectures Network types Multilayer perceptron LSTM Hopfield network Training Trainer class XOR benchmark Sequence recall test Use cases Browser ML demos Sequence learning Image filter learning

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Things people build with this

USE CASE 1

Build a custom neural network with any architecture in JavaScript and train it in the browser without any Python or server setup.

USE CASE 2

Use the built-in LSTM template to train a network that learns from sequences, like predicting the next word in text.

USE CASE 3

Run the included XOR training example as a sanity check that your network wiring and training loop are set up correctly.

USE CASE 4

Embed a trained neural network in a web page to classify user input or apply learned image filters client-side.

Tech stack

JavaScriptNode.js

Getting it running

Difficulty · easy Time to first run · 30min

No explicit license terms were stated in the explanation, check the repository before using commercially.

In plain English

Synaptic is a JavaScript library for building and training neural networks, usable both in a browser and in Node.js. A neural network is a type of software that learns patterns from examples rather than following explicit programmed rules. Synaptic lets developers create these networks by connecting together layers of artificial neurons, then training the result on data until it can recognize patterns or make predictions. The library is described as architecture-free, meaning it does not lock you into a specific network shape. You can wire neurons and layers together however you choose, building standard designs like a multilayer perceptron (a basic feedforward network) or more complex recurrent designs that can retain memory across a sequence of inputs. For common architectures, the library includes ready-made templates so you do not need to build from scratch: multilayer perceptrons, LSTM (long short-term memory) networks for sequence learning, liquid state machines, and Hopfield networks are all available. Training is handled by a built-in Trainer class. The library includes several standard test tasks, such as learning the XOR logic operation (a classic first test for neural networks), a sequence recall task, and an Embedded Reber Grammar test, which lets you measure and compare how well different network designs learn. The underlying training algorithm is based on a published academic paper on generalized training for second-order recurrent networks. The README links to interactive browser demos, including a version that learns to apply image filters, a self-organizing map, and a network that reads from Wikipedia. Installation is through npm for Node.js or a CDN script tag for browser use. The project began in Buenos Aires, Argentina, and accepts code contributions. A discussion of a major version 2 rewrite was open in the repository at the time the README was written.

Copy-paste prompts

Prompt 1

Using Synaptic in Node.js, create a multilayer perceptron, train it on the XOR problem, and print the accuracy after training.

Prompt 2

Help me build a Synaptic LSTM network that learns a short character sequence and predicts the next character when I give it a prompt.

Prompt 3

I want to train a Synaptic neural network in the browser and visualize its output in real time on a canvas. Show me the HTML and JavaScript setup.

Prompt 4

Explain what the Trainer class in Synaptic does and show me how to use it to measure whether my custom network architecture has finished learning a task.

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