Search Results for "neuron"
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Showing 24 open source projects for "neuron"
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Neuron AI
The PHP Agentic Framework to build production-ready AI driven apps
Neuron AI is a PHP agentic framework for building production-ready AI applications that connect models, memory, vector databases, and tools into working agents. It is designed for developers who want to create systems such as RAG pipelines, multi-agent workflows, and business process automations without having to hand-build every integration from scratch. -
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AWS Neuron
Powering Amazon custom machine learning chips
AWS Neuron is a software development kit (SDK) for running machine learning inference using AWS Inferentia chips. It consists of a compiler, run-time, and profiling tools that enable developers to run high-performance and low latency inference using AWS Inferentia-based Amazon EC2 Inf1 instances. Using Neuron developers can easily train their machine learning models on any popular framework such as TensorFlow, PyTorch, and MXNet, and run it optimally on Amazon EC2 Inf1 instances. ... -
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Transformer Debugger
Tool for exploring and debugging transformer model behaviors
...It automatically identifies and explains the most influential components, highlights activation patterns, and maps relationships across circuits within the model. The tool includes both a React-based neuron viewer for exploring model components and a backend activation server for running inferences and serving data. -
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PowerInfer
High-speed Large Language Model Serving for Local Deployment
...This hybrid execution strategy significantly reduces memory bottlenecks and improves overall inference speed. PowerInfer incorporates specialized algorithms and sparse operators to manage neuron activation patterns and minimize data transfers between hardware components. As a result, it enables powerful language models to run on consumer hardware while achieving performance comparable to more expensive server-grade systems. -
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SpikingJelly
SpikingJelly is an open-source deep learning framework
...This makes it especially relevant for researchers interested in biologically inspired computing, event-driven processing, and energy-efficient AI systems. The framework includes neuron models, surrogate gradient training methods, encoding strategies, network components, and utilities for simulation and experimentation, allowing users to develop a wide variety of spiking architectures. It also supports integration with familiar PyTorch workflows, which lowers the barrier for machine learning practitioners who want to explore spiking approaches without abandoning mainstream tooling. -
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BertViz
BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
...The head view visualizes attention for one or more attention heads in the same layer. It is based on the excellent Tensor2Tensor visualization tool. The model view shows a bird's-eye view of attention across all layers and heads. The neuron view visualizes individual neurons in the query and key vectors and shows how they are used to compute attention. -
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FruitGPT
Chat with a real fruit fly brain: 165,122 neurons running on your comp
FruitGPT lets you chat with a real fruit fly brain: a simulation of all 165,122 neurons of the FlyWire connectome running on your own computer, with its activity translated into English. Edit the brain — remove sections, silence neuron types, cut or strengthen wires, turn the dials — and see how its answers change. Runs offline on Windows, macOS and Linux. -
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Automated Interpretability
Code for Language models can explain neurons in language models paper
The automated-interpretability repository implements tools and pipelines for automatically generating, simulating, and scoring explanations of neuron (or latent feature) behavior in neural networks. Instead of relying purely on manual, ad hoc interpretability probing, this repo aims to scale interpretability by using algorithmic methods that produce candidate explanations and assess their quality. It includes a “neuron explainer” component that, given a target neuron or latent feature, proposes natural language explanations or heuristics (e.g. ... -
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snntorch
Deep and online learning with spiking neural networks in Python
...This allows researchers to train spiking neural models using familiar deep learning workflows while taking advantage of GPU acceleration and automatic differentiation. snnTorch provides implementations of common spiking neuron models, surrogate gradient training methods, and utilities for handling temporal neural dynamics. Because spiking neural networks operate over time and encode information through spike timing, the library includes tools for simulating temporal behavior. -
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Ecco
Explain, analyze, and visualize NLP language models
Ecco is an interpretability tool for transformers that helps visualize and analyze how language models generate text, making model behavior more transparent. -
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CNN Explainer
Learning Convolutional Neural Networks with Interactive Visualization
...Let’s break down a CNN into its basic building blocks. A tensor can be thought of as an n-dimensional matrix. In the CNN above, tensors will be 3-dimensional with the exception of the output layer. A neuron can be thought of as a function that takes in multiple inputs and yields a single output. The outputs of neurons are represented above as the red → blue activation maps. -
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iqr is a simulation software to graphically design and control large-scale neuronal models. Simulations in iqr can control real-world devices in real-time. iqr can be extended by new neuron, and synapse types, and custom interfaces to hardware.
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Multidimensional Neural Network
Multidimensional Neural Network
In Fully Connected Backpropagation Neural Networks, with many layers and many neurons in layers there is problem known as Gradient Vanishing Problem. Solution to lower its magnitude is to use Not Fully Connected Neural Network, when that is the case than with which neurons from previous layer neuron is connected has to be considered. The simplest solution would be to use Cartesian Coordinate System, and treat layers as one dimensional lines or two dimensional rectangles or three, four, five ... dimensional cuboids. In that model each neuron in layer is connected to neurons in its surrounding in previous layer. -
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Dynamic Routing Between Capsules
A PyTorch implementation of the NIPS 2017 paper
Dynamic Routing Between Capsules is a PyTorch implementation of the Capsule Network architecture originally proposed to address limitations in traditional convolutional neural networks. Capsule networks aim to improve how neural models represent spatial hierarchies and relationships between objects within images. Instead of scalar neuron activations, capsules output vectors that encode both the presence of features and their spatial properties such as orientation or pose. The repository implements the dynamic routing algorithm between capsules, which allows lower-level features to route their outputs to higher-level structures that best represent the detected patterns. ... -
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Lerning Process Hypercube Neuron
Visualization of lerning process of hypercube neuron with two inputs.
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bnns
Research tool for interactive training of artificial neural networks.
BNNS is a research tool for interactive training of artificial neural networks based on the Response Function Plots visualization method. It enables users to simulate, visualize and interact in the learning process of a Multi-Layer Perceptron on tasks which have a 2D character. Tasks like the famous two-spirals task or classification of satellite image data. -
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ViNet
fast intelligent neuronet and tools
The challenge to build near-biological, weakly layered neuron network with all time learning/growing abilities. -
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TronTool
Tuning of java neuroph.org neural nets.
...Easy watch it on the screen and move the values up or down. More or less value at the output neurons. Saving, Loading from different .nnet`s. Adding comments. Changing the visible output neuron Setting wished input neurons to get visible. A small fast and easy weight version and a continuing version, which should get involved in the neuroph project. -
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Black Hole Cortex
Sphere surface layers of visual cortex approach maximum info density
...Similarly, our imagination is the set of all possible things we can draw onto our most dense layer of visual cortex in electricity patterns. Bigger layers have more neurons to handle those possibilities. A Black Hole Cortex is a kind of visual cortex that has density of neuron layers similar to density at various radius from a black hole. What we think our eyes see, the imagination, is the densest and smallest layer. SphereSurfaces outside it recursively have more neurons, more surface area, but less density since it has to eventually dimension-reduce to high level ideas, like there are 10000 Wikipedia page names that cover most parts of the world. ... -
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Neuron
Hypercube Neuron.
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This is a C++ Neural Network controller developed under Linux. This controller abstracts a reletionship between inputs and outputs. The data used are in text format. New neuron types are needed and a graphic interface. www.unipd.it/~akela2/project
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Amygdala is a C++ spiking neural network library. It includes several neuron models, SMP support and facilities for developing SNNs with genetic algorithms. Support for running Amygdala neural networks on workstation clusters and MPPs is also under way
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BPN++ is an easy-to-use C++ implementation of a backpropagation neuron net. This library should make it easy to equip your programms with the abilty to learn.
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