The NEST simulator
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Updated
Sep 8, 2026 - C++
The NEST simulator
Reinforcement learning framework for spiking neural network actors with R-STDP for the master's thesis "Training Spiking Neural Networks with Reinforcement Learning".
A web-based GUI application for NEST simulator (legacy, moved to https://github.com/nest-desktop/nest-desktop)
Neuro-Cybernetic Protocol: versioned canonical-JSON contract connecting neural simulators and neuromorphic controllers to robots, UAVs and analysis clients over Zenoh. Rust reference, independent TypeScript validator, Python/C/C++ bindings. v0.8.0 is the latest release; HEAD is a release-blocked 1.0 candidate.
A generalizable model of spike-timing dependent plasticity for the Neural Simulation Tool (NEST).
Spiking neuronal network simulations (Python, NEST Simulator) for continuous attractor working memory networks with short-term plasticity.
Bio-inspired neuromorphic cerebellum
deNEST: A declarative interface for specifying networks and simulations in NEST
Provenance-first figure contracts for NEST neural-simulation data: strict declarative JSON in, deterministic SVG plus exact-value tables out, every figure bound to a fail-closed honesty caption. TypeScript library and offline CLI for reproducible, agent-ready scientific figures. Pre-1.0.
My master thesis "Statistical tests for connection algorithms for neural networks". Also available at https://nmbu.brage.unit.no/nmbu-xmlui/bitstream/handle/11250/189117/Hjertholm2013.pdf?sequence=1
Simulation code for simulations run in my PhD
Nest Simulator quick guides and examples, adding new model using NESTML
Statistical tests for some of the most common connection algorithms used in neural networks. Brian, PyNN, NEST and CSA are tested.
Simulation source code used in SinhaEtAl2021
Scripts used to post process data collected from my research simulations
A web documentation for NEST simulator
Analysis of the neural simulation data from NEST Simulator. The following analysis is part of solution for the semestral project for Informatics and Cognitive Science II from Faculty of Math and Physics of Charles University in Prague.
Adaptive Generalized Leaky Integrate-and-Fire Model (AGLIF) (Marasco et al., 2023)
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