• Stars
    star
    4
  • Rank 3,304,323 (Top 66 %)
  • Language
    Python
  • License
    GNU General Publi...
  • Created over 4 years ago
  • Updated over 4 years ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

Simulation code for Limbacher, T. and Legenstein, R. (2020). Emergence of Stable Synaptic Clusters on Dendrites Through Synaptic Rewiring

More Repositories

1

WeatherDiffusion

Code for "Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models" [TPAMI 2023]
Python
306
star
2

LSNN-official

Long short-term memory Spiking Neural Networks
Python
94
star
3

eligibility_propagation

Python
84
star
4

LSM

Liquid State Machines in Python and NEST
Python
49
star
5

live-plotter

Live plots with matplotlib with a simple interface
Python
44
star
6

L2L

Learning to Learn: Gradient-free Optimization framework
Python
36
star
7

spore-nest-module

Synaptic Plasticity with Online Reinforcement learning
C++
25
star
8

H-Mem

Code for Limbacher, T. and Legenstein, R. (2020). H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks
Python
13
star
9

SimManager

The Simulation Manager is a library for enabling reproducible scientific simulations.
Python
9
star
10

SparseAdversarialTraining

Code for "Training Adversarially Robust Sparse Networks via Bayesian Connectivity Sampling" [ICML 2021]
Python
8
star
11

structured_information_representation

Simulation code für Müller et al., A model for structured information representation in neural networks of the brain
Python
6
star
12

MemoryDependentComputation

Code for Limbacher, T., Özdenizci, O., & Legenstein, R. (2022). Memory-enriched computation and learning in spiking neural networks through Hebbian plasticity. arXiv preprint arXiv:2205.11276.
Python
6
star
13

OutputCodeMatching

Code for "Improving Robustness Against Stealthy Weight Bit-Flip Attacks by Output Code Matching" [CVPR 2022]
Python
5
star
14

Cognitive-Map-Learner

This repo contains an example jupyter-notebook for the CML algorithm on all different kinds of the abstract random graph tasks.
Jupyter Notebook
5
star
15

IGI-Reading-Group

IGI Reading Group
5
star
16

Spike-Frequency-Adaptation-Supports-Network-Computations

Code for: Spike Frequency Adaptation Supports Network Computations on Temporally Dispersed Information
Jupyter Notebook
5
star
17

CSNN

Python
4
star
18

SimRecorder

An high-performance library for recording and storing simulation data
Python
4
star
19

nevesim

Neural EVEnt-based SIMulator
C++
3
star
20

RobustSNNConversion

Code for "Adversarially Robust Spiking Neural Networks Through Conversion" [TMLR 2024]
Python
2
star
21

dynamic_rnn_with_gradients

Python
1
star
22

adaptation_working_memory

Spike frequency adaptation supports network computations on temporally dispersed information
Jupyter Notebook
1
star