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Found 8 papers, 3 papers with code
Control flow in active inference systems
Living systems face both environmental complexity and limited access to free-energy resources.
Memory via Temporal Delays in weightless Spiking Neural Network
A common view in the neuroscience community is that memory is encoded in the connection strength between neurons.
Reinforcement learning with a network of spiking agents
Neuroscientific theory suggests that dopaminergic neurons broadcast global reward prediction errors to large areas of the brain influencing the synaptic plasticity of the neurons in those regions.
Lattice Map Spiking Neural Networks (LM-SNNs) for Clustering and Classifying Image Data
Spiking neural networks (SNNs) with a lattice architecture are introduced in this work, combining several desirable properties of SNNs and self-organized maps (SOMs).
Locally Connected Spiking Neural Networks for Unsupervised Feature Learning
In recent years, Spiking Neural Networks (SNNs) have demonstrated great successes in completing various Machine Learning tasks.
Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI games
Previous studies in image classification domain demonstrated that standard NNs (with ReLU nonlinearity) trained using supervised learning can be converted to SNNs with negligible deterioration in performance.
Unsupervised Learning with Self-Organizing Spiking Neural Networks
We present a system comprising a hybridization of self-organized map (SOM) properties with spiking neural networks (SNNs) that retain many of the features of SOMs.
BindsNET: A machine learning-oriented spiking neural networks library in Python
In this paper, we describe a new Python package for the simulation of spiking neural networks, specifically geared towards machine learning and reinforcement learning.
