Search Results for author:
Found 11 papers, 4 papers with code
Strategy and Benchmark for Converting Deep Q-Networks to Event-Driven Spiking Neural Networks
The present work focuses on using SNNs in combination with deep reinforcement learning in ATARI games, which involves additional complexity as compared to image classification.
Reinforcement Learning with Feedback-modulated TD-STDP
Spiking neuron networks have been used successfully to solve simple reinforcement learning tasks with continuous action set applying learning rules based on spike-timing-dependent plasticity (STDP).
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.
Minibatch Processing in Spiking Neural Networks
To our knowledge, this is the first general-purpose implementation of mini-batch processing in a spiking neural networks simulator, which works with arbitrary neuron and synapse models.
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.
STDP Learning of Image Patches with Convolutional Spiking Neural Networks
Spiking neural networks are motivated from principles of neural systems and may possess unexplored advantages in the context of machine learning.
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.
Complete stability analysis of a heuristic ADP control design
This paper provides new stability results for Action-Dependent Heuristic Dynamic Programming (ADHDP), using a control algorithm that iteratively improves an internal model of the external world in the autonomous system based on its continuous interaction with the environment.
