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Found 13 papers, 4 papers with code
Hidden Traveling Waves bind Working Memory Variables in Recurrent Neural Networks
Traveling waves are a fundamental phenomenon in the brain, playing a crucial role in short-term information storage.
Episodic Memory Theory for the Mechanistic Interpretation of Recurrent Neural Networks
Utilizing the EMT, we formulate a mathematically rigorous circuit that facilitates variable binding in these tasks.
On the Dynamics of Learning Time-Aware Behavior with Recurrent Neural Networks
In this way, we demonstrate how dynamical systems theory can provide insights into not only the learned representations of these models, but also the dynamics of the learning process itself.
Energy-based General Sequential Episodic Memory Networks at the Adiabatic Limit
We introduce a new class of General Sequential Episodic Memory Models (GSEMM) that, in the adiabatic limit, exhibit temporally changing energy surface, leading to a series of meta-stable states that are sequential episodic memories.
Signal Propagation: A Framework for Learning and Inference In a Forward Pass
To further support relevance to biological and hardware learning, we use sigprop to train continuous time neural networks with Hebbian updates, and train spiking neural networks with only the voltage or with biologically and hardware compatible surrogate functions.
Replay in Deep Learning: Current Approaches and Missing Biological Elements
Replay is the reactivation of one or more neural patterns, which are similar to the activation patterns experienced during past waking experiences.
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.
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.
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.
Error Forward-Propagation: Reusing Feedforward Connections to Propagate Errors in Deep Learning
This mechanism, Error Forward-Propagation, is a plausible basis for how error feedback occurs deep in the brain independent of and yet in support of the functionality underlying intricate network architectures.
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.
Support Vector Clustering
We present a novel clustering method using the approach of support vector machines.
