arXiv Machine Learning
Perforated Backpropagation: A Neuroscience Inspired Extension to Artificial Neural Networks
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arXiv Machine Learning
Jul 17
NeuronSoup: Evolving Asynchronous, Shared-Neuron Temporal Graphs without Backpropagation
arXiv:2607. 15217v1 Announce Type: cross Abstract: We present NeuronSoup, a neural computation architecture that replaces synchronous layer-by-layer processing with asynchronous, delay-mediated signal propagation through a pool of shared neurons.
arXiv AI
Jun 16
No One-Size-Fits-All Neurons: Task-based Neurons for Artificial Neural Networks
arXiv:2405. 02369v2 Announce Type: replace-cross Abstract: In the past decade, many successful networks are on novel architectures, which almost exclusively use the same type of neurons.
arXiv AI
Aug 10
Learning in Deep Networks under Dale's Constraint
arXiv:2608. 06963v1 Announce Type: new Abstract: Biologically plausible learning models aim to explain how neural circuits can implement effective learning under the constraints of real neurons.
arXiv AI
6d ago
Purin: A Biology-inspired Mechanism for Artificial Neural Networks
Purin is a biology-inspired mechanism for artificial neural networks that adds synaptic efficacy modulation to conventional convolutional neural networks. It introduces a time‑interval‑based abstraction for neural activities, allowing short‑ and long‑term synaptic efficacy changes without discrete time‑steps. Experiments on AlexNet, VGG11, and GoogLeNet show that Purin improves classification accuracies across evaluated datasets.
arXiv AI
Aug 6
NeuMoSync: End-to-End Neuromodulatory Control for Plasticity and Adaptability in Continual Learning
arXiv:2608. 04358v1 Announce Type: new Abstract: Continual learning (CL) requires models to learn tasks sequentially, yet deep neural networks often suffer from plasticity loss and poor knowledge transfer, which can impede their long-term adaptability.
arXiv AI
Jun 30
Globally Optimal Training of Spiking Neural Networks via Parameter Reconstruction
arXiv:2605. 08022v2 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs) have been proposed as biologically plausible and energy-efficient alternatives to conventional Artificial Neural Networks (ANNs).