Synaptic Dynamic

Synaptic dynamics research explores how the strength and function of connections between neurons change over time, impacting learning, memory, and overall brain function. Current investigations focus on understanding how diverse neuronal and synaptic properties, including heterogeneity in plasticity rules and the role of interneurons, contribute to efficient and robust learning in both biological and artificial neural networks. This includes developing novel neuromorphic architectures and algorithms that mimic these biological mechanisms, such as those incorporating neuromodulation or self-modifying synapses, to improve the efficiency and adaptability of artificial intelligence systems. Ultimately, this research aims to unravel the fundamental principles governing brain plasticity and translate these insights into more powerful and adaptable AI.

Papers

October 15, 2024

Network Representation Learning for Biophysical Neural Network Analysis
Youngmok Ha, Yongjoo Kim, Hyun Jae Jang, Seungyeon Lee, Eunji Pak
Graph Attention Computational Neuroscience Biological Neural Network Network Representation BNN Layer Synaptic Dynamic

August 4, 2024

Mamba-Spike: Enhancing the Mamba Architecture with a Spiking Front-End for Efficient Temporal Data Processing
Jiahao Qin, Feng Liu
Neuromorphic Computing Neuron Model Neuromorphic Datasets Mamba Based Standard Interface Neuromorphic Platform Synaptic Dynamic

May 27, 2024

Theories of synaptic memory consolidation and intelligent plasticity for continual learning
Friedemann Zenke, Axel Laborieux
Continual LEArning Theoretical Understanding Plasticity Rule Synaptic Plasticity Memory Consolidation Neural Plasticity Synaptic Dynamic

February 22, 2023

Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles
Biswadeep Chakraborty, Saibal Mukhopadhyay
Spiking Neural Network Theoretical Understanding Design Principle Spike Timing Dependent Plasticity Neuronal Heterogeneity Synaptic Dynamic Spike Depth Estimation

February 17, 2023

Theory of coupled neuronal-synaptic dynamics
David G. Clark, L. F. Abbott
Theoretical Understanding Hebbian Plasticity Synaptic Dynamic

December 10, 2022

Phenomenological modeling of diverse and heterogeneous synaptic dynamics at natural density
Agnes Korcsak-Gorzo, Charl Linssen, Jasper Albers, Stefan Dasbach, Renato Duarte, Susanne Kunkel, Abigail Morrison, Johanna Senk, Jonas Stapmanns, Tom Tetzlaff, Markus Diesmann, Sacha J. van Albada
Computational Neuroscience Particle Density Synaptic Plasticity Synaptic Feedback Synaptic Connectivity Synaptic Dynamic

September 21, 2022

Interneurons accelerate learning dynamics in recurrent neural networks for statistical adaptation
David Lipshutz, Cengiz Pehlevan, Dmitri B. Chklovskii
Recurrent Neural Network Single Neuron Level Linear Recurrent Communication Pattern Synaptic Dynamic

April 8, 2022

Learning to Modulate Random Weights: Neuromodulation-inspired Neural Networks For Efficient Continual Learning
Jinyung Hong, Theodore P. Pavlic
LeArning Abstract Continual LEArning Catastrophic Forgetting Efficient Continual Learning Random Weight Synaptic Dynamic

February 4, 2022

Meta-Reinforcement Learning with Self-Modifying Networks
Mathieu Chalvidal, Thomas Serre, Rufin VanRullen
Deep Reinforcement Learning Meta Reinforcement Learning Synaptic Plasticity Self Improving Synaptic Dynamic

Synaptic Dynamic

Papers

Network Representation Learning for Biophysical Neural Network Analysis

Mamba-Spike: Enhancing the Mamba Architecture with a Spiking Front-End for Efficient Temporal Data Processing

Theories of synaptic memory consolidation and intelligent plasticity for continual learning

Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles

Theory of coupled neuronal-synaptic dynamics

Phenomenological modeling of diverse and heterogeneous synaptic dynamics at natural density

Interneurons accelerate learning dynamics in recurrent neural networks for statistical adaptation

Learning to Modulate Random Weights: Neuromodulation-inspired Neural Networks For Efficient Continual Learning

Meta-Reinforcement Learning with Self-Modifying Networks