2 citations · 2 across the 6 of their papers we have counts for
6 papers
Emergent Generalization by Representation Learning in Artificial Neural Networks
Hardik Rajpal, Dan Goodman
Dimensionality reduction has proven powerful for identifying neural manifolds, which are low-dimensional structures underlying high-dimensional neural activity. These low-dimension…
Unifying Dynamical Systems and Graph Theory to Mechanistically Understand Computation in Neural Networks
Jatin Sharma, Dan F. M Goodman, Danyal Akarca
Understanding how biological and artificial neural networks implement computation from connectivity is a central problem in neuroscience and machine learning. In neural systems, st…
Algorithm-hardware co-design of neuromorphic networks with dual memory pathways
Pengfei Sun, Zhe Su, Jascha Achterberg +3
Spiking neural networks excel at event-driven sensing. Yet, maintaining task-relevant context over long timescales both algorithmically and in hardware, while respecting both tight…
Exploiting heterogeneous delays for efficient computation in low-bit neural networks
Pengfei Sun, Jascha Achterberg, Zhe Su +2
Neural networks rely on learning synaptic weights. However, this overlooks other neural parameters that can also be learned and may be utilized by the brain. One such parameter is…
Beyond Rate Coding: Surrogate Gradients Enable Spike Timing Learning in Spiking Neural Networks
Ziqiao Yu, Pengfei Sun, Danyal Akarca +1
The surrogate gradient descent algorithm enabled spiking neural networks to be trained to carry out challenging sensory processing tasks, an important step in understanding how spi…
A Path to Universal Neural Cellular Automata
Gabriel Béna, Maxence Faldor, Dan F. M. Goodman +1
Cellular automata have long been celebrated for their ability to generate complex behaviors from simple, local rules, with well-known discrete models like Conway's Game of Life pro…