4 citations · 6 across the 3 of their papers we have counts for
6 papers
Spiking Network Initialisation and Firing Rate Collapse
Nicolas Perez-Nieves, Dan F. M Goodman
In recent years, newly developed methods to train spiking neural networks (SNNs) have rendered them as a plausible alternative to Artificial Neural Networks (ANNs) in terms of accu…
Dynamics of specialization in neural modules under resource constraints
Gabriel Béna, Dan F. M. Goodman
It has long been believed that the brain is highly modular both in terms of structure and function, although recent evidence has led some to question the extent of both types of mo…
Sparse Spiking Gradient Descent
Nicolas Perez-Nieves, Dan F. M. Goodman
There is an increasing interest in emulating Spiking Neural Networks (SNNs) on neuromorphic computing devices due to their low energy consumption. Recent advances have allowed trai…
Learning spatial hearing via innate mechanisms
Yang Chu, Wayne Luk, Dan Goodman
The acoustic cues used by humans and other animals to localise sounds are subtle, and change during and after development. This means that we need to constantly relearn or recalibr…
Further Towards Unambiguous Edge Bundling: Investigating Power-Confluent Drawings for Network Visualization
Jonathan X. Zheng, Samraat Pawar, Dan F. M. Goodman
Bach et al. [1] recently presented an algorithm for constructing confluent drawings, by leveraging power graph decomposition to generate an auxiliary routing graph. We identify two…
Graph Drawing by Stochastic Gradient Descent
Jonathan X. Zheng, Samraat Pawar, Dan F. M. Goodman
A popular method of force-directed graph drawing is multidimensional scaling using graph-theoretic distances as input. We present an algorithm to minimize its energy function, know…