The Neural Reckoning Group is led by Dan Goodman. It is part of the Intelligent Systems and Networks group in the Department of Electrical and Electronic Engineering at Imperial College London. Follow us on Twitter @neuralreckoning.
We aim to understand the brain, and intelligent behaviour more widely, via theoretical and computational models.
We are particularly interested in sensory processing, primarily the auditory system, where our goal is to understand how we make sense of complex, natural environments.
One of the most striking operating principles of the brain is the use of highly connected, parallel networks of neurons communicating via precisely timed, discrete neural impulses (called "spikes"). This spike-based form of computation is specific to the brain, being radically different to conventional digital and analogue computation. We wish to uncover the principles underlying spike-based neuronal computation, both as a fundamental research problem and with an eye to wider applications in intelligent systems.
Underlying much of our work is neuroinformatics, or the application of computational methods in neuroscience. We are particularly interested in the simulation of spiking neural networks and the analysis of experimentally recorded spiking data (see Software).
We maintain a short list of resources for learning computational neuroscience that might be useful to students and people new to the field.
- Chu Y, Luk W, Goodman D
Learning Absolute Sound Source Localisation With Limited Supervisions.
- Stimberg M, Goodman DFM, Nowotny T
Brian2GeNN: a system for accelerating a large variety of spiking neural networks with graphics hardware.
- Steadman MA, Kim C, Lestang JH, Goodman DFM, Picinali L
Short-term effects of sound localization training in virtual reality.
- Zheng JX, Pawar S, Goodman DFM
Further Towards Unambiguous Edge Bundling: Investigating Power-Confluent Drawings for Network Visualization.
IEEE Transactions on Visualization and Computer Graphics
- Chu Y, Goodman DFM (2019)
An Inference Network Model for Goal-directed Attentional Selection.
Cognitive Computational Neuroscience
- Stimberg M, Brette R, Goodman DFM
Brian 2, an intuitive and efficient neural simulator.
- Goodman DFM, Winter IM, Léger AC, de Cheveigné A, Lorenzi C
Modelling firing regularity in the ventral cochlear nucleus: mechanisms, and effects of stimulus level and synaptopathy.
- Rossant C, et al.
Spike sorting for large, dense electrode arrays.
- Goodman DFM, Benichoux V, Brette R
Decoding neural responses to temporal cues for sound localization.
- Goodman DFM, Brette R
Spike-timing-based computation in sound localization.
PLoS Computational Biology