Neuroinformatics

Neuroinformatics

A Brunsviga hand cranked mechanical calculator as used by Hodgkin and Huxley to compute numerically the solutions to their differential equations for the action potential in the squid giant axon in 1952.

A Brunsviga hand cranked mechanical calculator as used by Hodgkin and Huxley to compute numerically the solutions to their differential equations for the action potential in the squid giant axon in 1952.

Neuroinformatics is concerned with developing computational techniques for understanding the brain. Specifically, processing and analysing experimental data, and simulating models. This work is becoming increasingly important as neuroscientists study larger and more detailed systems, requiring the use of high performance computational techniques which are not, as yet, easily accessible for non-experts. Much of our research is about leveraging modern, heterogeneous forms of computing such as GPUs and FPGAs, but making them accessible to neuroscience researchers.

Our main ongoing area of research is in simulating spiking neural networks: on the design of the Brian simulator and related software packages.

If you are interested in working on these types of problems, please see our openings page and the list of suggested research topics. See also the list of neuroinformatics publications below for an idea of the sort of research we have done in this field in the past.

Related organisations

European network for immersive audio.

Related software

Auditory Model Initiative

Python/Matlab package for comparing binaural auditory models.

HumanlikeHearing

Python package for psychophysical tests of automatic speech recognition systems.

Related videos

Multimodal units fuse-then-accumulate evidence across channels
Talk on multimodal processing given at VVTNS 2023 seminar series
Talk / 2023
The Psychometrics of Automatic Speech Recognition
Talk on applying psychometric testing to automatic speech recognition systems.
Talk / 2022
Understanding the role of neural heterogeneity in learning
Talk on neural heterogeneity by Nicolas Perez.
Talk / 2021
Neural heterogeneity promotes robust learning
Talk on neural heterogeneity by Dan Goodman.
Talk / 2021
Binaural sound localisation game demo
Demo of binaural sound localisation virtual reality game.
Demo / 2016

Related publications

2024

Ghosh M, Béna G, Bormuth V, Goodman DFM
Nonlinear fusion is optimal for a wide class of multisensory tasks.
Preprint

2022

Weerts L, Rosen S, Clopath C, Goodman DFM
The Psychometrics of Automatic Speech Recognition.
Preprint
Engel I, Goodman DFM, Picinali L (2022)
Assessing HRTF preprocessing methods for Ambisonics rendering through perceptual models.
Acta Acustica

2021

Perez-Nieves N, Leung VCH, Dragotti PL, Goodman DFM (2021)
Neural heterogeneity promotes robust learning.
Nature Communications
Su Y, Chung Y, Goodman DFM, Hancock KE, Delgutte B (2021)
Rate and Temporal Coding of Regular and Irregular Pulse Trains in Auditory Midbrain of Normal‑Hearing and Cochlear‑Implanted Rabbits.
Journal of the Association for Research in Otolaryngology

2020

Chu Y, Luk W, Goodman D
Learning Absolute Sound Source Localisation With Limited Supervisions.
Preprint

2019

Lestang J-H, Goodman DFM
General neural mechanisms can account for rising slope preference in localization of ambiguous sounds.
Preprint
Chu Y, Goodman DFM (2019)
An Inference Network Model for Goal-directed Attentional Selection.
Cognitive Computational Neuroscience
Engel I, Goodman DFM, Picinali L (2019)
The Effect of Auditory Anchors on Sound Localization: A Preliminary Study.
Immersive and Interactive Audio
Weerts L, Clopath C, Goodman DFM (2019)
A Unifying Framework for Neuro-Inspired, Data-Driven Detection of Low-Level Auditory Features.
Cognitive Computational Neuroscience
Steadman MA, Kim C, Lestang JH, Goodman DFM, Picinali L (2019)
Short-term effects of sound localization training in virtual reality.
Scientific Reports
Lestang J-H (2019)
The role of canonical neural computations in sound localization.
PhD thesis, Imperial College London
+ 3 conference papers

2018

Dietz M, et al. (2018)
A framework for testing and comparing binaural models.
Hearing Research
Goodman DFM, Winter IM, Léger AC, de Cheveigné A, Lorenzi C (2018)
Modelling firing regularity in the ventral cochlear nucleus: mechanisms, and effects of stimulus level and synaptopathy.
Hearing Research
Chungeun K, Steadman M, Lestang JH, Goodman DFM, Picinali L (2018)
A VR-Based Mobile Platform for Training to Non-Individualized Binaural 3D Audio.
Audio Engineering Society
+ 1 conference paper

2017

Goodman DF (2017)
On the use of hypothesis-driven reduced models in auditory neuroscience.
Acoustical Society of America
Dietz M, et al. (2017)
An initiative for testability and comparability of binaural models.
Acoustical Society of America
Lestang JH, Goodman DF (2017)
The roles of inhibition and adaptation for spatial hearing in difficult listening conditions.
Acoustical Society of America
+ 3 conference papers

2016

Dietz M, et al. (2016)
A framework for auditory model comparability and applicability.
Acoustical Society of America
+ 1 conference paper

2015

Goodman DFM, de Cheveigné A, Winter IM, Lorenzi C (2015)
Downstream changes in firing regularity following damage to the early auditory system.
Computational Neuroscience
+ 1 conference paper

2013

Goodman DFM, Benichoux V, Brette R (2013)
Decoding neural responses to temporal cues for sound localization.
eLife

2011

Fontaine B, Goodman DFM, Benichoux V, Brette R (2011)
Brian Hears: online auditory processing using vectorisation over channels.
Frontiers in Neuroinformatics
Kremer Y, Léger J-F, Goodman D, Brette R, Bourdieu L (2011)
Late emergence of the vibrissa direction selectivity map in the rat barrel cortex.
Journal of Neuroscience

2010

Goodman DFM, Brette R (2010)
Learning to localise sounds with spiking neural networks.
Advances in Neural Information Processing Systems
Goodman DFM, Brette R (2010)
Spike-timing-based computation in sound localization.
PLoS Computational Biology