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 more recent 2007 IBM Blue Gene/P supercomputer used in the Blue Brain Project, with thousands of processors. (Image credit: Wikipedia.)
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 nonexperts. Our research is about leveraging modern, heterogeneous forms of computing such as GPUs, FPGAs, and the SpiNNaker spiking neural network supercomputer, but making them accessible to neuroscience researchers.
We currently work in two main areas of neuroinformatics with associated software packages: simulating spiking neural networks (the Brian simulator); and, analysing data recorded from multichannel electrodes in the brain (KlustaSuite).
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.
Publications in neuroinformatics

Stimberg M, Goodman DFM, Brette R, De PittÃ M
Modeling neuronglia interactions with the Brian 2 simulator.

Dietz M, Lestang JH, Majdak P, Stern RM, Marquardt T, Ewert SD, Hartmann WH, Goodman DFM
(2018)
A framework for testing and comparing binaural models.
Hearing Research doi: 10.1016/j.heares.2017.11.010

Rossant C, Kadir SN, Goodman DFM, et al.
(2016)
Spike sorting for large, dense electrode arrays.
Nature Neuroscience doi: 10.1038/nn.4268

Stimberg M, Goodman DFM, Benichoux V, Brette R
(2014)
Equationoriented specification of neural models for simulations.
Frontiers in Neuroinformatics 8:6. doi: 10.3389/fninf.2014.00006 
Kadir SN, Goodman DFM, Harris KD
(2014)
Highdimensional cluster analysis with the masked EM algorithm.
Neural Computation 26:11. doi:10.1162/NECO_a_00661

Goodman DFM, Brette R
(2013)
Brian simulator.
Scholarpedia 8(1):10883 
Goodman DFM, Brette R
(2013)
Brian Spiking Neural Network Simulator.
Encyclopedia of Computational Neuroscience SpringerReference 
Rossant C, Fontaine B, Goodman DFM
(2013)
Playdoh: a lightweight Python package for distributed computing and optimisation.
Journal of Computational Science 4(5):352259

Brette R, Goodman DFM
(2012)
Simulating spiking neural networks on GPU.
Network: Computation in Neural Systems 23(4)

Fontaine B, Goodman DFM, Benichoux V, Brette R
(2011)
Brian Hears: online auditory processing using vectorisation over channels.
Frontiers in Neuroinformatics 5:9. doi: 10.3389/fninf.2011.00009 
Brette R, Goodman DFM
(2011)
Vectorised algorithms for spiking neural network simulation.
Neural Computation 23:6 
Rossant C, Goodman DFM, Fontaine B, Platkiewicz J, Magnusson AK, Brette R
(2011)
Fitting neuron models to spike trains.
Frontiers in Neuroscience 5:9. doi: 10.3389/fnins.2011.00009

Goodman DFM
(2010)
Code Generation: A Strategy for Neural Network Simulators.
Neuroinformatics 8, no. 3 (9). doi:10.1007/s120210109082x 
Rossant C, Goodman DFM, Platkiewicz J, Brette R
(2010)
Automatic fitting of spiking neuron models to electrophysiological recordings.
Frontiers in Neuroinformatics doi:10.3389/neuro.11.002.2010

Goodman DFM, Brette R
(2009)
The Brian simulator.
Frontiers in Neuroscience 3(2), doi:10.3389/neuro.01.026.2009 
Brette R, Goodman D
(2009)
Brian: a simple and flexible simulator for spiking neural networks.
The Neuromorphic Engineer doi: 10.2417/1200906.1659

Goodman D, Brette R
(2008)
Brian: a simulator for spiking neural networks in Python.
Frontiers in Neuroinformatics 2(5), doi:10.3389/neuro.11.005.2008