neuroinformatics software engineering position at UC London

[Angus Silver]

Software Development: Tools for Grid-Based Computational Neurobiology

A research-based software engineering position is available in the
Department of Physiology University College London UK as part of the MRC
funded Grid-enabled modelling tools and databases for neuroinformatics a
collaborative project with the Institute for Adaptive and Neural
Computation, Division of Informatics, University of Edinburgh. The aim of
the project is to develop software tools to aid construction, visualization,
and analysis for both realistic neural network models of cerebellar cortex
constructed in the Neuron and Genesis simulation environments and for lower
level 3D-diffusion-reaction models of synaptic mechanisms. The candidate
will have a strong quantitative background with a degree in neuroscience,
computer science, physics or engineering and will be expert in programming
in JAVA, and C. A higher degree (MSc/PhD) would be advantageous and
experience with the Neuron simulator would be useful but not essential.
Development of software tools will be closely linked to existing modelling
projects (e.g. see abstract below) and to the electrophysiological and
optical experiments carried out in Dr Silvers lab. The post is funded for 2
years at #33025 p.a. Further information is available from Angus Silver
(a.silver at ucl.ac.uk). To apply please send CV before July 10th 2003.

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Shunting Inhibition Modulates Neuronal Gain during Synaptic Excitation.

Neuron. 2003 May 8;38(3):433-45.

Mitchell SJ, Silver RA.

Department of Physiology, University College London, Gower Street, WC1E 6BT,
London, United Kingdom

Neuronal gain control is important for processing information in the brain.
Shunting inhibition is not thought to control gain since it shifts
input-output relationships during tonic excitation rather than changing
their slope. Here we show that tonic inhibition reduces the gain and shifts
the offset of cerebellar granule cell input-output relationships during
frequency-dependent excitation with synaptic conductance waveforms. Shunting
inhibition scales subthreshold voltage, increasing the excitation frequency
required to attain a particular firing rate. This reduces gain because
frequency-dependent increases in input variability, which couple mean
subthreshold voltage to firing rate, boost voltage fluctuations during
inhibition. Moreover, synaptic time course and the number of inputs also
influence gain changes by setting excitation variability. Our results
suggest that shunting inhibition can multiplicatively scale rate-coded
information in neurons with high-variability synaptic inputs.