Simulating neural computation and information processing with Brian - - PowerPoint PPT Presentation

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Simulating neural computation and information processing with Brian - - PowerPoint PPT Presentation

Dec 26, 2023 248 likes •395 views

Simulating neural computation and information processing with Brian Marcel Stimberg Institut de la Vision/Sorbonne Universit marcel.stimberg@inserm.fr Course material Updated material will be uploaded here: g i t h u b . c o m /

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Simulating neural computation and information processing with Brian Marcel Stimberg

Institut de la Vision/Sorbonne Université

marcel.stimberg@inserm.fr

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Course material

Updated material will be uploaded here:

g i t h u b . c

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/ b r i a n

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e a m / b r i a n

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a t e r i a l / t r e e / m a s t e r / 2 1 9

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n e To download everything in a single ZIP fjle (includes material from other courses as well): g i t h u b . c

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/ b r i a n

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e a m / b r i a n

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a t e r i a l / a r c h i v e / m a s t e r . z i p To download individual jupyter notebook fjles, make sure to switch to “raw” view

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Plan for today

  • Introductjon to modelling with Brian

Interactjve tutorial (“live coding”):

  • The jupyter notebook
  • Part 1: Modelling neurons
  • Part 2: Modelling synapses
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Modelling networks of neurons

Individual elements

Detailed neuronal morphologies point-neuron models

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Modelling networks of neurons

Individual elements

Point-neuron models

Hodgkin-Huxley formalism integrate-and-fjre model

input activity

fjring rate models

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Modelling networks of neurons

Synapses

membrane potential

“delta synapse”

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Modelling networks of neurons

Synapses

membrane potential synaptic current

“delta synapse” exponential current-based

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Modelling networks of neurons

Synapses

membrane potential synaptic current synaptic conductance

“delta synapse” exponential current-based exponential conductance-based

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The simulator

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Who is Brian?

  • Simulator for spiking neuronal networks, written in Python
  • Started by Dan Goodman and Romain Brette at ENS Paris in 2007
  • “A simulator should not only save the time of processors, but also

the time of scientists”

  • Does not provide a library of fjxed models but allows for a fmexible

defjnition of (almost) arbitrary models

  • Focusses on “medium-sized” neuronal networks

(“a few” to ~100000 neurons), simulations on standard PCs, not supercomputers

  • Tool for research and teaching
  • Free-and-open-source
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Brian's approach

  • Philosophy: Mathematical model descriptions

– Flexible system to defjne models instead of library of prepared models – Explicit about model details – Mathematical notation, physical units

  • Technology: Code generation

– High-level descriptions transformed into low-level code – Modular architecture allows for extensions (e.g. to run code on GPU)

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Example: neuron model

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Example synapse model

exponential, current-based synapse:

  • when a spike arrives, increase Isyn by 0.1nA
  • between spikes, decay exponentially with τsyn

dI syn dt =−I syn τsyn

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More info

Docum cument ntation: https://brian2.readthedocs.io Mailing list st: briansupport@googlegroups.com Art Articles: s:

Stimberg, Marcel, Romain Brette, and Dan FM Goodman. “Brian 2, an Intuitive and Effjcient Neural Simulator.” ELife 8 (2019): e47314. https://doi.org/10.7554/eLife.47314. Stimberg, Marcel, Dan F. M. Goodman, Victor Benichoux, and Romain Brette. “Equation-Oriented Specifjcation of Neural Models for Simulations.” Frontiers in Neuroinformatics 8 (2014). https://doi.org/10.3389/fninf.2014.00006