Journal Article

FZJ-2018-00732

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Perfect Detection of Spikes in the Linear Sub-threshold Dynamics of Point Neurons

Krishnan, J. (Corresponding author)FZJ* ; Mana, P. ; Helias, M.FZJ* ; Diesmann, M.FZJ* ; Di Napoli, E.FZJ*

2018
Frontiers Research Foundation Lausanne

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Please use a persistent id in citations: http://hdl.handle.net/2128/16676  doi:10.3389/fninf.2017.00075

Abstract: Spiking neuronal networks are usually simulated with three main simulation schemes: the classical time-driven and event-driven schemes, and the more recent hybrid scheme. All three schemes evolve the state of a neuron through a series of checkpoints: equally spaced in the first scheme and determined neuron-wise by spike events in the latter two. The time-driven and the hybrid scheme determine whether the membrane potential of a neuron crosses a threshold at the end of of the time interval between consecutive checkpoints. Threshold crossing can, however, occur within the interval even if this test is negative. Spikes can therefore be missed. The present work derives, implements, and benchmarks a method for perfect retrospective spike detection. This method can be applied to neuron models with affine or linear subthreshold dynamics. The idea behind the method is to propagate the threshold with a time-inverted dynamics, testing whether the threshold crosses the neuron state to be evolved, rather than vice versa. Algebraically this translates into a set of inequalities necessary and sufficient for threshold crossing. This test is slower than the imperfect one, but faster than an alternative perfect tests based on bisection or root-finding methods. Comparison confirms earlier results that the imperfect test rarely misses spikes (less than a fraction $1/10^8$ of missed spikes) in biologically relevant settings. This study offers an alternative geometric point of view on neuronal dynamics.

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Contributing Institute(s):

1. Theoretical Neuroscience (IAS-6)
2. Computational and Systems Neuroscience (INM-6)
3. Jara-Institut Brain structure-function relationships (INM-10)
4. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 511 - Computational Science and Mathematical Methods (POF3-511) (POF3-511)
2. 574 - Theory, modelling and simulation (POF3-574) (POF3-574)
3. HBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270) (720270)
4. MSNN - Theory of multi-scale neuronal networks (HGF-SMHB-2014-2018) (HGF-SMHB-2014-2018)
5. SMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017) (HGF-SMHB-2013-2017)
6. Simulation and Data Laboratory Quantum Materials (SDLQM) (SDLQM) (SDLQM)

Appears in the scientific report 2018

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Database coverage:
; ; ; ; BIOSIS Previews ; DOAJ Seal ; IF < 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Thomson Reuters Master Journal List ; Web of Science Core Collection

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