This title appears in the Scientific Report :
2018
Please use the identifier:
http://dx.doi.org/10.3389/fninf.2017.00075 in citations.
Please use the identifier: http://hdl.handle.net/2128/16676 in citations.
Perfect Detection of Spikes in the Linear Sub-threshold Dynamics of Point Neurons
Perfect Detection of Spikes in the Linear Sub-threshold Dynamics of Point Neurons
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...
Saved in:
Personal Name(s): | Krishnan, Jeyashree (Corresponding author) |
---|---|
Mana, PierGianLuca / Helias, Moritz / Diesmann, Markus / Di Napoli, Edoardo | |
Contributing Institute: |
Computational and Systems Neuroscience; IAS-6 Jülich Supercomputing Center; JSC Jara-Institut Brain structure-function relationships; INM-10 Computational and Systems Neuroscience; INM-6 |
Published in: | Frontiers in neuroinformatics, 11 (2018) S. 75 |
Imprint: |
Lausanne
Frontiers Research Foundation
2018
|
DOI: |
10.3389/fninf.2017.00075 |
PubMed ID: |
29379430 |
Document Type: |
Journal Article |
Research Program: |
Supercomputing and Modelling for the Human Brain Theory of multi-scale neuronal networks Human Brain Project Specific Grant Agreement 1 Theory, modelling and simulation Computational Science and Mathematical Methods Simulation and Data Laboratory Quantum Materials (SDLQM) |
Link: |
OpenAccess OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://hdl.handle.net/2128/16676 in citations.
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. |