Literature DB >> 18252548

Class 1 neural excitability, conventional synapses, weakly connected networks, and mathematical foundations of pulse-coupled models.

E M Izhikevich1.   

Abstract

Many scientists believe that all pulse-coupled neural networks are toy models that are far away from the biological reality. We show here, however, that a huge class of biophysically detailed and biologically plausible neural-network models can be transformed into a canonical pulse-coupled form by a piece-wise continuous, possibly noninvertible, change of variables. Such transformations exist when a network satisfies a number of conditions; e.g., it is weakly connected; the neurons are Class 1 excitable (i.e., they can generate action potentials with an arbitrary small frequency); and the synapses between neurons are conventional (i.e., axo-dendritic and axo-somatic). Thus, the difference between studying the pulse-coupled model and Hodgkin-Huxley-type neural networks is just a matter of a coordinate change. Therefore, any piece of information about the pulse-coupled model is valuable since it tells something about all weakly connected networks of Class 1 neurons. For example, we show that the pulse-coupled network of identical neurons does not synchronize in-phase. This confirms Ermentrout's result that weakly connected Class 1 neurons are difficult to synchronize, regardless of the equations that describe dynamics of each cell.

Entities:  

Year:  1999        PMID: 18252548     DOI: 10.1109/72.761707

Source DB:  PubMed          Journal:  IEEE Trans Neural Netw        ISSN: 1045-9227


  13 in total

1.  Phase-response curves and synchronized neural networks.

Authors:  Roy M Smeal; G Bard Ermentrout; John A White
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2010-08-12       Impact factor: 6.237

2.  Neural synchronization at tonic-to-bursting transitions.

Authors:  Svetlana Postnova; Karlheinz Voigt; Hans A Braun
Journal:  J Biol Phys       Date:  2007-10-26       Impact factor: 1.365

3.  Integrate and fire neural networks, piecewise contractive maps and limit cycles.

Authors:  Eleonora Catsigeras; Pierre Guiraud
Journal:  J Math Biol       Date:  2012-07-21       Impact factor: 2.259

4.  Determining the contributions of divisive and subtractive feedback in the Hodgkin-Huxley model.

Authors:  Sevgi Sengül; Robert Clewley; Richard Bertram; Joël Tabak
Journal:  J Comput Neurosci       Date:  2014-06-25       Impact factor: 1.621

Review 5.  Timing in cognition and EEG brain dynamics: discreteness versus continuity.

Authors:  Andrew A Fingelkurts; Alexander A Fingelkurts
Journal:  Cogn Process       Date:  2006-07-11

6.  A Framework for Engineering the Collective Behavior of Complex Rhythmic Systems.

Authors:  Craig G Rusin; István Z Kiss; Hiroshi Kori; John L Hudson
Journal:  Ind Eng Chem Res       Date:  2009-03-16       Impact factor: 3.720

7.  Rapid Spectral Dynamics in Hippocampal Oscillons.

Authors:  M S Zobaer; Carli M Domenico; Luca Perotti; Daoyun Ji; Yuri Dabaghian
Journal:  Front Comput Neurosci       Date:  2022-06-10       Impact factor: 3.387

8.  Interaction of cellular and network mechanisms in spatiotemporal pattern formation in neuronal networks.

Authors:  Andrew Bogaard; Jack Parent; Michal Zochowski; Victoria Booth
Journal:  J Neurosci       Date:  2009-02-11       Impact factor: 6.167

9.  A computational role for bistability and traveling waves in motor cortex.

Authors:  Stewart Heitmann; Pulin Gong; Michael Breakspear
Journal:  Front Comput Neurosci       Date:  2012-09-11       Impact factor: 2.380

10.  Deformation of attractor landscape via cholinergic presynaptic modulations: a computational study using a phase neuron model.

Authors:  Takashi Kanamaru; Hiroshi Fujii; Kazuyuki Aihara
Journal:  PLoS One       Date:  2013-01-11       Impact factor: 3.240

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