Emergent properties of interacting populations of spiking neurons
Dynamic neuronal networks are a key paradigm of increasing importance in brain research, concerned with the functional analysis of biological neuronal networks and, at the same time, with the synthesis of artificial brain-like systems. In this context, neuronal network models serve as mathematical t...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2011-12-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fncom.2011.00059/full |
| _version_ | 1798043380332101632 |
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| author | Stefano eCardanobile Stefan eRotter |
| author_facet | Stefano eCardanobile Stefan eRotter |
| author_sort | Stefano eCardanobile |
| collection | DOAJ |
| description | Dynamic neuronal networks are a key paradigm of increasing importance in brain research, concerned with the functional analysis of biological neuronal networks and, at the same time, with the synthesis of artificial brain-like systems. In this context, neuronal network models serve as mathematical tools to understand the function of brains, but they might as well develop into future tools for enhancing certain functions of our nervous system.Here, we discuss our recent achievements in developing multiplicative point processes into a viable mathematical framework for spiking network modeling. The perspective is that the dynamic behavior of these neuronal networks on the population level is faithfully reflected by a set of non-linear rate equations, describing all interactions on this level. These equations, in turn, are similar in structure to the Lotka-Volterra equations, well known by their use in modeling predator-prey relationships in population biology, but abundant applications to economic theory have also been described.We present a number of biologically relevant examples for spiking network function, which can be studied with the help of the aforementioned correspondence between spike trains and specific systems of non-linear coupled ordinary differential equations. We claim that, enabled by the use of multiplicative point processes, we can make essential contributions to a more thorough understanding of the dynamical properties of neural populations. |
| first_indexed | 2024-04-11T22:48:16Z |
| format | Article |
| id | doaj.art-9b9a17da32c94eb2b6edd4e69c42676b |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-04-11T22:48:16Z |
| publishDate | 2011-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-9b9a17da32c94eb2b6edd4e69c42676b2022-12-22T03:58:42ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882011-12-01510.3389/fncom.2011.0005910913Emergent properties of interacting populations of spiking neuronsStefano eCardanobile0Stefan eRotter1University of FreiburgUniversity of FreiburgDynamic neuronal networks are a key paradigm of increasing importance in brain research, concerned with the functional analysis of biological neuronal networks and, at the same time, with the synthesis of artificial brain-like systems. In this context, neuronal network models serve as mathematical tools to understand the function of brains, but they might as well develop into future tools for enhancing certain functions of our nervous system.Here, we discuss our recent achievements in developing multiplicative point processes into a viable mathematical framework for spiking network modeling. The perspective is that the dynamic behavior of these neuronal networks on the population level is faithfully reflected by a set of non-linear rate equations, describing all interactions on this level. These equations, in turn, are similar in structure to the Lotka-Volterra equations, well known by their use in modeling predator-prey relationships in population biology, but abundant applications to economic theory have also been described.We present a number of biologically relevant examples for spiking network function, which can be studied with the help of the aforementioned correspondence between spike trains and specific systems of non-linear coupled ordinary differential equations. We claim that, enabled by the use of multiplicative point processes, we can make essential contributions to a more thorough understanding of the dynamical properties of neural populations.http://journal.frontiersin.org/Journal/10.3389/fncom.2011.00059/fullcentral pattern generatorstochastic resonanceInteracting Poisson processesLinear classifierLotka-Volterra equationsPoint Processes |
| spellingShingle | Stefano eCardanobile Stefan eRotter Emergent properties of interacting populations of spiking neurons Frontiers in Computational Neuroscience central pattern generator stochastic resonance Interacting Poisson processes Linear classifier Lotka-Volterra equations Point Processes |
| title | Emergent properties of interacting populations of spiking neurons |
| title_full | Emergent properties of interacting populations of spiking neurons |
| title_fullStr | Emergent properties of interacting populations of spiking neurons |
| title_full_unstemmed | Emergent properties of interacting populations of spiking neurons |
| title_short | Emergent properties of interacting populations of spiking neurons |
| title_sort | emergent properties of interacting populations of spiking neurons |
| topic | central pattern generator stochastic resonance Interacting Poisson processes Linear classifier Lotka-Volterra equations Point Processes |
| url | http://journal.frontiersin.org/Journal/10.3389/fncom.2011.00059/full |
| work_keys_str_mv | AT stefanoecardanobile emergentpropertiesofinteractingpopulationsofspikingneurons AT stefanerotter emergentpropertiesofinteractingpopulationsofspikingneurons |