Technical integration of hippocampus, basal ganglia and physical models for spatial navigation

Computational neuroscience is increasingly moving beyond modeling individual neurons or neural systems to consider the integration of multiple models, often constructed by different research groups. We report on our preliminary technical integration of recent hippocampal formation, basal ganglia an...
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Gespeichert in:

Autor*in:	Charles W Fox [verfasserIn] Mark D Humphries [verfasserIn] Ben Mitchinson [verfasserIn] Tamas Kiss [verfasserIn] Zoltan Somogyva [verfasserIn] Tony J Prescott [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2009

Schlagwörter:	Basal Ganglia BRAHMS Hippocampus Place Cells plus-maze python

Übergeordnetes Werk:	In: Frontiers in Neuroinformatics - Frontiers Media S.A., 2008, 3(2009)
Übergeordnetes Werk:	volume:3 ; year:2009

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/neuro.11.006.2009

Katalog-ID:	DOAJ017708486

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allfieldsSound	10.3389/neuro.11.006.2009 doi (DE-627)DOAJ017708486 (DE-599)DOAJbe64935d78f340d988e3f60037d8d9d2 DE-627 ger DE-627 rakwb eng RC321-571 Charles W Fox verfasserin aut Technical integration of hippocampus, basal ganglia and physical models for spatial navigation 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Computational neuroscience is increasingly moving beyond modeling individual neurons or neural systems to consider the integration of multiple models, often constructed by different research groups. We report on our preliminary technical integration of recent hippocampal formation, basal ganglia and physical environment models, together with visualisation tools, as a case study in the use of Python across the modelling tool-chain. We do not present new modeling results here. The architecture incorporates leaky-integrator and rate-coded neurons, a 3D environment with collision detection and tactile sensors, 3D graphics and 2D plots. We found Python to be a flexible platform, offering a significant reduction in development time, without a corresponding significant increase in execution time. We illustrate this by implementing a part of the model in various alternative languages and coding styles, and comparing their execution times. For very large scale system integration, communication with other languages and parallel execution may be required, which we demonstrate using the BRAHMS framework's Python bindings. Basal Ganglia BRAHMS Hippocampus Place Cells plus-maze python Neurosciences. Biological psychiatry. Neuropsychiatry Mark D Humphries verfasserin aut Ben Mitchinson verfasserin aut Tamas Kiss verfasserin aut Zoltan Somogyva verfasserin aut Tony J Prescott verfasserin aut In Frontiers in Neuroinformatics Frontiers Media S.A., 2008 3(2009) (DE-627)57982652X (DE-600)2452979-5 16625196 nnns volume:3 year:2009 https://doi.org/10.3389/neuro.11.006.2009 kostenfrei https://doaj.org/article/be64935d78f340d988e3f60037d8d9d2 kostenfrei http://journal.frontiersin.org/Journal/10.3389/neuro.11.006.2009/full kostenfrei https://doaj.org/toc/1662-5196 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2009
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authorswithroles_txt_mv	Charles W Fox @@aut@@ Mark D Humphries @@aut@@ Ben Mitchinson @@aut@@ Tamas Kiss @@aut@@ Zoltan Somogyva @@aut@@ Tony J Prescott @@aut@@
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callnumber-first	R - Medicine
author	Charles W Fox
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title	Technical integration of hippocampus, basal ganglia and physical models for spatial navigation
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title_auth	Technical integration of hippocampus, basal ganglia and physical models for spatial navigation
abstract	Computational neuroscience is increasingly moving beyond modeling individual neurons or neural systems to consider the integration of multiple models, often constructed by different research groups. We report on our preliminary technical integration of recent hippocampal formation, basal ganglia and physical environment models, together with visualisation tools, as a case study in the use of Python across the modelling tool-chain. We do not present new modeling results here. The architecture incorporates leaky-integrator and rate-coded neurons, a 3D environment with collision detection and tactile sensors, 3D graphics and 2D plots. We found Python to be a flexible platform, offering a significant reduction in development time, without a corresponding significant increase in execution time. We illustrate this by implementing a part of the model in various alternative languages and coding styles, and comparing their execution times. For very large scale system integration, communication with other languages and parallel execution may be required, which we demonstrate using the BRAHMS framework's Python bindings.
abstractGer	Computational neuroscience is increasingly moving beyond modeling individual neurons or neural systems to consider the integration of multiple models, often constructed by different research groups. We report on our preliminary technical integration of recent hippocampal formation, basal ganglia and physical environment models, together with visualisation tools, as a case study in the use of Python across the modelling tool-chain. We do not present new modeling results here. The architecture incorporates leaky-integrator and rate-coded neurons, a 3D environment with collision detection and tactile sensors, 3D graphics and 2D plots. We found Python to be a flexible platform, offering a significant reduction in development time, without a corresponding significant increase in execution time. We illustrate this by implementing a part of the model in various alternative languages and coding styles, and comparing their execution times. For very large scale system integration, communication with other languages and parallel execution may be required, which we demonstrate using the BRAHMS framework's Python bindings.
abstract_unstemmed	Computational neuroscience is increasingly moving beyond modeling individual neurons or neural systems to consider the integration of multiple models, often constructed by different research groups. We report on our preliminary technical integration of recent hippocampal formation, basal ganglia and physical environment models, together with visualisation tools, as a case study in the use of Python across the modelling tool-chain. We do not present new modeling results here. The architecture incorporates leaky-integrator and rate-coded neurons, a 3D environment with collision detection and tactile sensors, 3D graphics and 2D plots. We found Python to be a flexible platform, offering a significant reduction in development time, without a corresponding significant increase in execution time. We illustrate this by implementing a part of the model in various alternative languages and coding styles, and comparing their execution times. For very large scale system integration, communication with other languages and parallel execution may be required, which we demonstrate using the BRAHMS framework's Python bindings.
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title_short	Technical integration of hippocampus, basal ganglia and physical models for spatial navigation
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Technical integration of hippocampus, basal ganglia and physical models for spatial navigation

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