Spike Timing-Dependent Plasticity with Enhanced Long-Term Depression Leads to an Increase of Statistical Complexity

Synaptic plasticity is characterized by remodeling of existing synapses caused by strengthening and/or weakening of connections. This is represented by long-term potentiation (LTP) and long-term depression (LTD). The occurrence of a presynaptic spike (or action potential) followed by a temporally ne...

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Autores principales: Pallares Di Nunzio, Monserrat, Montani, Fernando Fabián
Formato: Articulo
Lenguaje:Inglés
Publicado: 2022
Materias:
Ciencias Exactas
Física
information theory
Band and Pompe
plasticity
STDP
triplets
deep learning
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/155993
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-155993
record_format dspace
spelling I19-R120-10915-1559932023-08-07T20:07:41Z http://sedici.unlp.edu.ar/handle/10915/155993 Spike Timing-Dependent Plasticity with Enhanced Long-Term Depression Leads to an Increase of Statistical Complexity Pallares Di Nunzio, Monserrat Montani, Fernando Fabián 2022-10 2023-08-07T15:39:31Z en Ciencias Exactas Física information theory Band and Pompe plasticity STDP triplets deep learning Synaptic plasticity is characterized by remodeling of existing synapses caused by strengthening and/or weakening of connections. This is represented by long-term potentiation (LTP) and long-term depression (LTD). The occurrence of a presynaptic spike (or action potential) followed by a temporally nearby postsynaptic spike induces LTP; conversely, if the postsynaptic spike precedes the presynaptic spike, it induces LTD. This form of synaptic plasticity induction depends on the order and timing of the pre- and postsynaptic action potential, and has been termed spike time-dependent plasticity (STDP). After an epileptic seizure, LTD plays an important role as a depressor of synapses, which may lead to their complete disappearance together with that of their neighboring connections until days after the event. Added to the fact that after an epileptic seizure the network seeks to regulate the excess activity through two key mechanisms: depressed connections and neuronal death (eliminating excitatory neurons from the network), LTD becomes of great interest in our study. To investigate this phenomenon, we develop a biologically plausible model that privileges LTD at the triplet level while maintaining the pairwise structure in the STPD and study how network dynamics are affected as neuronal damage increases. We find that the statistical complexity is significantly higher for the network where LTD presented both types of interactions. While in the case where the STPD is defined with purely pairwise interactions an increase is observed as damage becomes higher for both Shannon Entropy and Fisher information. Instituto de Física La Plata Articulo Articulo http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International (CC BY 4.0) application/pdf
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Exactas
Física
information theory
Band and Pompe
plasticity
STDP
triplets
deep learning
spellingShingle Ciencias Exactas
Física
information theory
Band and Pompe
plasticity
STDP
triplets
deep learning
Pallares Di Nunzio, Monserrat
Montani, Fernando Fabián
Spike Timing-Dependent Plasticity with Enhanced Long-Term Depression Leads to an Increase of Statistical Complexity
topic_facet Ciencias Exactas
Física
information theory
Band and Pompe
plasticity
STDP
triplets
deep learning
description Synaptic plasticity is characterized by remodeling of existing synapses caused by strengthening and/or weakening of connections. This is represented by long-term potentiation (LTP) and long-term depression (LTD). The occurrence of a presynaptic spike (or action potential) followed by a temporally nearby postsynaptic spike induces LTP; conversely, if the postsynaptic spike precedes the presynaptic spike, it induces LTD. This form of synaptic plasticity induction depends on the order and timing of the pre- and postsynaptic action potential, and has been termed spike time-dependent plasticity (STDP). After an epileptic seizure, LTD plays an important role as a depressor of synapses, which may lead to their complete disappearance together with that of their neighboring connections until days after the event. Added to the fact that after an epileptic seizure the network seeks to regulate the excess activity through two key mechanisms: depressed connections and neuronal death (eliminating excitatory neurons from the network), LTD becomes of great interest in our study. To investigate this phenomenon, we develop a biologically plausible model that privileges LTD at the triplet level while maintaining the pairwise structure in the STPD and study how network dynamics are affected as neuronal damage increases. We find that the statistical complexity is significantly higher for the network where LTD presented both types of interactions. While in the case where the STPD is defined with purely pairwise interactions an increase is observed as damage becomes higher for both Shannon Entropy and Fisher information.
format Articulo
Articulo
author Pallares Di Nunzio, Monserrat
Montani, Fernando Fabián
author_facet Pallares Di Nunzio, Monserrat
Montani, Fernando Fabián
author_sort Pallares Di Nunzio, Monserrat
title Spike Timing-Dependent Plasticity with Enhanced Long-Term Depression Leads to an Increase of Statistical Complexity
title_short Spike Timing-Dependent Plasticity with Enhanced Long-Term Depression Leads to an Increase of Statistical Complexity
title_full Spike Timing-Dependent Plasticity with Enhanced Long-Term Depression Leads to an Increase of Statistical Complexity
title_fullStr Spike Timing-Dependent Plasticity with Enhanced Long-Term Depression Leads to an Increase of Statistical Complexity
title_full_unstemmed Spike Timing-Dependent Plasticity with Enhanced Long-Term Depression Leads to an Increase of Statistical Complexity
title_sort spike timing-dependent plasticity with enhanced long-term depression leads to an increase of statistical complexity
publishDate 2022
url http://sedici.unlp.edu.ar/handle/10915/155993
work_keys_str_mv AT pallaresdinunziomonserrat spiketimingdependentplasticitywithenhancedlongtermdepressionleadstoanincreaseofstatisticalcomplexity
AT montanifernandofabian spiketimingdependentplasticitywithenhancedlongtermdepressionleadstoanincreaseofstatisticalcomplexity
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