Modeling the contribution of theta-gamma coupling to sequential memory, imagination, and dreaming

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Název: Modeling the contribution of theta-gamma coupling to sequential memory, imagination, and dreaming
Autoři: Pirazzini G., Ursino M.
Zdroj: Front Neural Circuits
Frontiers in Neural Circuits, Vol 18 (2024)
Informace o vydavateli: Frontiers Media SA, 2024.
Rok vydání: 2024
Témata: 0301 basic medicine, hippocampus, Models, Neurological, Neurosciences. Biological psychiatry. Neuropsychiatry, episodic memory, encoding, Hippocampus, 03 medical and health sciences, 0302 clinical medicine, Memory, theta-gamma coupling, Imagination, Gamma Rhythm, Humans, Animals, Neural Networks, Computer, Theta Rhythm, retrieval, imagination, dreaming, RC321-571, Neuroscience
Popis: Gamma oscillations nested in a theta rhythm are observed in the hippocampus, where are assumed to play a role in sequential episodic memory, i.e., memorization and retrieval of events that unfold in time. In this work, we present an original neurocomputational model based on neural masses, which simulates the encoding of sequences of events in the hippocampus and subsequent retrieval by exploiting the theta-gamma code. The model is based on a three-layer structure in which individual Units oscillate with a gamma rhythm and code for individual features of an episode. The first layer (working memory in the prefrontal cortex) maintains a cue in memory until a new signal is presented. The second layer (CA3 cells) implements an auto-associative memory, exploiting excitatory and inhibitory plastic synapses to recover an entire episode from a single feature. Units in this layer are disinhibited by a theta rhythm from an external source (septum or Papez circuit). The third layer (CA1 cells) implements a hetero-associative net with the previous layer, able to recover a sequence of episodes from the first one. During an encoding phase, simulating high-acetylcholine levels, the network is trained with Hebbian (synchronizing) and anti-Hebbian (desynchronizing) rules. During retrieval (low-acetylcholine), the network can correctly recover sequences from an initial cue using gamma oscillations nested inside the theta rhythm. Moreover, in high noise, the network isolated from the environment simulates a mind-wandering condition, randomly replicating previous sequences. Interestingly, in a state simulating sleep, with increased noise and reduced synapses, the network can “dream” by creatively combining sequences, exploiting features shared by different episodes. Finally, an irrational behavior (erroneous superimposition of features in various episodes, like “delusion”) occurs after pathological-like reduction in fast inhibitory synapses. The model can represent a straightforward and innovative tool to help mechanistically understand the theta-gamma code in different mental states.
Druh dokumentu: Article
Other literature type
Popis souboru: application/pdf; application/vnd.openxmlformats-officedocument.wordprocessingml.document
ISSN: 1662-5110
DOI: 10.3389/fncir.2024.1326609
Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/38947492
https://doaj.org/article/a94479c440594ce9901c224ab4448712
https://hdl.handle.net/11585/997750
https://www.frontiersin.org/journals/neural-circuits/articles/10.3389/fncir.2024.1326609/full
https://doi.org/10.3389/fncir.2024.1326609
Rights: CC BY
Přístupové číslo: edsair.doi.dedup.....bfd11d2e68c77cbf7f4eabe773c30304
Databáze: OpenAIRE
Popis
Abstrakt:Gamma oscillations nested in a theta rhythm are observed in the hippocampus, where are assumed to play a role in sequential episodic memory, i.e., memorization and retrieval of events that unfold in time. In this work, we present an original neurocomputational model based on neural masses, which simulates the encoding of sequences of events in the hippocampus and subsequent retrieval by exploiting the theta-gamma code. The model is based on a three-layer structure in which individual Units oscillate with a gamma rhythm and code for individual features of an episode. The first layer (working memory in the prefrontal cortex) maintains a cue in memory until a new signal is presented. The second layer (CA3 cells) implements an auto-associative memory, exploiting excitatory and inhibitory plastic synapses to recover an entire episode from a single feature. Units in this layer are disinhibited by a theta rhythm from an external source (septum or Papez circuit). The third layer (CA1 cells) implements a hetero-associative net with the previous layer, able to recover a sequence of episodes from the first one. During an encoding phase, simulating high-acetylcholine levels, the network is trained with Hebbian (synchronizing) and anti-Hebbian (desynchronizing) rules. During retrieval (low-acetylcholine), the network can correctly recover sequences from an initial cue using gamma oscillations nested inside the theta rhythm. Moreover, in high noise, the network isolated from the environment simulates a mind-wandering condition, randomly replicating previous sequences. Interestingly, in a state simulating sleep, with increased noise and reduced synapses, the network can “dream” by creatively combining sequences, exploiting features shared by different episodes. Finally, an irrational behavior (erroneous superimposition of features in various episodes, like “delusion”) occurs after pathological-like reduction in fast inhibitory synapses. The model can represent a straightforward and innovative tool to help mechanistically understand the theta-gamma code in different mental states.
ISSN:16625110
DOI:10.3389/fncir.2024.1326609