Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity.
Bottom-up models of functionally relevant patterns of neural activity provide an explicit link between neuronal dynamics and computation. A prime example of functional activity patterns are propagating bursts of place-cell activities called hippocampal replay, which is critical for memory consolidat...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-12-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1010809 |
| _version_ | 1811167268905680896 |
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| author | Bastian Pietras Valentin Schmutz Tilo Schwalger |
| author_facet | Bastian Pietras Valentin Schmutz Tilo Schwalger |
| author_sort | Bastian Pietras |
| collection | DOAJ |
| description | Bottom-up models of functionally relevant patterns of neural activity provide an explicit link between neuronal dynamics and computation. A prime example of functional activity patterns are propagating bursts of place-cell activities called hippocampal replay, which is critical for memory consolidation. The sudden and repeated occurrences of these burst states during ongoing neural activity suggest metastable neural circuit dynamics. As metastability has been attributed to noise and/or slow fatigue mechanisms, we propose a concise mesoscopic model which accounts for both. Crucially, our model is bottom-up: it is analytically derived from the dynamics of finite-size networks of Linear-Nonlinear Poisson neurons with short-term synaptic depression. As such, noise is explicitly linked to stochastic spiking and network size, and fatigue is explicitly linked to synaptic dynamics. To derive the mesoscopic model, we first consider a homogeneous spiking neural network and follow the temporal coarse-graining approach of Gillespie to obtain a "chemical Langevin equation", which can be naturally interpreted as a stochastic neural mass model. The Langevin equation is computationally inexpensive to simulate and enables a thorough study of metastable dynamics in classical setups (population spikes and Up-Down-states dynamics) by means of phase-plane analysis. An extension of the Langevin equation for small network sizes is also presented. The stochastic neural mass model constitutes the basic component of our mesoscopic model for replay. We show that the mesoscopic model faithfully captures the statistical structure of individual replayed trajectories in microscopic simulations and in previously reported experimental data. Moreover, compared to the deterministic Romani-Tsodyks model of place-cell dynamics, it exhibits a higher level of variability regarding order, direction and timing of replayed trajectories, which seems biologically more plausible and could be functionally desirable. This variability is the product of a new dynamical regime where metastability emerges from a complex interplay between finite-size fluctuations and local fatigue. |
| first_indexed | 2024-04-10T16:06:29Z |
| format | Article |
| id | doaj.art-df391135d82f49aeb8bb8be848e498b5 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-10T16:06:29Z |
| publishDate | 2022-12-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-df391135d82f49aeb8bb8be848e498b52023-02-10T05:30:47ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-12-011812e101080910.1371/journal.pcbi.1010809Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity.Bastian PietrasValentin SchmutzTilo SchwalgerBottom-up models of functionally relevant patterns of neural activity provide an explicit link between neuronal dynamics and computation. A prime example of functional activity patterns are propagating bursts of place-cell activities called hippocampal replay, which is critical for memory consolidation. The sudden and repeated occurrences of these burst states during ongoing neural activity suggest metastable neural circuit dynamics. As metastability has been attributed to noise and/or slow fatigue mechanisms, we propose a concise mesoscopic model which accounts for both. Crucially, our model is bottom-up: it is analytically derived from the dynamics of finite-size networks of Linear-Nonlinear Poisson neurons with short-term synaptic depression. As such, noise is explicitly linked to stochastic spiking and network size, and fatigue is explicitly linked to synaptic dynamics. To derive the mesoscopic model, we first consider a homogeneous spiking neural network and follow the temporal coarse-graining approach of Gillespie to obtain a "chemical Langevin equation", which can be naturally interpreted as a stochastic neural mass model. The Langevin equation is computationally inexpensive to simulate and enables a thorough study of metastable dynamics in classical setups (population spikes and Up-Down-states dynamics) by means of phase-plane analysis. An extension of the Langevin equation for small network sizes is also presented. The stochastic neural mass model constitutes the basic component of our mesoscopic model for replay. We show that the mesoscopic model faithfully captures the statistical structure of individual replayed trajectories in microscopic simulations and in previously reported experimental data. Moreover, compared to the deterministic Romani-Tsodyks model of place-cell dynamics, it exhibits a higher level of variability regarding order, direction and timing of replayed trajectories, which seems biologically more plausible and could be functionally desirable. This variability is the product of a new dynamical regime where metastability emerges from a complex interplay between finite-size fluctuations and local fatigue.https://doi.org/10.1371/journal.pcbi.1010809 |
| spellingShingle | Bastian Pietras Valentin Schmutz Tilo Schwalger Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity. PLoS Computational Biology |
| title | Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity. |
| title_full | Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity. |
| title_fullStr | Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity. |
| title_full_unstemmed | Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity. |
| title_short | Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity. |
| title_sort | mesoscopic description of hippocampal replay and metastability in spiking neural networks with short term plasticity |
| url | https://doi.org/10.1371/journal.pcbi.1010809 |
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