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Hippocampal CA1 Ripples as Inhibitory Transients.

Malerba P, Krishnan GP, Fellous JM, Bazhenov M - PLoS Comput. Biol. (2016)

Bottom Line: Memories are stored and consolidated as a result of a dialogue between the hippocampus and cortex during sleep.We found that noise-induced loss of synchrony among CA1 interneurons dynamically constrains individual ripple duration.Our study proposes a novel mechanism of hippocampal ripple generation consistent with a broad range of experimental data, and highlights the role of noise in regulating the duration of input-driven oscillatory spiking in an inhibitory network.

View Article: PubMed Central - PubMed

Affiliation: Department of Cell Biology and Neuroscience, University of California Riverside, Riverside, California, United States of America.

ABSTRACT
Memories are stored and consolidated as a result of a dialogue between the hippocampus and cortex during sleep. Neurons active during behavior reactivate in both structures during sleep, in conjunction with characteristic brain oscillations that may form the neural substrate of memory consolidation. In the hippocampus, replay occurs within sharp wave-ripples: short bouts of high-frequency activity in area CA1 caused by excitatory activation from area CA3. In this work, we develop a computational model of ripple generation, motivated by in vivo rat data showing that ripples have a broad frequency distribution, exponential inter-arrival times and yet highly non-variable durations. Our study predicts that ripples are not persistent oscillations but result from a transient network behavior, induced by input from CA3, in which the high frequency synchronous firing of perisomatic interneurons does not depend on the time scale of synaptic inhibition. We found that noise-induced loss of synchrony among CA1 interneurons dynamically constrains individual ripple duration. Our study proposes a novel mechanism of hippocampal ripple generation consistent with a broad range of experimental data, and highlights the role of noise in regulating the duration of input-driven oscillatory spiking in an inhibitory network.

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Computational model of CA1 ripples.(a) Model schematic. Parameters: see Materials and Methods. (b) Example voltage traces of pyramidal cell (black) and interneuron (red). (c) Distributions of synaptic weights. (d) A simulation, described from the top. Traces of current inputs from CA3 to pyramidal cells (black) and to inhibitory interneurons (red). Rastergram where cells are stacked along the y-axis and crosses represent spikes of pyramidal cells (black) and interneurons (red). Probability to spike for pyramidal cells (black) and interneurons (red) in 1ms bins. LFP wide-band, and band-passed filtered (50-350Hz).
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pcbi.1004880.g002: Computational model of CA1 ripples.(a) Model schematic. Parameters: see Materials and Methods. (b) Example voltage traces of pyramidal cell (black) and interneuron (red). (c) Distributions of synaptic weights. (d) A simulation, described from the top. Traces of current inputs from CA3 to pyramidal cells (black) and to inhibitory interneurons (red). Rastergram where cells are stacked along the y-axis and crosses represent spikes of pyramidal cells (black) and interneurons (red). Probability to spike for pyramidal cells (black) and interneurons (red) in 1ms bins. LFP wide-band, and band-passed filtered (50-350Hz).

Mentions: We reasoned that our model needed to represent a small patch of CA1, reached by strong incoming excitation from CA3, so that the activity of all cells we modeled would be picked up by a single electrode. Given that ripples are measured in the pyramidal layer, we chose to model only pyramidal cells and parvalbumin positive basket cells. In fact, other interneuron types that might be active during sharp waves impinge on pyramidal cells at different layers [30, 31], which do not show ripple frequency oscillations. Hence, they might have a modulatory influence on the overall ripple appearance but are not in a position to set the pace of ripple frequency. The network consisted of 800 pyramidal cells and 160 interneurons, a ratio in agreement with CA1 anatomy [32], and we used all-to-all connectivity, with the exception of the synapses between pyramidal cells, which were few and much weaker than all others [33], consistent with CA1 anatomy [34]. Fig 2 shows a network representation (Fig 2A), example traces from a pyramidal cell and an interneuron (Fig 2B), and the distributions of synaptic weights (Fig 2C) in the model.


Hippocampal CA1 Ripples as Inhibitory Transients.

Malerba P, Krishnan GP, Fellous JM, Bazhenov M - PLoS Comput. Biol. (2016)

Computational model of CA1 ripples.(a) Model schematic. Parameters: see Materials and Methods. (b) Example voltage traces of pyramidal cell (black) and interneuron (red). (c) Distributions of synaptic weights. (d) A simulation, described from the top. Traces of current inputs from CA3 to pyramidal cells (black) and to inhibitory interneurons (red). Rastergram where cells are stacked along the y-axis and crosses represent spikes of pyramidal cells (black) and interneurons (red). Probability to spike for pyramidal cells (black) and interneurons (red) in 1ms bins. LFP wide-band, and band-passed filtered (50-350Hz).
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4836732&req=5

pcbi.1004880.g002: Computational model of CA1 ripples.(a) Model schematic. Parameters: see Materials and Methods. (b) Example voltage traces of pyramidal cell (black) and interneuron (red). (c) Distributions of synaptic weights. (d) A simulation, described from the top. Traces of current inputs from CA3 to pyramidal cells (black) and to inhibitory interneurons (red). Rastergram where cells are stacked along the y-axis and crosses represent spikes of pyramidal cells (black) and interneurons (red). Probability to spike for pyramidal cells (black) and interneurons (red) in 1ms bins. LFP wide-band, and band-passed filtered (50-350Hz).
Mentions: We reasoned that our model needed to represent a small patch of CA1, reached by strong incoming excitation from CA3, so that the activity of all cells we modeled would be picked up by a single electrode. Given that ripples are measured in the pyramidal layer, we chose to model only pyramidal cells and parvalbumin positive basket cells. In fact, other interneuron types that might be active during sharp waves impinge on pyramidal cells at different layers [30, 31], which do not show ripple frequency oscillations. Hence, they might have a modulatory influence on the overall ripple appearance but are not in a position to set the pace of ripple frequency. The network consisted of 800 pyramidal cells and 160 interneurons, a ratio in agreement with CA1 anatomy [32], and we used all-to-all connectivity, with the exception of the synapses between pyramidal cells, which were few and much weaker than all others [33], consistent with CA1 anatomy [34]. Fig 2 shows a network representation (Fig 2A), example traces from a pyramidal cell and an interneuron (Fig 2B), and the distributions of synaptic weights (Fig 2C) in the model.

Bottom Line: Memories are stored and consolidated as a result of a dialogue between the hippocampus and cortex during sleep.We found that noise-induced loss of synchrony among CA1 interneurons dynamically constrains individual ripple duration.Our study proposes a novel mechanism of hippocampal ripple generation consistent with a broad range of experimental data, and highlights the role of noise in regulating the duration of input-driven oscillatory spiking in an inhibitory network.

View Article: PubMed Central - PubMed

Affiliation: Department of Cell Biology and Neuroscience, University of California Riverside, Riverside, California, United States of America.

ABSTRACT
Memories are stored and consolidated as a result of a dialogue between the hippocampus and cortex during sleep. Neurons active during behavior reactivate in both structures during sleep, in conjunction with characteristic brain oscillations that may form the neural substrate of memory consolidation. In the hippocampus, replay occurs within sharp wave-ripples: short bouts of high-frequency activity in area CA1 caused by excitatory activation from area CA3. In this work, we develop a computational model of ripple generation, motivated by in vivo rat data showing that ripples have a broad frequency distribution, exponential inter-arrival times and yet highly non-variable durations. Our study predicts that ripples are not persistent oscillations but result from a transient network behavior, induced by input from CA3, in which the high frequency synchronous firing of perisomatic interneurons does not depend on the time scale of synaptic inhibition. We found that noise-induced loss of synchrony among CA1 interneurons dynamically constrains individual ripple duration. Our study proposes a novel mechanism of hippocampal ripple generation consistent with a broad range of experimental data, and highlights the role of noise in regulating the duration of input-driven oscillatory spiking in an inhibitory network.

Show MeSH
Related in: MedlinePlus