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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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Transients in inhibitory network explain ripple mechanism.(a) Schematic representation of the model considered, only composed of inhibitory neurons. (b) Histograms of spike probabilities, with step current input (blue). The time axes is binned in 1ms bins. Changing parameters: decay time scale of inhibitory synapses (τ) and scaling (α) of maximal inhibitory synapses conductance. (c) Transient duration (in ms) as a function of α and τ. (d) Transient frequency. (e) Number of peak cycles in the transient, before the distribution flattens (see Materials and Methods).
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pcbi.1004880.g005: Transients in inhibitory network explain ripple mechanism.(a) Schematic representation of the model considered, only composed of inhibitory neurons. (b) Histograms of spike probabilities, with step current input (blue). The time axes is binned in 1ms bins. Changing parameters: decay time scale of inhibitory synapses (τ) and scaling (α) of maximal inhibitory synapses conductance. (c) Transient duration (in ms) as a function of α and τ. (d) Transient frequency. (e) Number of peak cycles in the transient, before the distribution flattens (see Materials and Methods).

Mentions: The step of DC current delivered to all inhibitory interneurons amounted to the same value as the peak input current from CA3 to interneurons in the full model (700 pA). To construct the profile of spiking probability in response to the current step, we run 100 simulations for each parameter set, and built the cumulative histogram of probability of spiking as a function of time. Fig 5A shows a schematic of the reduced network, while Fig 5B shows that the common input step (at time = 1s) initially organized the network as indicated by the rhythmic oscillations of the population activity. The amplitude of these oscillations progressively decreased, indicating a transient nature of the high-frequency activity, which was de-synchronized by the intrinsic noise. Eventually, the population firing rate stopped oscillating and settled to a mean constant value, which depended on the size of the current step. This behavior of the isolated interneuron population is consistent with data [46]: in vitro optogenetic experiments (albeit in area CA3) show that activating only parvalbumin positive interneurons with a step of light, of duration up to 50ms, results in an average oscillatory behavior in which the peaks are progressively attenuated in time.


Hippocampal CA1 Ripples as Inhibitory Transients.

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

Transients in inhibitory network explain ripple mechanism.(a) Schematic representation of the model considered, only composed of inhibitory neurons. (b) Histograms of spike probabilities, with step current input (blue). The time axes is binned in 1ms bins. Changing parameters: decay time scale of inhibitory synapses (τ) and scaling (α) of maximal inhibitory synapses conductance. (c) Transient duration (in ms) as a function of α and τ. (d) Transient frequency. (e) Number of peak cycles in the transient, before the distribution flattens (see Materials and Methods).
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004880.g005: Transients in inhibitory network explain ripple mechanism.(a) Schematic representation of the model considered, only composed of inhibitory neurons. (b) Histograms of spike probabilities, with step current input (blue). The time axes is binned in 1ms bins. Changing parameters: decay time scale of inhibitory synapses (τ) and scaling (α) of maximal inhibitory synapses conductance. (c) Transient duration (in ms) as a function of α and τ. (d) Transient frequency. (e) Number of peak cycles in the transient, before the distribution flattens (see Materials and Methods).
Mentions: The step of DC current delivered to all inhibitory interneurons amounted to the same value as the peak input current from CA3 to interneurons in the full model (700 pA). To construct the profile of spiking probability in response to the current step, we run 100 simulations for each parameter set, and built the cumulative histogram of probability of spiking as a function of time. Fig 5A shows a schematic of the reduced network, while Fig 5B shows that the common input step (at time = 1s) initially organized the network as indicated by the rhythmic oscillations of the population activity. The amplitude of these oscillations progressively decreased, indicating a transient nature of the high-frequency activity, which was de-synchronized by the intrinsic noise. Eventually, the population firing rate stopped oscillating and settled to a mean constant value, which depended on the size of the current step. This behavior of the isolated interneuron population is consistent with data [46]: in vitro optogenetic experiments (albeit in area CA3) show that activating only parvalbumin positive interneurons with a step of light, of duration up to 50ms, results in an average oscillatory behavior in which the peaks are progressively attenuated in time.

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