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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 depend on input strength and noise size.(a, b) Effect of the input amplitude on oscillation properties. (a) Histograms of firing probabilities for a model shown in Fig 5, only with a DC step half the size (350 pA). The time axes is binned in 1ms bins. Note that synchrony is strongly affected, and the transient is composed by a drastically reduced number of cycles (b). (c, d) Effect of noise and strength of inhibition on transient properties. (c) Histograms of firing probabilities, for changing synaptic strength (scaled by α) and noise standard deviation (scaled by σ). The time axes is binned in 1ms bins. (d) Top panel: number of cycles composing the transient event. Bottom panel: network frequency.
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pcbi.1004880.g006: Transients in inhibitory network depend on input strength and noise size.(a, b) Effect of the input amplitude on oscillation properties. (a) Histograms of firing probabilities for a model shown in Fig 5, only with a DC step half the size (350 pA). The time axes is binned in 1ms bins. Note that synchrony is strongly affected, and the transient is composed by a drastically reduced number of cycles (b). (c, d) Effect of noise and strength of inhibition on transient properties. (c) Histograms of firing probabilities, for changing synaptic strength (scaled by α) and noise standard deviation (scaled by σ). The time axes is binned in 1ms bins. (d) Top panel: number of cycles composing the transient event. Bottom panel: network frequency.

Mentions: To further explore this point, we studied how the transients organize when the initial step of the input current is halved (Fig 6A). In that case, much fewer interneurons were recruited to the initial synchronous population (note the scale of firing probability on the y-axes of Fig 6A). Inhibitory currents still affected oscillations, but the transient lasted very few cycles (Fig 6B) and peaks were smaller. We concluded that if the initial current step failed to synchronize a large enough population of neurons, the resulting slower oscillation faded in only 2 to 3 cycles. Hence, this network shows an all-or-none property: a smaller step of input that could in principle recruit lower frequency oscillations cannot recruit a transient at all. In fact, it takes an input of sufficient size to generate a transient that lasts enough cycles and recruits enough neurons for a fast oscillation to be visible in the LFP.


Hippocampal CA1 Ripples as Inhibitory Transients.

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

Transients in inhibitory network depend on input strength and noise size.(a, b) Effect of the input amplitude on oscillation properties. (a) Histograms of firing probabilities for a model shown in Fig 5, only with a DC step half the size (350 pA). The time axes is binned in 1ms bins. Note that synchrony is strongly affected, and the transient is composed by a drastically reduced number of cycles (b). (c, d) Effect of noise and strength of inhibition on transient properties. (c) Histograms of firing probabilities, for changing synaptic strength (scaled by α) and noise standard deviation (scaled by σ). The time axes is binned in 1ms bins. (d) Top panel: number of cycles composing the transient event. Bottom panel: network frequency.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004880.g006: Transients in inhibitory network depend on input strength and noise size.(a, b) Effect of the input amplitude on oscillation properties. (a) Histograms of firing probabilities for a model shown in Fig 5, only with a DC step half the size (350 pA). The time axes is binned in 1ms bins. Note that synchrony is strongly affected, and the transient is composed by a drastically reduced number of cycles (b). (c, d) Effect of noise and strength of inhibition on transient properties. (c) Histograms of firing probabilities, for changing synaptic strength (scaled by α) and noise standard deviation (scaled by σ). The time axes is binned in 1ms bins. (d) Top panel: number of cycles composing the transient event. Bottom panel: network frequency.
Mentions: To further explore this point, we studied how the transients organize when the initial step of the input current is halved (Fig 6A). In that case, much fewer interneurons were recruited to the initial synchronous population (note the scale of firing probability on the y-axes of Fig 6A). Inhibitory currents still affected oscillations, but the transient lasted very few cycles (Fig 6B) and peaks were smaller. We concluded that if the initial current step failed to synchronize a large enough population of neurons, the resulting slower oscillation faded in only 2 to 3 cycles. Hence, this network shows an all-or-none property: a smaller step of input that could in principle recruit lower frequency oscillations cannot recruit a transient at all. In fact, it takes an input of sufficient size to generate a transient that lasts enough cycles and recruits enough neurons for a fast oscillation to be visible in the LFP.

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