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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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Effect of driving only Pyramidal cells or only Interneurons on ripple oscillations.(a) Schematic of the model, in which input current is delivered only to inhibitory interneurons. (b) Examples of model behavior. Top plot: input current. Middle plot: firing probabilities of interneurons (red) and pyramidal cells (black)–note that the pyramidal cells are not spiking during stimulation. Lower plots: LFP, wide band above and band-passed (50–350 Hz) below. (c) The ratio of interneuron receiving input current affects the size of filtered LFP. Note small amplitude of LFP when only a fraction of interneurons in activated. (d) Schematic of the model, in which only pyramidal cells receive input current. (e) Examples of model behavior. Same as in b. Note that reducing adaptation shows an increased duration of the high frequency event, which quickly (after less than 30ms) defaults to a gamma frequency oscillation. (f) Spike-frequency adaptation in pyramidal cells regulates the duration of HFO triggered by input only on pyramidal cells.
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pcbi.1004880.g007: Effect of driving only Pyramidal cells or only Interneurons on ripple oscillations.(a) Schematic of the model, in which input current is delivered only to inhibitory interneurons. (b) Examples of model behavior. Top plot: input current. Middle plot: firing probabilities of interneurons (red) and pyramidal cells (black)–note that the pyramidal cells are not spiking during stimulation. Lower plots: LFP, wide band above and band-passed (50–350 Hz) below. (c) The ratio of interneuron receiving input current affects the size of filtered LFP. Note small amplitude of LFP when only a fraction of interneurons in activated. (d) Schematic of the model, in which only pyramidal cells receive input current. (e) Examples of model behavior. Same as in b. Note that reducing adaptation shows an increased duration of the high frequency event, which quickly (after less than 30ms) defaults to a gamma frequency oscillation. (f) Spike-frequency adaptation in pyramidal cells regulates the duration of HFO triggered by input only on pyramidal cells.

Mentions: We started by delivering input current only to interneurons (Fig 7A): since simulations of a purely inhibitory network showed that current size controls synchrony among the interneurons (Fig 6A), the size of the current step was first kept the same as for the full model. In Fig 7B we show examples of spiking probabilities for two stimulation conditions (50% or 100% of all interneurons were stimulated) in the model when only interneurons received inputs. The inhibitory activity was still oscillatory, however all pyramidal cells were hyperpolarized, resulting in a field potential consisting of shunted currents. As a consequence, the LFP was much smaller than the one in the full model (compare Fig 7B with Fig 2D); LFP amplitude increased with the percent of interneurons recruited (Fig 7C). Specifically, in the case where 50% of interneurons were stimulated (which is likely much larger than any optogenetic stimulation in vivo), we found that the LFP amplitude was only about 10% of the LFP observed when both excitatory and inhibitory neurons received an input drive from CA3. Increasing the amplitude of current stimulation in the model led to even stronger shunting of pyramidal neurons and even smaller LFP amplitude. In comparing with experimental results, we emphasize that the reduction of the LFP amplitude in our model depends on shunting effect of inhibition on pyramidal neurons, which in actual in vivo experiments would depend on the cell geometry, location of excitatory and inhibitory synapses, and other factors that our minimal cell model cannot explicitly capture. Even in this simplified setting, we are able to show a qualitative match with the strong reduction of the amplitude of oscillations in the LFP. Thus, our model reveals that if only interneurons are driven by a light source, LFP oscillations will be very small in amplitude, regardless of the stimulation amplitude, to the point that they will be experimentally negligible, in agreement with in vivo results [21].


Hippocampal CA1 Ripples as Inhibitory Transients.

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

Effect of driving only Pyramidal cells or only Interneurons on ripple oscillations.(a) Schematic of the model, in which input current is delivered only to inhibitory interneurons. (b) Examples of model behavior. Top plot: input current. Middle plot: firing probabilities of interneurons (red) and pyramidal cells (black)–note that the pyramidal cells are not spiking during stimulation. Lower plots: LFP, wide band above and band-passed (50–350 Hz) below. (c) The ratio of interneuron receiving input current affects the size of filtered LFP. Note small amplitude of LFP when only a fraction of interneurons in activated. (d) Schematic of the model, in which only pyramidal cells receive input current. (e) Examples of model behavior. Same as in b. Note that reducing adaptation shows an increased duration of the high frequency event, which quickly (after less than 30ms) defaults to a gamma frequency oscillation. (f) Spike-frequency adaptation in pyramidal cells regulates the duration of HFO triggered by input only on pyramidal cells.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004880.g007: Effect of driving only Pyramidal cells or only Interneurons on ripple oscillations.(a) Schematic of the model, in which input current is delivered only to inhibitory interneurons. (b) Examples of model behavior. Top plot: input current. Middle plot: firing probabilities of interneurons (red) and pyramidal cells (black)–note that the pyramidal cells are not spiking during stimulation. Lower plots: LFP, wide band above and band-passed (50–350 Hz) below. (c) The ratio of interneuron receiving input current affects the size of filtered LFP. Note small amplitude of LFP when only a fraction of interneurons in activated. (d) Schematic of the model, in which only pyramidal cells receive input current. (e) Examples of model behavior. Same as in b. Note that reducing adaptation shows an increased duration of the high frequency event, which quickly (after less than 30ms) defaults to a gamma frequency oscillation. (f) Spike-frequency adaptation in pyramidal cells regulates the duration of HFO triggered by input only on pyramidal cells.
Mentions: We started by delivering input current only to interneurons (Fig 7A): since simulations of a purely inhibitory network showed that current size controls synchrony among the interneurons (Fig 6A), the size of the current step was first kept the same as for the full model. In Fig 7B we show examples of spiking probabilities for two stimulation conditions (50% or 100% of all interneurons were stimulated) in the model when only interneurons received inputs. The inhibitory activity was still oscillatory, however all pyramidal cells were hyperpolarized, resulting in a field potential consisting of shunted currents. As a consequence, the LFP was much smaller than the one in the full model (compare Fig 7B with Fig 2D); LFP amplitude increased with the percent of interneurons recruited (Fig 7C). Specifically, in the case where 50% of interneurons were stimulated (which is likely much larger than any optogenetic stimulation in vivo), we found that the LFP amplitude was only about 10% of the LFP observed when both excitatory and inhibitory neurons received an input drive from CA3. Increasing the amplitude of current stimulation in the model led to even stronger shunting of pyramidal neurons and even smaller LFP amplitude. In comparing with experimental results, we emphasize that the reduction of the LFP amplitude in our model depends on shunting effect of inhibition on pyramidal neurons, which in actual in vivo experiments would depend on the cell geometry, location of excitatory and inhibitory synapses, and other factors that our minimal cell model cannot explicitly capture. Even in this simplified setting, we are able to show a qualitative match with the strong reduction of the amplitude of oscillations in the LFP. Thus, our model reveals that if only interneurons are driven by a light source, LFP oscillations will be very small in amplitude, regardless of the stimulation amplitude, to the point that they will be experimentally negligible, in agreement with in vivo results [21].

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