Linking minimal and detailed models of CA1 microcircuits reveals how theta rhythms emerge and how their frequencies are controlled

Chatzikalymniou, Alexandra P.; Gumus, Melisa; Lunyov, Anton; Rich, Scott; Lefebvre, Jérémie; Skinner, Frances K.

doi:10.1101/2020.07.28.225557

Cited by 4 publications

(14 citation statements)

References 66 publications

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“…We consider this to be a third building block feature for theta rhythm generation. Further, for the model output to be consistent with experimental observations of excitatory postsynaptic current (EPSC) and inhibitory postsynaptic current (IPSC) amplitude ratios, we found that the connection probability from PV+ to PYR cells was required to be larger than from PYR to PV+ cells—a particular prediction that has been examined and found to be consistent with empirically derived connectivities (Chatzikalymniou et al, 2020 ).…”

Section: A Design Of Microcircuit Models That Produce Theta Rhythmssupporting

confidence: 81%

“…However, since a detailed, biophysical CA1 network model that includes PYR cells and eight different inhibitory cell types has been created (Bezaire et al, 2016 ), one can consider using it as a proxy for the actual biological system to start to explore this. We have done this by bringing together the described microcircuit model used herein and the detailed, full-scale CA1 microcircuit model, and examined how the theta network frequency produced by the detailed model depends on the net input received by the PYR cells (Chatzikalymniou et al, 2020 ). We found that the biologically detailed models strongly support this dependence and thus our proposed hypothesis for theta rhythm frequency control.…”

Section: Discussionmentioning

confidence: 99%

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A Hypothesis for Theta Rhythm Frequency Control in CA1 Microcircuits

Skinner

Rich

Lunyov

et al. 2021

Front. Neural Circuits

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Computational models of neural circuits with varying levels of biophysical detail have been generated in pursuit of an underlying mechanism explaining the ubiquitous hippocampal theta rhythm. However, within the theta rhythm are at least two types with distinct frequencies associated with different behavioral states, an aspect that must be considered in pursuit of these mechanistic explanations. Here, using our previously developed excitatory-inhibitory network models that generate theta rhythms, we investigate the robustness of theta generation to intrinsic neuronal variability by building a database of heterogeneous excitatory cells and implementing them in our microcircuit model. We specifically investigate the impact of three key “building block” features of the excitatory cell model that underlie our model design: these cells' rheobase, their capacity for post-inhibitory rebound, and their spike-frequency adaptation. We show that theta rhythms at various frequencies can arise dependent upon the combination of these building block features, and we find that the speed of these oscillations are dependent upon the excitatory cells' response to inhibitory drive, as encapsulated by their phase response curves. Taken together, these findings support a hypothesis for theta frequency control that includes two aspects: (i) an internal mechanism that stems from the building block features of excitatory cell dynamics; (ii) an external mechanism that we describe as “inhibition-based tuning” of excitatory cell firing. We propose that these mechanisms control theta rhythm frequencies and underlie their robustness.

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Section: A Design Of Microcircuit Models That Produce Theta Rhythmssupporting

confidence: 81%

Section: Discussionmentioning

confidence: 99%

A Hypothesis for Theta Rhythm Frequency Control in CA1 Microcircuits

Skinner

Rich

Lunyov

et al. 2021

Front. Neural Circuits

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“…As already noted above, the initiation of the theta rhythm is known to be due to the PYR cell population (Chatzikalymniou et al, 2021). The requirement of recurrent connectivity is shown in FIGURE 2C where connections between PYR cells are removed to reveal the loss of theta rhythms.…”

Section: Dissecting the Theta/gamma Fsm Circuitmentioning

confidence: 88%

“…We already know from our previous modeling work that theta rhythm expression is possible with a large enough number PYR cells that have some recurrent connectivity (Chatzikalymniou et al, 2021). In that work, we distinguished the PYR cell population, and not any of the inhibitory cell populations, as the theta rhythm initiators.…”

Section: Anatomy Of a Theta Cycle In The Full-scale Model (Fsm)mentioning

confidence: 99%

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Cholecystokinin-expressing (CCK+) basket cells are key controllers of theta-gamma coupled rhythms in the hippocampus

Chatzikalymniou

Sengupta

Lefebvre

et al. 2022

Preprint

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It has been shown that different inhibitory cell types underlie brain rhythmic and cross-frequency coupling output, but exactly how inhibitory cell types manifest their contributions to these outputs is far from clear. Brain rhythms and their couplings are functionally important in cognition and behaviour, and thus it is essential that we determine and understand these contributions. To do this, one needs simultaneous access to multiple cell types and population brain output in functionally relevant states. This is extremely challenging to do with experiments alone, and here we use mathematical models of different types to gain insight into the contributions of the different inhibitory cell types. We focus on theta and gamma rhythms in the hippocampus and use a previously developed detailed model to develop precise hypotheses of how these rhythms are generated and coupled. We find critical contributions by four different cell types - pyramidal cells, parvalbumin expressing (PV+) basket cells (BCs), cholecystokinin-expressing (CCK+) BCs and bistratified cells. We develop a reduced population rate model (PRM) based on these hypotheses and do extensive explorations and visualizations of the PRM to obtain testable predictions. We find that CCK+BCs exhibit a much higher degree of control relative to PV+BCs for theta or gamma rhythm dominance and thus their coupling, and that coupling from PV+BCs to CCK+BCs more strongly affects theta frequencies relative to coupling from CCK+BCs to PV+BCs. As it is now possible to target these specific inhibitory cell types in the behaving animal, experimentally testing these hypotheses and predictions would be possible. Moreover, our work shows that a variety of mathematical model types creates new insights that would not be revealed with a single model type.

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A hypothesis for theta rhythm frequency control in CA1 microcircuits

Skinner

Rich

Lunyov

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Computational models of neural circuits with varying levels of biophysical detail have been generated in pursuit of an underlying mechanism explaining the ubiquitous hippocampal theta rhythm. However, within the theta rhythm are at least two types with distinct frequencies associated with different behavioural states, an aspect that must be considered in pursuit of these mechanistic explanations. Here, using our previously developed excitatory-inhibitory network models that generate theta rhythms, we investigate the robustness of theta generation to intrinsic neuronal variability by building a database of heterogeneous excitatory cells and implementing them in our microcircuit model. We specifically investigate the impact of three key ‘building block’ features of the excitatory cell model that underlie our model design: these cells’ rheobase, their capacity for post-inhibitory rebound, and their spike-frequency adaptation. We show that theta rhythms at various frequencies can arise dependent upon the combination of these building block features, and we find that the speed of these oscillations are dependent upon the excitatory cells’ response to inhibitory drive, as encapsulated by their phase response curves. Taken together, these findings support a hypothesis for theta frequency control that includes two aspects: (i) an internal mechanism that stems from the building block features of excitatory cell dynamics; (ii) an external mechanism that we describe as ‘inhibition-based tuning’ of excitatory cell firing. We propose that these mechanisms control theta rhythm frequencies and underlie their robustness.

show abstract

Linking minimal and detailed models of CA1 microcircuits reveals how theta rhythms emerge and how their frequencies are controlled

Cited by 4 publications

References 66 publications

A Hypothesis for Theta Rhythm Frequency Control in CA1 Microcircuits

A Hypothesis for Theta Rhythm Frequency Control in CA1 Microcircuits

Cholecystokinin-expressing (CCK+) basket cells are key controllers of theta-gamma coupled rhythms in the hippocampus

A hypothesis for theta rhythm frequency control in CA1 microcircuits

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