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Network models provide insights into how oriens-lacunosum-moleculare and bistratified cell interactions influence the power of local hippocampal CA1 theta oscillations.

Ferguson KA, Huh CY, Amilhon B, Manseau F, Williams S, Skinner FK - Front Syst Neurosci (2015)

Bottom Line: We found that our models operate in regimes that could be distinguished by whether OLM cells minimally or strongly affected the power of network theta oscillations due to balances that, respectively, allow compensatory effects or not.Inactivation of OLM cells could result in no change or even an increase in theta power.We predict that the dis-inhibitory effect of OLM cells to BiCs to pyramidal cell interactions plays a critical role in the resulting power of network theta oscillations.

View Article: PubMed Central - PubMed

Affiliation: Division of Fundamental Neurobiology, Toronto Western Research Institute, University Health Network Toronto, ON, Canada ; Department of Physiology, University of Toronto Toronto, ON, Canada.

ABSTRACT
Hippocampal theta is a 4-12 Hz rhythm associated with episodic memory, and although it has been studied extensively, the cellular mechanisms underlying its generation are unclear. The complex interactions between different interneuron types, such as those between oriens-lacunosum-moleculare (OLM) interneurons and bistratified cells (BiCs), make their contribution to network rhythms difficult to determine experimentally. We created network models that are tied to experimental work at both cellular and network levels to explore how these interneuron interactions affect the power of local oscillations. Our cellular models were constrained with properties from patch clamp recordings in the CA1 region of an intact hippocampus preparation in vitro. Our network models are composed of three different types of interneurons: parvalbumin-positive (PV+) basket and axo-axonic cells (BC/AACs), PV+ BiCs, and somatostatin-positive OLM cells. Also included is a spatially extended pyramidal cell model to allow for a simplified local field potential representation, as well as experimentally-constrained, theta frequency synaptic inputs to the interneurons. The network size, connectivity, and synaptic properties were constrained with experimental data. To determine how the interactions between OLM cells and BiCs could affect local theta power, we explored how the number of OLM-BiC connections and connection strength affected local theta power. We found that our models operate in regimes that could be distinguished by whether OLM cells minimally or strongly affected the power of network theta oscillations due to balances that, respectively, allow compensatory effects or not. Inactivation of OLM cells could result in no change or even an increase in theta power. We predict that the dis-inhibitory effect of OLM cells to BiCs to pyramidal cell interactions plays a critical role in the resulting power of network theta oscillations. Overall, our network models reveal a dynamic interplay between different classes of interneurons in influencing local theta power.

No MeSH data available.


Left: A schematic of the inhibitory network model showing that the strength of the connections to the passive PYR model is increased with distance from the soma, in order to create similar input strengths at the level of the soma for all three inputs. Right: Distinct regimes in which OLM cells minimally or strongly affect the power of network oscillations remain when the strength at which each cell type influences the passive PYR model is varied. The strength of connections to our passive multi-compartment PYR model is increased with distance, such that each IPSC to the PYR is equal in size (as opposed to Figure 11A in which each connection has equal weights, and thus IPSCs decrease with distance). Here, populations are randomly connected with a probability of 0.21 for OLM-BiC connections, and 0.13 for BiC-OLM connections, as in Figure 11A.
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Figure 14: Left: A schematic of the inhibitory network model showing that the strength of the connections to the passive PYR model is increased with distance from the soma, in order to create similar input strengths at the level of the soma for all three inputs. Right: Distinct regimes in which OLM cells minimally or strongly affect the power of network oscillations remain when the strength at which each cell type influences the passive PYR model is varied. The strength of connections to our passive multi-compartment PYR model is increased with distance, such that each IPSC to the PYR is equal in size (as opposed to Figure 11A in which each connection has equal weights, and thus IPSCs decrease with distance). Here, populations are randomly connected with a probability of 0.21 for OLM-BiC connections, and 0.13 for BiC-OLM connections, as in Figure 11A.

Mentions: The network model simulations so far have been done with similar synaptic weights between the different interneuron populations and the pyramidal cell (see Table 3). As we do not know precisely how each cell type affects pyramidal cells, and thus our LFP representation, we also consider a varied distribution of strengths in which the cell populations affect the passive PYR model. Specifically, we consider that the strength of connections to our passive PYR is increased with distance from the soma (giving weights of OLM-passive PYR: 0.00067, BiC-passive PYR: 0.00044, BC/AAC-passive PYR: 0.00038), such that each post-synaptic potential onto the passive PYR model is equal in size. This is different from the above simulations, in which each cell population affected the passive PYR model with equal connection strength (giving weights of OLM-passive PYR: 0.00067, BiC-passive PYR: 0.00067, BC/AAC-passive PYR: 0.00067). We find that although overall there is a decrease in LFP power, high and low theta power regimes are still present. Compare Figure 14 with Figure 11A. The full set of connection probabilities are shown in Figure S3. Given the described compensatory effect this makes sense. That is, with the varied synaptic weights in which the further away ones (from OLM cells) are stronger than closer ones, OLM cells' direct influence on PYRs is stronger than for BiCs, and so it would be less likely that a compensatory effect can occur considering the same ranges of OLM-BiC and BiC-OLM cell connection strengths (compare Figure S1 and Figure S3 where less red regions in Figure S3 are apparent).


Network models provide insights into how oriens-lacunosum-moleculare and bistratified cell interactions influence the power of local hippocampal CA1 theta oscillations.

Ferguson KA, Huh CY, Amilhon B, Manseau F, Williams S, Skinner FK - Front Syst Neurosci (2015)

Left: A schematic of the inhibitory network model showing that the strength of the connections to the passive PYR model is increased with distance from the soma, in order to create similar input strengths at the level of the soma for all three inputs. Right: Distinct regimes in which OLM cells minimally or strongly affect the power of network oscillations remain when the strength at which each cell type influences the passive PYR model is varied. The strength of connections to our passive multi-compartment PYR model is increased with distance, such that each IPSC to the PYR is equal in size (as opposed to Figure 11A in which each connection has equal weights, and thus IPSCs decrease with distance). Here, populations are randomly connected with a probability of 0.21 for OLM-BiC connections, and 0.13 for BiC-OLM connections, as in Figure 11A.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4528165&req=5

Figure 14: Left: A schematic of the inhibitory network model showing that the strength of the connections to the passive PYR model is increased with distance from the soma, in order to create similar input strengths at the level of the soma for all three inputs. Right: Distinct regimes in which OLM cells minimally or strongly affect the power of network oscillations remain when the strength at which each cell type influences the passive PYR model is varied. The strength of connections to our passive multi-compartment PYR model is increased with distance, such that each IPSC to the PYR is equal in size (as opposed to Figure 11A in which each connection has equal weights, and thus IPSCs decrease with distance). Here, populations are randomly connected with a probability of 0.21 for OLM-BiC connections, and 0.13 for BiC-OLM connections, as in Figure 11A.
Mentions: The network model simulations so far have been done with similar synaptic weights between the different interneuron populations and the pyramidal cell (see Table 3). As we do not know precisely how each cell type affects pyramidal cells, and thus our LFP representation, we also consider a varied distribution of strengths in which the cell populations affect the passive PYR model. Specifically, we consider that the strength of connections to our passive PYR is increased with distance from the soma (giving weights of OLM-passive PYR: 0.00067, BiC-passive PYR: 0.00044, BC/AAC-passive PYR: 0.00038), such that each post-synaptic potential onto the passive PYR model is equal in size. This is different from the above simulations, in which each cell population affected the passive PYR model with equal connection strength (giving weights of OLM-passive PYR: 0.00067, BiC-passive PYR: 0.00067, BC/AAC-passive PYR: 0.00067). We find that although overall there is a decrease in LFP power, high and low theta power regimes are still present. Compare Figure 14 with Figure 11A. The full set of connection probabilities are shown in Figure S3. Given the described compensatory effect this makes sense. That is, with the varied synaptic weights in which the further away ones (from OLM cells) are stronger than closer ones, OLM cells' direct influence on PYRs is stronger than for BiCs, and so it would be less likely that a compensatory effect can occur considering the same ranges of OLM-BiC and BiC-OLM cell connection strengths (compare Figure S1 and Figure S3 where less red regions in Figure S3 are apparent).

Bottom Line: We found that our models operate in regimes that could be distinguished by whether OLM cells minimally or strongly affected the power of network theta oscillations due to balances that, respectively, allow compensatory effects or not.Inactivation of OLM cells could result in no change or even an increase in theta power.We predict that the dis-inhibitory effect of OLM cells to BiCs to pyramidal cell interactions plays a critical role in the resulting power of network theta oscillations.

View Article: PubMed Central - PubMed

Affiliation: Division of Fundamental Neurobiology, Toronto Western Research Institute, University Health Network Toronto, ON, Canada ; Department of Physiology, University of Toronto Toronto, ON, Canada.

ABSTRACT
Hippocampal theta is a 4-12 Hz rhythm associated with episodic memory, and although it has been studied extensively, the cellular mechanisms underlying its generation are unclear. The complex interactions between different interneuron types, such as those between oriens-lacunosum-moleculare (OLM) interneurons and bistratified cells (BiCs), make their contribution to network rhythms difficult to determine experimentally. We created network models that are tied to experimental work at both cellular and network levels to explore how these interneuron interactions affect the power of local oscillations. Our cellular models were constrained with properties from patch clamp recordings in the CA1 region of an intact hippocampus preparation in vitro. Our network models are composed of three different types of interneurons: parvalbumin-positive (PV+) basket and axo-axonic cells (BC/AACs), PV+ BiCs, and somatostatin-positive OLM cells. Also included is a spatially extended pyramidal cell model to allow for a simplified local field potential representation, as well as experimentally-constrained, theta frequency synaptic inputs to the interneurons. The network size, connectivity, and synaptic properties were constrained with experimental data. To determine how the interactions between OLM cells and BiCs could affect local theta power, we explored how the number of OLM-BiC connections and connection strength affected local theta power. We found that our models operate in regimes that could be distinguished by whether OLM cells minimally or strongly affected the power of network theta oscillations due to balances that, respectively, allow compensatory effects or not. Inactivation of OLM cells could result in no change or even an increase in theta power. We predict that the dis-inhibitory effect of OLM cells to BiCs to pyramidal cell interactions plays a critical role in the resulting power of network theta oscillations. Overall, our network models reveal a dynamic interplay between different classes of interneurons in influencing local theta power.

No MeSH data available.