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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.


The interactions between OLM and BiC affect the network power in a complex manner. Here, a single BiC-OLM conductance (gBiC, OLM = 3.25 nS) is considered, whereas the OLM-BiC conductances varies, and representative LFP traces are shown. In these simulations, the populations were randomly connected with a probability of 0.19 for OLM-BiC connections, and 0.12 for BiC-OLM connections, and each cell type is connected to our passive PYR model with equal connection strength.
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Figure 10: The interactions between OLM and BiC affect the network power in a complex manner. Here, a single BiC-OLM conductance (gBiC, OLM = 3.25 nS) is considered, whereas the OLM-BiC conductances varies, and representative LFP traces are shown. In these simulations, the populations were randomly connected with a probability of 0.19 for OLM-BiC connections, and 0.12 for BiC-OLM connections, and each cell type is connected to our passive PYR model with equal connection strength.

Mentions: To consider this difference more closely, we considered the distribution of firing of each cell population, and found that the amount of firing and timing of the individual cell populations are factors affecting the power of the network theta oscillations. For the example networks shown in Figure 8, the respective spike distributions of each population are shown in Figure 9 for one of the cycles. It is apparent that the number of spikes and the phase of firing differs between these two networks, particularly for the OLM cell population (this was the case for other cycles for this parameter set). These aspects affect the inputs into the passive PYR model, changing the overall LFP power. Thus, our models indicate that the connection strengths between the two cell populations can strongly influence the amount of firing and their timing (although timing differences are not as large), which ultimately affects network power. Other examples of high and low power also show this difference, but the difference varies for different parameter sets. Precisely how the OLM and BiC populations affect the network power is not straightforward, and depends on the complex balance of interactions between them. An example of how the LFP representation can vary across the OLM-BiC conductance is shown in Figure 10, where the color represents the peak LFP power, and three examples of actual LFP representations are shown. As can be seen, when OLM-BiC conductance strengths increase from 1 nS, there is an increase and then a decrease in power. This suggests that interpreting changes in synaptic strengths (e.g., during plasticity) could be difficult as either increases or decreases in power could be expected. However, it is important to note that the theta power is a metric of the peak LFP spectral power, and it does not capture shape details of the LFP representation. Further, although our LFP representation is able to capture spatio-temporal aspects due to using a multi-compartment PYR model, it is still a simplistic representation relative to the experimental situation.


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)

The interactions between OLM and BiC affect the network power in a complex manner. Here, a single BiC-OLM conductance (gBiC, OLM = 3.25 nS) is considered, whereas the OLM-BiC conductances varies, and representative LFP traces are shown. In these simulations, the populations were randomly connected with a probability of 0.19 for OLM-BiC connections, and 0.12 for BiC-OLM connections, and each cell type is connected to our passive PYR model with equal connection strength.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 10: The interactions between OLM and BiC affect the network power in a complex manner. Here, a single BiC-OLM conductance (gBiC, OLM = 3.25 nS) is considered, whereas the OLM-BiC conductances varies, and representative LFP traces are shown. In these simulations, the populations were randomly connected with a probability of 0.19 for OLM-BiC connections, and 0.12 for BiC-OLM connections, and each cell type is connected to our passive PYR model with equal connection strength.
Mentions: To consider this difference more closely, we considered the distribution of firing of each cell population, and found that the amount of firing and timing of the individual cell populations are factors affecting the power of the network theta oscillations. For the example networks shown in Figure 8, the respective spike distributions of each population are shown in Figure 9 for one of the cycles. It is apparent that the number of spikes and the phase of firing differs between these two networks, particularly for the OLM cell population (this was the case for other cycles for this parameter set). These aspects affect the inputs into the passive PYR model, changing the overall LFP power. Thus, our models indicate that the connection strengths between the two cell populations can strongly influence the amount of firing and their timing (although timing differences are not as large), which ultimately affects network power. Other examples of high and low power also show this difference, but the difference varies for different parameter sets. Precisely how the OLM and BiC populations affect the network power is not straightforward, and depends on the complex balance of interactions between them. An example of how the LFP representation can vary across the OLM-BiC conductance is shown in Figure 10, where the color represents the peak LFP power, and three examples of actual LFP representations are shown. As can be seen, when OLM-BiC conductance strengths increase from 1 nS, there is an increase and then a decrease in power. This suggests that interpreting changes in synaptic strengths (e.g., during plasticity) could be difficult as either increases or decreases in power could be expected. However, it is important to note that the theta power is a metric of the peak LFP spectral power, and it does not capture shape details of the LFP representation. Further, although our LFP representation is able to capture spatio-temporal aspects due to using a multi-compartment PYR model, it is still a simplistic representation relative to the experimental situation.

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.