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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.


Two simulated networks with different OLM-BiC synaptic strengths are compared. The effect of the synaptic strengths between the two cell types (gOLM, BiC and gBiC, OLM) is difficult to visualize from the raster plot output of the network, whereas the spectral peak power of the LFP output is quite different. Raster plots (left, top and bottom), LFP representation output (left, middle), and resulting LFP power spectrum (right, in mV2∕Hz) are shown for two 850-cell networks with various maximal conductance values for OLM-BiCs (top: 1 nS; bottom: 0.5 nS) and BiC-OLM (top: 2.75 nS; bottom: 0.75 nS). Here the populations are randomly connected with a probability of 0.21 for OLM-BiC connections, and 0.13 for BiC-OLM connections, and each cell type is connected to our passive PYR model with equal connection strength.
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Figure 8: Two simulated networks with different OLM-BiC synaptic strengths are compared. The effect of the synaptic strengths between the two cell types (gOLM, BiC and gBiC, OLM) is difficult to visualize from the raster plot output of the network, whereas the spectral peak power of the LFP output is quite different. Raster plots (left, top and bottom), LFP representation output (left, middle), and resulting LFP power spectrum (right, in mV2∕Hz) are shown for two 850-cell networks with various maximal conductance values for OLM-BiCs (top: 1 nS; bottom: 0.5 nS) and BiC-OLM (top: 2.75 nS; bottom: 0.75 nS). Here the populations are randomly connected with a probability of 0.21 for OLM-BiC connections, and 0.13 for BiC-OLM connections, and each cell type is connected to our passive PYR model with equal connection strength.

Mentions: From our many simulations, we found that the spectral peak power of the LFP output representation could be high (approx > 1.5 mV2∕Hz) or low (approx < 1 mV2∕Hz). Even when the power was quite different, the effect of the synaptic strengths between the two cell types (gOLM, BiC and gBiC, OLM) could be difficult to visualize from the raster plot output of the network. This can be seen from Figure 8, where two simulated networks are compared. The first (top) has weaker OLM-BiC connections relative to BiC-OLM ones (gOLM, BiC = 1 nS; gBiC, OLM = 2.75 nS), whereas the second (bottom) has overall weaker synapses, and also weaker OLM-BiC connections relative to BiC-OLM ones (gOLM, BiC = 0.5 nS; gBiC, OLM = 0.75 nS). The theta power in the top example is high whereas the power is low in the bottom example.


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)

Two simulated networks with different OLM-BiC synaptic strengths are compared. The effect of the synaptic strengths between the two cell types (gOLM, BiC and gBiC, OLM) is difficult to visualize from the raster plot output of the network, whereas the spectral peak power of the LFP output is quite different. Raster plots (left, top and bottom), LFP representation output (left, middle), and resulting LFP power spectrum (right, in mV2∕Hz) are shown for two 850-cell networks with various maximal conductance values for OLM-BiCs (top: 1 nS; bottom: 0.5 nS) and BiC-OLM (top: 2.75 nS; bottom: 0.75 nS). Here the populations are randomly connected with a probability of 0.21 for OLM-BiC connections, and 0.13 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 8: Two simulated networks with different OLM-BiC synaptic strengths are compared. The effect of the synaptic strengths between the two cell types (gOLM, BiC and gBiC, OLM) is difficult to visualize from the raster plot output of the network, whereas the spectral peak power of the LFP output is quite different. Raster plots (left, top and bottom), LFP representation output (left, middle), and resulting LFP power spectrum (right, in mV2∕Hz) are shown for two 850-cell networks with various maximal conductance values for OLM-BiCs (top: 1 nS; bottom: 0.5 nS) and BiC-OLM (top: 2.75 nS; bottom: 0.75 nS). Here the populations are randomly connected with a probability of 0.21 for OLM-BiC connections, and 0.13 for BiC-OLM connections, and each cell type is connected to our passive PYR model with equal connection strength.
Mentions: From our many simulations, we found that the spectral peak power of the LFP output representation could be high (approx > 1.5 mV2∕Hz) or low (approx < 1 mV2∕Hz). Even when the power was quite different, the effect of the synaptic strengths between the two cell types (gOLM, BiC and gBiC, OLM) could be difficult to visualize from the raster plot output of the network. This can be seen from Figure 8, where two simulated networks are compared. The first (top) has weaker OLM-BiC connections relative to BiC-OLM ones (gOLM, BiC = 1 nS; gBiC, OLM = 2.75 nS), whereas the second (bottom) has overall weaker synapses, and also weaker OLM-BiC connections relative to BiC-OLM ones (gOLM, BiC = 0.5 nS; gBiC, OLM = 0.75 nS). The theta power in the top example is high whereas the power is low in the bottom example.

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.