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Simple, biologically-constrained CA1 pyramidal cell models using an intact, whole hippocampus context.

Ferguson KA, Huh CY, Amilhon B, Williams S, Skinner FK - F1000Res (2014)

Bottom Line: These models have been used to help us understand, for example, the effects of synaptic integration and voltage gated channel densities and distributions on cellular responses.In this article, we describe the development of simple models of CA1 pyramidal cells, as derived in a well-defined experimental context of an intact, whole hippocampus preparation expressing population oscillations.These models are based on the intrinsic properties and frequency-current profiles of CA1 pyramidal cells, and can be used to build, fully examine, and analyze large networks.

View Article: PubMed Central - PubMed

Affiliation: Toronto Western Research Institute, University Health Network, Toronto, Ontario, M5T 2S8, Canada ; Department of Physiology, University of Toronto, Toronto, Ontario, M5S 1A1, Canada.

ABSTRACT
The hippocampus is a heavily studied brain structure due to its involvement in learning and memory. Detailed models of excitatory, pyramidal cells in hippocampus have been developed using a range of experimental data. These models have been used to help us understand, for example, the effects of synaptic integration and voltage gated channel densities and distributions on cellular responses. However, these cellular outputs need to be considered from the perspective of the networks in which they are embedded. Using modeling approaches, if cellular representations are too detailed, it quickly becomes computationally unwieldy to explore large network simulations. Thus, simple models are preferable, but at the same time they need to have a clear, experimental basis so as to allow physiologically based understandings to emerge. In this article, we describe the development of simple models of CA1 pyramidal cells, as derived in a well-defined experimental context of an intact, whole hippocampus preparation expressing population oscillations. These models are based on the intrinsic properties and frequency-current profiles of CA1 pyramidal cells, and can be used to build, fully examine, and analyze large networks.

No MeSH data available.


A: An example intracellular recording of Pyramidal cell 1 during current clamp with applied current of 188 pA (top, blue) is compared with the firing of our strongly adapting pyramidal cell model, also with an applied current of 188 pA (bottom, dark green). The firing rates and amount of adaptation of the model are similar to those of the experiment.B: Two examples of rebound firing in our strongly adapting model. In each case, a one-second step of hyperpolarizing input is applied (shown as dashed lines; top: 20 pA step, bottom: 50 pA step). In each case, the strongly adapting model produces rebound spiking (solid line), where more spiking occurs for larger amounts of hyperpolarizing input.
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f3: A: An example intracellular recording of Pyramidal cell 1 during current clamp with applied current of 188 pA (top, blue) is compared with the firing of our strongly adapting pyramidal cell model, also with an applied current of 188 pA (bottom, dark green). The firing rates and amount of adaptation of the model are similar to those of the experiment.B: Two examples of rebound firing in our strongly adapting model. In each case, a one-second step of hyperpolarizing input is applied (shown as dashed lines; top: 20 pA step, bottom: 50 pA step). In each case, the strongly adapting model produces rebound spiking (solid line), where more spiking occurs for larger amounts of hyperpolarizing input.

Mentions: We kept our previously determined parameters constant (i.e.vr = -61.8 mV,vt = -57.0 mV,c = -65.8 mV,vpeak = 22.6 mV, andkhigh = 3.3 nS/mV), and set our membrane capacitance toCm = 115pA. We then varied the parametersa, b, d, andklow to produce multiple models. We determined the rheobase current and the slope of the initial and final f-I curve over 10 Hz (using a least squares approach) for each model in order to settle upon a final model in which our initial and final f-I slopes and rheobase approximated that which we determined biologically. We determined thata = 0.0012ms-1, b = 3nS, klow = 0.1nS/mV, andd = 10pA. This gave us a model f-I initial slope of 0.432, a final slope of 0.099, and a rheobase of ~0 pA (seeFigure 2). As shown inFigure 3, the model shows strongly adapting firing (Figure 3A) and rebound firing when hyperpolarized (Figure 3B).


Simple, biologically-constrained CA1 pyramidal cell models using an intact, whole hippocampus context.

Ferguson KA, Huh CY, Amilhon B, Williams S, Skinner FK - F1000Res (2014)

A: An example intracellular recording of Pyramidal cell 1 during current clamp with applied current of 188 pA (top, blue) is compared with the firing of our strongly adapting pyramidal cell model, also with an applied current of 188 pA (bottom, dark green). The firing rates and amount of adaptation of the model are similar to those of the experiment.B: Two examples of rebound firing in our strongly adapting model. In each case, a one-second step of hyperpolarizing input is applied (shown as dashed lines; top: 20 pA step, bottom: 50 pA step). In each case, the strongly adapting model produces rebound spiking (solid line), where more spiking occurs for larger amounts of hyperpolarizing input.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4215760&req=5

f3: A: An example intracellular recording of Pyramidal cell 1 during current clamp with applied current of 188 pA (top, blue) is compared with the firing of our strongly adapting pyramidal cell model, also with an applied current of 188 pA (bottom, dark green). The firing rates and amount of adaptation of the model are similar to those of the experiment.B: Two examples of rebound firing in our strongly adapting model. In each case, a one-second step of hyperpolarizing input is applied (shown as dashed lines; top: 20 pA step, bottom: 50 pA step). In each case, the strongly adapting model produces rebound spiking (solid line), where more spiking occurs for larger amounts of hyperpolarizing input.
Mentions: We kept our previously determined parameters constant (i.e.vr = -61.8 mV,vt = -57.0 mV,c = -65.8 mV,vpeak = 22.6 mV, andkhigh = 3.3 nS/mV), and set our membrane capacitance toCm = 115pA. We then varied the parametersa, b, d, andklow to produce multiple models. We determined the rheobase current and the slope of the initial and final f-I curve over 10 Hz (using a least squares approach) for each model in order to settle upon a final model in which our initial and final f-I slopes and rheobase approximated that which we determined biologically. We determined thata = 0.0012ms-1, b = 3nS, klow = 0.1nS/mV, andd = 10pA. This gave us a model f-I initial slope of 0.432, a final slope of 0.099, and a rheobase of ~0 pA (seeFigure 2). As shown inFigure 3, the model shows strongly adapting firing (Figure 3A) and rebound firing when hyperpolarized (Figure 3B).

Bottom Line: These models have been used to help us understand, for example, the effects of synaptic integration and voltage gated channel densities and distributions on cellular responses.In this article, we describe the development of simple models of CA1 pyramidal cells, as derived in a well-defined experimental context of an intact, whole hippocampus preparation expressing population oscillations.These models are based on the intrinsic properties and frequency-current profiles of CA1 pyramidal cells, and can be used to build, fully examine, and analyze large networks.

View Article: PubMed Central - PubMed

Affiliation: Toronto Western Research Institute, University Health Network, Toronto, Ontario, M5T 2S8, Canada ; Department of Physiology, University of Toronto, Toronto, Ontario, M5S 1A1, Canada.

ABSTRACT
The hippocampus is a heavily studied brain structure due to its involvement in learning and memory. Detailed models of excitatory, pyramidal cells in hippocampus have been developed using a range of experimental data. These models have been used to help us understand, for example, the effects of synaptic integration and voltage gated channel densities and distributions on cellular responses. However, these cellular outputs need to be considered from the perspective of the networks in which they are embedded. Using modeling approaches, if cellular representations are too detailed, it quickly becomes computationally unwieldy to explore large network simulations. Thus, simple models are preferable, but at the same time they need to have a clear, experimental basis so as to allow physiologically based understandings to emerge. In this article, we describe the development of simple models of CA1 pyramidal cells, as derived in a well-defined experimental context of an intact, whole hippocampus preparation expressing population oscillations. These models are based on the intrinsic properties and frequency-current profiles of CA1 pyramidal cells, and can be used to build, fully examine, and analyze large networks.

No MeSH data available.