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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.


The strongly adapting pyramidal cell f-I curves (Pyramidal cells 1 and 2 shown in blue and red respectively) are shown against the strongly adapting pyramidal cell model (dark green).The initial data points are shown as asterisks and the final points shown as squares. The initial model curve is shown as a solid line (initial slope: 0.432), and the final curve shown as a dashed line (final slope: 0.099). The model rheobase (~0 pA) and slope approximate those determined experimentally.
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f2: The strongly adapting pyramidal cell f-I curves (Pyramidal cells 1 and 2 shown in blue and red respectively) are shown against the strongly adapting pyramidal cell model (dark green).The initial data points are shown as asterisks and the final points shown as squares. The initial model curve is shown as a solid line (initial slope: 0.432), and the final curve shown as a dashed line (final slope: 0.099). The model rheobase (~0 pA) and slope approximate those determined experimentally.

Mentions: It is important that our model f-I curve captures two important properties of the experimental data: the rheobase current (i.e. the minimum amount of current required to initiate a spike), and the approximate slope of the curve. If these properties are captured, then the model will spike with similar frequencies as the physiological cell given the same amount of synaptic input. We found that for the two strongly adapting pyramidal cells (1 and 2), the slope of the linear least squares approximation of the f-I curves above 5 Hz were 0.376 and 0.385 for the initial curves, and 0.030 and 0.040 for the final curves. Again, the initial frequencies are based on the inverse of the first inter-spike interval due to a one second current step, and the final frequencies are based on the last inter-spike interval. A series of depolarizing (25 and 10 pA) steps were used to determine the rheobase currents, which were 1.2 pA and 38.7 pA for the two strongly adapting cells (f-I curves, red and blue curves inFigure 1 or data inFigure 2).


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)

The strongly adapting pyramidal cell f-I curves (Pyramidal cells 1 and 2 shown in blue and red respectively) are shown against the strongly adapting pyramidal cell model (dark green).The initial data points are shown as asterisks and the final points shown as squares. The initial model curve is shown as a solid line (initial slope: 0.432), and the final curve shown as a dashed line (final slope: 0.099). The model rheobase (~0 pA) and slope approximate those determined experimentally.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f2: The strongly adapting pyramidal cell f-I curves (Pyramidal cells 1 and 2 shown in blue and red respectively) are shown against the strongly adapting pyramidal cell model (dark green).The initial data points are shown as asterisks and the final points shown as squares. The initial model curve is shown as a solid line (initial slope: 0.432), and the final curve shown as a dashed line (final slope: 0.099). The model rheobase (~0 pA) and slope approximate those determined experimentally.
Mentions: It is important that our model f-I curve captures two important properties of the experimental data: the rheobase current (i.e. the minimum amount of current required to initiate a spike), and the approximate slope of the curve. If these properties are captured, then the model will spike with similar frequencies as the physiological cell given the same amount of synaptic input. We found that for the two strongly adapting pyramidal cells (1 and 2), the slope of the linear least squares approximation of the f-I curves above 5 Hz were 0.376 and 0.385 for the initial curves, and 0.030 and 0.040 for the final curves. Again, the initial frequencies are based on the inverse of the first inter-spike interval due to a one second current step, and the final frequencies are based on the last inter-spike interval. A series of depolarizing (25 and 10 pA) steps were used to determine the rheobase currents, which were 1.2 pA and 38.7 pA for the two strongly adapting cells (f-I curves, red and blue curves inFigure 1 or data inFigure 2).

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.