Oscillation in a network model of neocortex

Dwyer, Jennifer E.; Lee, Hyong; Martell, Amber; Hereld, Mark; Drongelen, Wim van

doi:10.1016/j.neucom.2009.12.021

Cited by 11 publications

(7 citation statements)

References 18 publications

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“…Furthermore, our measurements of relative sag metrics combined with the results from our pharmacological experiments suggest that I h acts as the primary phenomenological inductor in these neurons (Figs and ). This interpretation also agrees with our previous modeling study, which estimated the time constant of the primary resonant ion channel in these neurons to be of the order of hundreds of milliseconds (Dwyer et al ., ). Our spiking statistics data, in addition to the pharmacology results, implicate other potassium currents as contributing auxiliary inductive components; the I NaP appears to play a role in amplifying the impedance magnitude without contributing significantly to the impedance phase response (Figs and ).…”

Section: Discussionmentioning

confidence: 97%

“…These data support an equivalent circuit model for resonance, such as the one described by Dwyer et al . (), in which resonance characteristics depend upon the presence and properties of an inductive element. Furthermore, our measurements of relative sag metrics combined with the results from our pharmacological experiments suggest that I h acts as the primary phenomenological inductor in these neurons (Figs and ).…”

Section: Discussionmentioning

confidence: 99%

“…Neuronal resonance is shaped by passive characteristics and the active voltage‐dependent conductances present in a cell (Hutcheon & Yarom, ), which reflects the presence of an inductor function, originally described by Cole (). Several authors have shown how inductance‐like behavior can be generated by mathematical linearization of the Hodgkin–Huxley (or Hodgkin–Huxley‐like) equations, which corresponds experimentally to examining subthreshold membrane responses near rest (Mauro et al ., ; Koch, , ; Rinzel & Ermentrout, ; Richardson et al ., ; Dwyer et al ., ). The resonance attributes determine how neurons will respond to input, and their frequency‐dependent responses affect spike‐timing and/or subthreshold behavior with respect to background activity (Pike et al ., ; Richardson et al ., ; Tohidi & Nadim, ; Dwyer et al ., ).…”

Section: Introductionmentioning

confidence: 97%

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Resonance in neocortical neurons and networks

Dwyer

Lee

Martell

et al. 2012

Eur J of Neuroscience

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Neocortical networks produce oscillations that often correspond to characteristic physiological or pathological patterns. However, the mechanisms underlying the generation of and the transitions between such oscillatory states remain poorly understood. In this study, we examined resonance in mouse layer V neocortical pyramidal neurons. To accomplish this, we employed standard electrophysiology to describe cellular resonance parameters. Bode plot analysis revealed a range of resonance magnitude values in layer V neurons and demonstrated that both magnitude and phase response characteristics of layer V neocortical pyramidal neurons are modulated by changes in the extracellular environment. Specifically, increased resonant frequencies and total inductive areas were observed at higher extracellular potassium concentrations and more hyperpolarised membrane potentials. Experiments using pharmacological agents suggested that current through hyperpolarization-activated cyclic nucleotide-gated channels (I(h) ) acts as the primary driver of resonance in these neurons, with other potassium currents, such as A-type potassium current and delayed-rectifier potassium current (Kv1.4 and Kv1.1, respectively), contributing auxiliary roles. The persistent sodium current was also shown to play a role in amplifying the magnitude of resonance without contributing significantly to the phase response. Although resonance effects in individual neurons are small, their properties embedded in large networks may significantly affect network behavior and may have potential implications for pathological processes.

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Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

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Resonance in neocortical neurons and networks

Dwyer

Lee

Martell

et al. 2012

Eur J of Neuroscience

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“…An interesting observation is that the Bode plot in Figure 4 shows a peak in the magnitude of the impedance, indicating that neurons and their networks display resonance [55,[57][58][59][60]. With an appropriate, realistic choice of parameters, this peak is located in the alpha-frequency range of the EEG at 75 rad/s (∼12 Hz).…”

Section: The Frequency Response Of the Linearized Modelmentioning

confidence: 94%

“…In this approach, we focus on solving for the excitatory component since this is considered the major contributor to the EEG signal. First we substitute the linearized version of (60) into (59):…”

Section: Zooming Outmentioning

confidence: 99%

Modeling Neural Activity

Drongelen¹

2013

ISRN Biomathematics

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This paper provides an overview of different types of models for studying activity of nerve cells and their networks with a special emphasis on neural oscillations. One part describes the neuronal models based on the Hodgkin and Huxley formalism first described in the 1950s. It is discussed how further simplifications of this formalism enable mathematical analysis of the process of neural excitability. The focus of the paper’s second component is on network activity. Understanding network function is one of the important frontiers remaining in neuroscience. At present, experimental techniques can only provide global recordings or samples of the activity of the huge networks that form the nervous system. Models in neuroscience can therefore play a critical role by providing a framework for integration of necessarily incomplete datasets, thereby providing insight into the mechanisms of neural function. Network models can either explicitly contain individual network nodes that model the neurons, or they can be based on representations of compound population activity. The latter approach was pioneered by Wilson and Cowan in the 1970s. Finally I provide an overview and discuss how network models are employed in the study of neuronal network pathology such as epilepsy.

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Oscillatory Behavior for a Class of Recurrent Neural Networks with Time-Varying Input and Delays

Feng

2012

Bio-Inspired Computing and Applications

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Oscillation in a network model of neocortex

Cited by 11 publications

References 18 publications

Resonance in neocortical neurons and networks

Resonance in neocortical neurons and networks

Modeling Neural Activity

Oscillatory Behavior for a Class of Recurrent Neural Networks with Time-Varying Input and Delays

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