4.4 Article

The h Current Is a Candidate Mechanism for Regulating the Sliding Modification Threshold in a BCM-Like Synaptic Learning Rule

Journal

JOURNAL OF NEUROPHYSIOLOGY
Volume 104, Issue 2, Pages 1020-1033

Publisher

AMER PHYSIOLOGICAL SOC
DOI: 10.1152/jn.01129.2009

Keywords

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Funding

  1. International Human Frontier Science Program Organization
  2. National Institutes of Health [MH-48432, MH-44754, NS-37444]

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Narayanan R, Johnston D. The h current is a candidate mechanism for regulating the sliding modification threshold in a BCM-like synaptic learning rule. J Neurophysiol 104: 1020-1033, 2010. First published June 16, 2010; doi: 10.1152/jn.01129.2009. Hebbian synaptic plasticity acts as a positive feedback mechanism and can destabilize a neuronal network unless concomitant homeostatic processes that counterbalance this instability are activated. Within a Bienenstock-Cooper-Munro (BCM)-like plasticity framework, such compensation is achieved through a modification threshold that slides in an activity-dependent fashion. Although the BCM-like plasticity framework has been a useful formulation to understand synaptic plasticity and metaplasticity, a mechanism for the activity-dependent regulation of this modification threshold has remained an open question. In this simulation study based on CA1 pyramidal cells, we use a modification of the calcium-dependent hypothesis proposed elsewhere and show that a change in the hyperpolarization-activated, nonspecific-cation h current is capable of shifting the modification threshold. Based on the direction of such a shift in relation to changes in the h current, and supported by previous experimental results, we argue that the h current fits the requirements for an activity-dependent regulator of this modification threshold. Additionally, using the same framework, we show that multiple voltage-and ligand-gated ion channels present in a neuronal compartment can regulate the modification threshold through complex interactions among themselves. Our results underscore the heavy mutual interdependence of synaptic and intrinsic properties/plasticity in regulating learning and homeostasis in single neurons and their networks under both physiological and pathological brain states.

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