Neuromodulation: Overview
Christiane Linster*Computational Physiology Lab, Department of Neurobiology & Behavior, Cornell University, Ithaca, NY, USA
Definition
Neuromodulation refers to the regulation of neural and synaptic function by regulatory extrinsic orintrinsic substances.
Detailed Description
Computational modeling of neural substrates provides an excellent theoretical framework for theunderstanding of the computational roles of neuromodulation. Neuromodulation can be defined asbiophysical processes that serve to modify – or modulate – the computation performed by a neuronor network as a function of task demands and behavioral state of the animal. These modulatoryeffects often involve substances such as acetylcholine (ACh), norepinephrine (NE), histamine,serotonin (5-HT), dopamine (DA), and a variety of neuropeptides. Modulatory effects are difficultto define, because they often originate from different structures and have different spatial distribu-tions and time courses of action. Because of the wider use of modeling techniques and growinginterest in systems neuroscience, the computational role of neuromodulation in informationprocessing has helped elucidate neuromodulatory function, and predictive theories have arisenfrom computational approaches.
Neuromodulation can be described by its spatial and temporal characteristics, as well as thespecific computational function ascribed to it. Spatial characteristics include extrinsic, originatingfrom an area extrinsic to the network under study, or intrinsic, originating from processes within thearea under study. The computational functions of extrinsic neuromodulation, such as ACh, NE, 5HT,and DA, are usually considered somewhat global, because they modulate many areas of the brainsimultaneously. Classically, ACh has been associated with attentional processes, NE with signal-to-noise modulation, DA with reward learning, and 5HT with sleep-wake transitions. In other cases,modulation is specific to the network under investigation and an integral part of the computationsperformed within. Second messenger systems, plasticity processes, and gene regulation are exam-ples of such intrinsic modulation. From a functional point of view, neuromodulation is oftenregulatory, for example, in the cases of second messenger systems or activity-dependent regulationof conductances. In the sensory systems, neuromodulation is often linked to tuning of receptivefields (ACh) and regulation of signal-to-noise ratio. A third highly important function of mostneuromodulators is the regulation of plasticity, via excitability of neurons, synaptic plasticity, andbroader modulation of network dynamics.
Exactly how neuromodulation is integrated in computational studies depends widely on thedetails of implementation of the computational model itself. Effects of neuromodulators can beimplemented from the detailed biophysical level, to broader regulation of network parameters incase of more abstract large-scale models. For example, specific effects on voltage-gated channels
*Email: [email protected]
Encyclopedia of Computational NeuroscienceDOI 10.1007/978-1-4614-7320-6_787-1# Springer Science+Business Media New York 2014
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may be implemented in a biophysical model by changing channel parameters, in a simplifiedintegrate and fire model by changing a related parameter such as firing threshold. Each studychooses the level of detail appropriate to the question asked and data available. Fora comprehensive review of levels of implementation of neuromodulation in computational models,we refer the reader to Fellous and Linster (1998). The chapters in this section cover overviews ofneuromodulatory computation divided by substance, nature of task, as well as overviews of types ofnetwork implementations and specific examples.
Cross-References
▶Computation with Dopaminergic Modulation▶Computation with Serotonergic Modulation▶Computational Models of Modulation of Oscillatory Dynamics▶Computational Models of Neuromodulation▶ Implementation of Neuromodulation: Large Scale Networks
References
Fellous JM, Linster C (1998) Computational models of neuromodulation. Neural Comput10(4):771–805
Further ReadingDayan P (2012) Twenty-five lessons from computational neuromodulation. Neuron 76(1):240–256.
doi:10.1016/j.neuron.2012.09.027
Encyclopedia of Computational NeuroscienceDOI 10.1007/978-1-4614-7320-6_787-1# Springer Science+Business Media New York 2014
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