Computational Models of Neuromodulation

Fellous, Jean-Marc; Linster, Christiane

doi:10.1162/089976698300017476

Cited by 84 publications

(54 citation statements)

References 106 publications

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“…Outside of cognitive aging research, there are quite a few formal models of neuromodulation [7]. However, to our knowledge no research e!ort has yet been devoted to instantiate formal models that link aging-induced changes in catecholaminergic modulation with a broad class of human cognitive aging phenomena at the behavioral level.…”

Section: Modeling Age-related Attenuation Of Catecholaminergic Functionmentioning

confidence: 99%

Unifying cognitive aging: From neuromodulation to representation to cognition

Lindenberger

Frensch

2000

Neurocomputing

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An integrative theory relating cognitive aging de"cits observed at the behavioral level with age-related de"ciency in neuromodulation causing less distinctive cortical representations is tested in a series of neural network simulations. Age-related attenuation of catecholaminergic function is simulated by lowering the mean gain of the processing unit, which subsequently reduces responsivity and raises intra-network activation variability. Age di!erences in learning rate, asymptotic performance, interference susceptibility, complexity cost, intra-and interindividual variability, and ability dedi!erentiation can all be modeled. Together, the simulations illustrate catecholamine's role in regulating the "delity of neural information processing and subsequent e!ects leading to cognitive aging de"cits.

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Section: Modeling Age-related Attenuation Of Catecholaminergic Functionmentioning

confidence: 99%

Unifying cognitive aging: From neuromodulation to representation to cognition

Lindenberger

Frensch

2000

Neurocomputing

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“…Driven by biological findings, researchers have augmented their models with modulatory signals (Hasselmo and Schnell, 1994;Fellous and Linster, 1998;Ludvig et al, 2008) or attempted to model biological modulatory activities (Baxter et al, 1999;Cohen, 2008). The precise role of various modulatory chemicals (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…serotonin, acetylcholine, dopamine and norepinephrine (Bear et al, 2005;Hasselmo, 2006)) is still debated, in particular regarding the role of dopamine in reward learning (Pennartz, 1996(Pennartz, , 1997Berridge and Robinson, 1998;Montague et al, 2004;Schultz, 2006;Redgrave et al, 2008). Moreover, modulation appears to regulate a large variety of behaviors like arousal, attention, reward learning, and memory (HarrisWarrick and Marder, 1991;Hasselmo, 1995;Aston-Jones and Cohen, 2005), resulting in an accordingly large spectrum of dynamics and models that regulate synaptic efficacy, synaptic changes and other neural variables (Hasselmo and Schnell, 1994;Fellous and Linster, 1998;Doya, 2002;Smith et al, 2002;Krichmar, 2008;Cox and Krichmar, 2009). One promising computational aspect of modulation is the possibility of increasing, decreasing or inverting the strength and sign of plasticity (Abbott, 1990;Montague et al, 1996;Florian, 2007;Porr and Wörgötter, 2007;Izhikevich, 2007;Pfeiffer et al, 2010), making neuromodulation particularly suitable for modeling and implementing learning processes (Sporns and Alexander, 2002;Doya, 2002;Doya and Uchibe, 2005;Soula et al, 2005;Farries and Fairhall, 2007;Krichmar, 2008;Cox and Krichmar, 2009).…”

Section: Introductionmentioning

confidence: 99%

From modulated Hebbian plasticity to simple behavior learning through noise and weight saturation

Soltoggio¹,

Stanley²

2012

Neural Networks

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Citation: SOLTOGGIO, A. and STANLEY, K.O., 2012. From modulated Hebbian plasticity to simple behavior learning through noise and weight saturation. Neural Networks, 34 pp. 28-41.Additional Information:• NOTICE: this is the author's version of a work that was accepted for publication in Neural Networks. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neural Networks, vol. 34 (2012) AbstractSynaptic plasticity is a major mechanism for adaptation, learning and memory. Yet current models struggle to link local synaptic changes to the acquisition of behaviors. The aim of this paper is to demonstrate a computational relationship between local Hebbian plasticity and behavior learning by exploiting two traditionally unwanted features: neural noise and synaptic weight saturation. A modulation signal is employed to arbitrate the sign of plasticity: when the modulation is positive, the synaptic weights saturate to express exploitative behavior; when it is negative, the weights converge to average values and neural noise reconfigures the network's functionality. This process is demonstrated through simulating neural dynamics in the autonomous emergence of fearful and aggressive navigating behaviors and in the solution to reward-based problems. The neural model learns, memorizes and modifies different behaviors that lead to positive modulation in a variety of settings. The algorithm establishes a simple relationship between local plasticity and behavior learning by demonstrating the utility of noise and weight saturation. Moreover it provides a new tool to simulate adaptive behavior and contributes to bridging the gap between synaptic changes and behavior in neural computation.

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“…Neuromodulation has been receiving progressively more attention in computational literature [1] . While neuronal network dynamics and their coding possibilities as defined by classical neurotransmission and the properties of the constituent cells have been extensively studied theoretically (references are too numerous to list, yet see [2,3] for general treatment) the dynamical and computational consequences of the extra-synaptic neuromodulation are just beginning to be explored [1] .…”

Section: Introductionmentioning

confidence: 99%

“…While neuronal network dynamics and their coding possibilities as defined by classical neurotransmission and the properties of the constituent cells have been extensively studied theoretically (references are too numerous to list, yet see [2,3] for general treatment) the dynamical and computational consequences of the extra-synaptic neuromodulation are just beginning to be explored [1] . Neuromodulatory action has been implicated in a wide variety of processes related to cognition, psychiatric disorders and drug addiction [4] .…”

Section: Introductionmentioning

confidence: 99%

Modeling nicotinic neuromodulation from global functional and network levels to nAChR based mechanisms

Graupner

Gutkin

2009

Acta Pharmacol Sin

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Neuromodulator action has received increasing attention in theoretical neuroscience. Yet models involving both neuronal populations dynamics at the circuit level and detailed receptor properties are only now being developed. Here we review recent computational approaches to neuromodulation, focusing specifically on acetylcholine (ACh) and nicotine. We discuss illustrative examples of models ranging from functional top-down to neurodynamical bottom-up. In the top-down approach, a computational theory views ACh as encoding the uncertainty expected in an environment. A different line of models accounts for neural population dynamics treating ACh as toggling neuronal networks between read-in of information and recall of memory. Building on the neurodynamics idea we discuss two models of nicotine's action with increasing degree of biological realism. Both consider explicitly receptor-level mechanisms but with different scales of detail. The first is a large-scale model of nicotine-dependent modulation of dopaminergic signaling that is capable of simulating nicotine self-administration. The second is a novel approach where circuit-level neurodynamics of the ventral tegmental area (VTA) are combined with explicit models of the dynamics of specific nicotinic ACh receptor subtypes. We show how the model is constructed based on local anatomy, electrophysiology and receptor properties and provide an illustration of its potential. In particular, we show how the model can shed light on the specific mechanisms by which nicotine controls dopaminergic neurotransmission in the VTA. This model serves us to conclude that detailed accounts for neuromodulator action at the basis of behavioral and cognitive models are crucial to understand how neuromodulators mediate their functional properties.

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Computational Models of Neuromodulation

Cited by 84 publications

References 106 publications

Unifying cognitive aging: From neuromodulation to representation to cognition

Unifying cognitive aging: From neuromodulation to representation to cognition

From modulated Hebbian plasticity to simple behavior learning through noise and weight saturation

Modeling nicotinic neuromodulation from global functional and network levels to nAChR based mechanisms

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