Box 5.17Computational Perspectives on Dopamine Function in the Prefrontal Cortex

Connectionist Models of Dopamine Neuromodulation

A long-held hypothesis suggests that catecholamine neurotransmitters, including dopamine (DA), modulate target neuron responses, by increasing their signal-to-noise (SNR) ratio (i.e. by increasing the differentiation between background or baseline firing rates and those that are evoked by afferent stimulation). For example, studies in the striatum showed that DA potentiated the response of target neurons to the effect of both excitatory and inhibitory signals. However, the precise biophysical mechanisms underlying these effects were not well understood. Moreover, the view that DA acts as a modulator in the pre-frontal cortex (PFC) has been controversial, because, for many years, DA application or stimulation of DA neurons reliably inhibited spontaneous PFC activity. Thus, many investigators argued that DA served as an inhibitory transmitter in PFC.

The first explicit computational models of the neuromodulatory function of catecholamines, and DA in particular, were developed within the connectionist framework, and focused on their effects on information processing. Although such models do not typically incorporate biophysical detail, by virtue of their simplicity they have the advantage of simulating system level function and performance in a wide variety of cognitive tasks. Within this framework, DA effects were simulated as a change in the slope (or gain) of the sigmoidally shaped input-output activation function of processing units. Thus, in the presence of DA, both the excitatory and inhibitory influences of afferent inputs are potentiated. Computational analyses showed that this modulatory function would not improve the SNR characteristics of single neurons, but could do so at the network level. Models implementing these ideas proved useful for accounting for a wide range of phenomena, including the pharmacological effects of DA on performance in tasks thought to rely on PFC and the effects of disturbances of DA in schizophrenia.

Biophysically Detailed Models

In recent work, computational studies have focused on more biophysically detailed accounts of DA action within PFC. Models by Durstewitz et al. and Brunel and Wang, all include data on the different biophysical effects of DA on specific cellular processes. These models have been used to simulate the dynamics of activity in networks that closely parallel the patterns observed in vivo within PFC….

These models synthesize the rapidly growing, but often confusing literature on the neurophysiology of DA within PFC. For example, the biophysical effects of DA are shown to produce a suppressive influence on spontaneous activity, explaining its apparent inhibitory actions, while at the same time causing an enhanced excitability in response to afferent drive. Furthermore, the selective enhancement of inputs from recurrent versus external afferents provides a mechanism for stabilizing sustained activity patterns within PFC that are resistant to interference from external inputs. These computational analyses support the characterization of DA as a modulatory neurotransmitter, rather than a classical excitatory or inhibitory one, and explain its role in support sustained activity within PFC.

Strikingly, these models are remarkably consistent with the original hypothesis that DA increases SNR within the PFC, and the expression of this idea in earlier connectionist models. The underlying assumption in both types of models is that short-term storage of information in PFC occurs through recirculating activity within local recurrent networks, which can be described as fixed-point attractor systems. DA activity helps to stabilize attractor states, both by making high activity states more stable (active maintenance), and low activity states (spontaneous background activity) less likely to spuriously transition to high activity states in the absence of strong afferent input. This is accomplished by the concurrent potentiation of excitatory and inhibitory transmission, implemented as changes in ion channel properties in biophysically detailed models and “summarized” as a change in the gain of the sigmoidal activation function in connectionist models.

These mechanisms can be used to simulate the effects of DA on performance in cognitive tasks that rely on PFC function. For example, in a task emphasizing the role of PFC in working memory, increased DA activation in the Durstewitz et al. model enhanced the stability of PFC working memory representations by making them less susceptible to interference from the intervening distractors. Within connectionist models, similar effects have been demonstrated by changing the gain of the activation function, and simulating human performance in tasks known to rely on PFC, tasks similar to those simulated by Durstewitz et al. and Brunel and Wang.

SOURCE: Reprinted by permission from J.D. Cohen, T.S. Braver, and J.W. Brown, “Computational Perspectives on Dopamine Function in Prefrontal Cortex,” Current Opinion in Neurobiology 12(2):223-229. Copyright 2002 Elsevier. (References omitted.)

From: 5, Computational Modeling and Simulation as Enablers for Biological Discovery

Cover of Catalyzing Inquiry at the Interface of Computing and Biology
Catalyzing Inquiry at the Interface of Computing and Biology.
National Research Council (US) Committee on Frontiers at the Interface of Computing and Biology; Wooley JC, Lin HS, editors.
Washington (DC): National Academies Press (US); 2005.
Copyright © 2005, National Academy of Sciences.

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