A neurocomputational theory of the dopaminergic modulation of working memory functions (original) (raw)
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Dopaminergic Modulation of Local Network Activity in Rat Prefrontal Cortex
Journal of Neurophysiology, 2007
The prefrontal cortex (PFC) is critically involved in working memory, which underlies memory-guided, goal-directed behavior. During working-memory tasks, PFC neurons exhibit sustained elevated activity, which may reflect the active holding of goal-related information or the preparation of forthcoming actions. Dopamine via the D1 receptor strongly modulates both this sustained (delay-period) activity and behavioral performance in working-memory tasks. However, the function of dopamine during delay-period activity and the underlying neural mechanisms are only poorly understood. Recently we proposed that dopamine might stabilize active neural representations in PFC circuits during tasks involving working memory and render them robust against interfering stimuli and noise. To further test this idea and to examine the dopamine-modulated ionic currents that could give rise to increased stability of neural representations, we developed a network model of the PFC consisting of multicompartment neurons equipped with Hodgkin-Huxley-like channel kinetics that could reproduce in vitro whole cell and in vivo recordings from PFC neurons. Dopaminergic effects on intrinsic ionic and synaptic conductances were implemented in the model based on in vitro data. Simulated dopamine strongly enhanced high, delay-type activity but not low, spontaneous activity in the model network. Furthermore the strength of an afferent stimulation needed to disrupt delay-type activity increased with the magnitude of the dopamine-induced shifts in network parameters, making the currently active representation much more stable. Stability could be increased by dopamine-induced enhancements of the persistent Na ϩ and N-methyl-D-aspartate (NMDA) conductances. Stability also was enhanced by a reduction in AMPA conductances. The increase in GABA A conductances that occurs after stimulation of dopaminergic D1 receptors was necessary in this context to prevent uncontrolled, spontaneous switches into high-activity states (i.e., spontaneous activation of task-irrelevant representations). In conclusion, the dopamine-induced changes in the biophysical properties of intrinsic ionic and synaptic conductances conjointly acted to highly increase stability of activated representations in PFC networks and at the same time retain control over network behavior and thus preserve its ability to adequately respond to task-related stimuli. Predictions of the model can be tested in vivo by locally applying specific D1 receptor, NMDA, or GABA A antagonists while recording from PFC neurons in delayed reaction-type tasks with interfering stimuli.
International journal of neural systems, 2010
How do organisms select and organize relevant sensory input in working memory (WM) in order to deal with constantly changing environmental cues? Once information has been stored in WM, how is it protected from and altered by the continuous stream of sensory input and internally generated planning? The present study proposes a novel role for dopamine (DA) in the maintenance of WM in the prefrontal cortex (Pfc) neurons that begins to address these issues. In particular, DA mediates the alternation of the Pfc network between input-driven and internally-driven states, which in turn drives WM updates and storage. A biologically inspired neural network model of Pfc is formulated to provide a link between the mechanisms of state switching and the biophysical properties of Pfc neurons. This model belongs to the recurrent competitive fields(33) class of dynamical systems which have been extensively mathematically characterized and exhibit the two functional states of interest: input-driven a...
Dopaminergic Regulation of Neuronal Circuits in Prefrontal Cortex
Neuromodulators, like dopamine, have considerable influence on the processing capabilities of neural networks. This has for instance been shown in the working memory functions of prefrontal cortex, which may be regulated by altering the dopamine level. Experimental work provides evidence on the biochemical and electrophysiological actions of dopamine receptors, but there are few theories concerning their significance for computational properties , (Hasselmo, 1994b)). We point to experimental data on neuromodulatory regulation of temporal properties of excitatory neurons and depolarization of inhibitory neurons, and suggest computational models employing these effects. Changes in membrane potential may be modelled by the firing threshold, and temporal properties by a parameterization of neuronal responsiveness according to the preceding spike interval. We apply these concepts to two examples using spiking neural networks. In the first case, there is a change in the input synchronization of neuronal groups, which leads to changes in the formation of synchronized neuronal ensembles. In the second case, the threshold of interneurons influences lateral inhibition, and the switch from a winner-take-all network to a parallel feedforward mode of processing. Both concepts are interesting for the modeling of cognitive functions and may have explanatory power for behavioral changes associated with dopamine regulation.
The Effect of Dopamine on Working Memory
Neural Processing Letters, 2012
In this study, our work is on the basis of Liang et al. (Cogn Neurodyn 4(4):359-366, 2010). Since the basal ganglia (BG) and dopamine (DA) are confirmed to play an important role in protecting memories against noise and distraction stimulus, we add the BG in our present model and remove the plausible Ca 2+ subsystem. We found that our network model maintained the persistent activity in response to a brief transient stimulus, which was similar to the above work. Furthermore the working memory performance was resistant to noise and performed better than that by Liang et al. (Cogn Neurodyn 4(4):359-366, 2010) in the aspect of resistance to distraction stimulus when the model is tuned to be bistable by DA.
An integrative theory of the phasic and tonic modes of dopamine modulation in the prefrontal cortex
Neural Networks, 2002
This paper presents a model of both tonic and phasic dopamine (DA) effects on maintenance of working memory representations in the prefrontal cortex (PFC). The central hypothesis is that DA modulates the efficacy of inputs to prefrontal pyramidal neurons to prevent interferences for active maintenance. Phasic DA release, due to DA neurons discharges, acts at a short time-scale (a few seconds), while the tonic mode of DA release, independent of DA neurons firing, acts at a long time-scale (a few minutes). The overall effect of DA modulation is modeled as a threshold restricting incoming inputs arriving on PFC neurons. Phasic DA release temporary increases this threshold while tonic DA release progressively increases the basal level of this threshold. Thus, unlike the previous gating theory of phasic DA release, proposing that it facilitates incoming inputs at the time of their arrival, the effect of phasic DA release is supposed to restrict incoming inputs during a period of time after DA neuron discharges. The model links the cellular and behavioral levels during performance of a working memory task. It allows us to understand why a critical range of DA D1 receptors stimulation is required for optimal working memory performance and how D1 receptor agonists (respectively antagonists) increase perseverations (respectively distractability). Finally, the model leads to several testable predictions, including that the PFC regulates DA neurons firing rate to adapt to the delay of the task and that increase in tonic DA release may either improve or decrease performance, depending on the level of DA receptors stimulation at the beginning of the task. q : S 0 8 9 3 -6 0 8 0 ( 0 2 ) 0 0 0 5 1 -5 Neural Networks 15 www.elsevier.com/locate/neunet
A model of prefrontal cortex dopaminergic modulation during the delayed alternation task
Journal of Cognitive Neuroscience, 2002
& Working memory performance is modulated by the level of dopamine (DA) D1 receptors stimulation in the prefrontal cortex (PFC). This modulation is exerted at different time scales. Injection of D1 agonists/antagonists exerts a longlasting influence (several minutes or hours) on PFC pyramidal neurons. In contrast, during performance of a cognitive task, the duration of the postsynaptic effect of phasic DA release is short lasting. The functional relationships of these two time scales of DA modulation remain poorly understood. Here we propose a model that combines these two time scales of DA modulation on a prefrontal neural network. The model links the cellular and behavioral levels during performance of the delayed alternation task. The network, which represents the activity of deep-layer pyramidal neurons with intrinsic neuronal properties, exhibits two stable states of activity that can be switched on and off by excitatory inputs from long-distance cortical areas arriving in superficial layers. These stable states allow PFC neurons to maintain representations during the delay period. The role of an increase of DA receptors stimulation is to restrict inputs arriving on the prefrontal network. The model explains how the level of working memory performance follows an inverted U-shape with an increased stimulation of DA D1 receptors. The model predicts that (1) D1 receptor agonists increase perseverations, (2) D1 antagonists increase distractability, and (3) the duration of the postsynaptic effect of phasic DA release in the PFC is adjusted to the delay period of the task. These results show how the precise duration of the postsynaptic effect of phasic DA release influences behavioral performance during a simple cognitive task. &
Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology, 2014
Dopamine modulation of GABAergic transmission in the prefrontal cortex (PFC) is thought to be critical for sustaining cognitive processes such as working memory and decision-making. Here, we developed a neurocomputational model of the PFC that includes physiological features of the facilitatory action of dopamine on fast-spiking interneurons to assess how a GABAergic dysregulation impacts on the prefrontal network stability and working memory. We found that a particular non-linear relationship between dopamine transmission and GABA function is required to enable input selectivity in the PFC for the formation and retention of working memory. Either degradation of the dopamine signal or the GABAergic function is sufficient to elicit hyperexcitability in pyramidal neurons and working memory impairments. The simulations also revealed an inverted U-shape relationship between working memory and dopamine, a function that is maintained even at high levels of GABA degradation. In fact, the w...
Attention and working memory: a dynamical model of neuronal activity in the prefrontal cortex
European Journal of Neuroscience, 2003
Cognitive behaviour requires complex context-dependent mapping between sensory stimuli and actions. The same stimulus can lead to different behaviours depending on the situation, or the same behaviour may be elicited by different cueing stimuli. Neurons in the primate prefrontal cortex show task-speci®c ®ring activity during working memory delay periods. These neurons provide a neural substrate for mapping stimulus and response in a¯exible, context-or rule-dependent, fashion. We describe here an integrate-and-®re network model to explain and investigate the different types of working-memory-related neuronal activity observed. The model contains different populations (or pools) of neurons (as found neurophysiologically) in attractor networks which respond in the delay period to the stimulus object, the stimulus position (`sensory pools'), to combinations of the stimulus sensory properties (e.g. the object identity or object location) and the response (`intermediate pools'), and to the response required (left or right) (`premotor pools'). The pools are arranged hierarchically, are linked by associative synaptic connections, and have global inhibition through inhibitory interneurons to implement competition. It is shown that a biasing attentional input to de®ne the current rule applied to the intermediate pools enables the system to select the correct response in what is a biased competition model of attention. The integrate-and-®re model not only produces realistic spiking dynamicals very similar to the neuronal data but also shows how dopamine could weaken and shorten the persistent neuronal activity in the delay period; and allows us to predict more response errors when dopamine is elevated because there is less different activity in the different pools of competing neurons, resulting in more con¯ict.