Analysis of Striatal Dynamics: The Existence of Two Modes of Behaviour

doi:10.1006/jtbi.1993.1128

Journal of Theoretical Biology

Volume 163, Issue 4, 21 August 1993, Pages 413-438

https://doi.org/10.1006/jtbi.1993.1128 Get rights and content

Abstract

The qualitative dynamical behaviour of a neural model based on the mammalian neostriatum was analysed. The neostriatum was modelled as a mutually inhibitory network of physiological neurones, which was driven by excitatory afferents from the cerebral cortex. The analysis defined the conditions under which the system would enter into one of two dynamic modes, competition or co-activation, in terms of the parameters defining receptor-operated and voltage-sensitive channels in the neuronal membrane. We have previously argued that the mode of co-activation in the neostriatum may correspond to the state of muscular rigidity which occurs as a symptom of Parkinson's disease. The present work extends a preliminary analysis of a two-neurone system to a system of arbitrary size. An explicit prediction is made of the conditions under which a transition from co-activation to competition will occur, which is testable experimentally. The wavelength of a non-uniform activity pattern produced by small departures from uniform afferent drive is determined for one- and two-dimensional arrays of neurones. Two mild assumptions about the connectivity of the network were used to simplify the analysis, namely that the network was symmetric and homogeneous. The implications of departures from these assumptions for understanding the disordered movement seen in Huntington's disease are also considered.

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Brain-inspired meta-reinforcement learning cognitive control in conflictual inhibition decision-making task for artificial agents
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The action values are initialized at the beginning of the experiment, updated automatically after the action execution (salient events, such as after reward or punishment delivery) and reset after the completion of a block of trials (except for the action value encoding the action inhibition). The basal ganglia layers transfer the information from the LFPC layer to the PMC, implementing a winner-take-all mechanism to prevent the execution of two actions at the same time (Alexander & Wickens, 1993; Berns & Sejnowski, 1996; Binas et al., 2014; Humphries, 2014). If the activity related to a non-selected action remains non-null, the competing mechanism is performed by a double inhibitory synapse, i.e., the first one between the striatum and the SNr and the second between the SNr and thalamus.
Conflictual cues and unexpected changes in human real-case scenarios may be detrimental to the execution of tasks by artificial agents, thus affecting their performance. Meta-learning applied to reinforcement learning may enhance the design of control algorithms, where an outer learning system progressively adjusts the operation of an inner learning system, leading to practical benefits for the learning schema. Here, we developed a brain-inspired meta-learning framework for inhibition cognitive control that i) exploits the meta-learning principles in the neuromodulation theory proposed by Doya, ii) relies on a well-established neural architecture that contains distributed learning systems in the human brain, and iii) proposes optimization rules of meta-learning hyperparameters that mimic the dynamics of the major neurotransmitters in the brain. We tested an artificial agent in inhibiting the action command in two well-known tasks described in the literature: NoGo and Stop-Signal Paradigms. After a short learning phase, the artificial agent learned to react to the hold signal, and hence to successfully inhibit the motor command in both tasks, via the continuous adjustment of the learning hyperparameters. We found a significant increase in global accuracy, right inhibition, and a reduction in the latency time required to cancel the action process, i.e., the Stop-signal reaction time. We also performed a sensitivity analysis to evaluate the behavioral effects of the meta-parameters, focusing on the serotoninergic modulation of the dopamine release. We demonstrated that brain-inspired principles can be integrated into artificial agents to achieve more flexible behavior when conflictual inhibitory signals are present in the environment.
Dopamine-modulated dynamic cell assemblies generated by the GABAergic striatal microcircuit
2009, Neural Networks
Citation Excerpt :
Many theories of striatal-specific or global basal ganglia function draw explicit attention to the role of the inhibitory local MSN collaterals as a substrate for competitive dynamics (e.g. Beiser & Houk, 1998; Frank, 2005; Pennartz, Groenewegen, & da Silva, 1994; Wickens, Alexander, & Miller, 1991), whether that competition be labelled ‘decision making’, ‘motor program selection’ or ‘pattern classification’. Wickens and colleagues’ domain hypothesis is the most developed, and proposes that the basic computational element of the striatum is the set–or “domain”–of all MSNs that are mutual inhibitory (see e.g. Alexander & Wickens, 1993; Wickens et al., 1991; Wickens, Kotter, & Alexander, 1995). In simulation, they have shown that winner-takes-all like competition occurs within a single domain, while winners-share-all dynamics (multiple active neurons) occur in networks composed of multiple overlapping domains (Alexander & Wickens, 1993; Wickens et al., 1991).
The striatum, the principal input structure of the basal ganglia, is crucial to both motor control and learning. It receives convergent input from all over the neocortex, hippocampal formation, amygdala and thalamus, and is the primary recipient of dopamine in the brain. Within the striatum is a GABAergic microcircuit that acts upon these inputs, formed by the dominant medium-spiny projection neurons (MSNs) and fast-spiking interneurons (FSIs). There has been little progress in understanding the computations it performs, hampered by the non-laminar structure that prevents identification of a repeating canonical microcircuit. We here begin the identification of potential dynamically-defined computational elements within the striatum. We construct a new three-dimensional model of the striatal microcircuit’s connectivity, and instantiate this with our dopamine-modulated neuron models of the MSNs and FSIs. A new model of gap junctions between the FSIs is introduced and tuned to experimental data. We introduce a novel multiple spike-train analysis method, and apply this to the outputs of the model to find groups of synchronised neurons at multiple time-scales. We find that, with realistic in vivo background input, small assemblies of synchronised MSNs spontaneously appear, consistent with experimental observations, and that the number of assemblies and the time-scale of synchronisation is strongly dependent on the simulated concentration of dopamine. We also show that feed-forward inhibition from the FSIs counter-intuitively increases the firing rate of the MSNs. Such small cell assemblies forming spontaneously only in the absence of dopamine may contribute to motor control problems seen in humans and animals following a loss of dopamine cells.
A neurocomputational model of tonic and phasic dopamine in action selection: A comparison with cognitive deficits in Parkinson's disease
2009, Behavioural Brain Research
The striatal dopamine signal has multiple facets; tonic level, phasic rise and fall, and variation of the phasic rise/fall depending on the expectation of reward/punishment. We have developed a network model of the striatal direct pathway using an ionic current level model of the medium spiny neuron that incorporates currents sensitive to changes in the tonic level of dopamine. The model neurons in the network learn action selection based on a novel set of mathematical rules that incorporate the phasic change in the dopamine signal. This network model is capable of learning to perform a sequence learning task that in humans is thought to be dependent on the basal ganglia. When both tonic and phasic levels of dopamine are decreased, as would be expected in unmedicated Parkinson's disease (PD), the model reproduces the deficits seen in a human PD group off medication. When the tonic level is increased to normal, but with reduced phasic increases and decreases in response to reward and punishment, respectively, as would be expected in PD medicated with L-Dopa, the model again reproduces the human data. These findings support the view that the cognitive dysfunctions seen in Parkinson's disease are not solely either due to the decreased tonic level of dopamine or to the decreased responsiveness of the phasic dopamine signal to reward and punishment, but to a combination of the two factors that varies dependent on disease stage and medication status.
Simulation of GABA function in the basal ganglia: computational models of GABAergic mechanisms in basal ganglia function
2007, Progress in Brain Research
Citation Excerpt :
This dynamic may be necessary to prevent cocontraction of groups of muscular antagonists in movement. In Parkinson's disease, in which cocontraction replaces reciprocal inhibition of muscles at the onset of movements, this dynamic may break down (Wickens et al., 1991; Alexander and Wickens, 1993). The computational operation performed by WTA type networks appears deceptively simple, when in fact it is a computationally demanding operation.
This chapter outlines current interpretation of computational aspects of GABAergic circuits of the striatum. Recent hypotheses and controversial matters are reviewed. Quantitative aspects of striatal synaptology relevant to computational models are considered, with estimates of the connectivity of the spiny projection neurons and fast-spiking interneurons. Against this background, insights into the computational properties of inhibitory circuits based on analysis and simulation of simple models are discussed. The paper concludes with suggestions for further theoretical and experimental studies.
Striatopallidal regulation of affect in bipolar disorder
2006, Journal of Affective Disorders
Evidence from the neuroimaging literature suggests that the basal ganglia plays an important role in the regulation of affect. This conclusion stems almost exclusively from group comparisons and it remains unclear whether previous findings can be confirmed from a longitudinal study of mood change. The aim of this study was to increase our understanding of the functional role of the basal ganglia and thalamus in relation to change in affect in patients with bipolar disorder.
Ten bipolar disorder subjects participated in a functional MRI study. We used a simple motor reaction time task to probe subcortical regions bilaterally. Subjects were scanned twice, once when their self-reported mood ratings indicated hypomania or euthymia and then again when they were in depressed states.
Subjects in their euthymic or hypomanic states exhibited increased caudate activity bilaterally and the globus pallidus of the left hemisphere. Longitudinal analyses revealed a significant association between an increase in severity of depression and a decrease in activity in the external segment of the right globus pallidus.
Our findings suggest that the globus pallidus is less responsive during a simple motor task in the depressed compared to the normal or euthymic states in patients with bipolar disorder. These results are consistent with current physiologic models of basal ganglia circuitry in which an increase in caudate activity results in an increase in inhibitory GABAergic outflow to the external globus pallidus and subsequent decrease in thalamocortical excitation and may underlie the clinical manifestations of depression in bipolar disorder.
The findings of this study need to be interpreted with caution as correlation coefficients may be overestimated in this small study sample.
Computational models of the basal ganglia: From robots to membranes
2004, Trends in Neurosciences
With the rapid accumulation of neuroscientific data comes a pressing need to develop models that can explain the computational processes performed by the basal ganglia. Relevant biological information spans a range of structural levels, from the activity of neuronal membranes to the role of the basal ganglia in overt behavioural control. This viewpoint presents a framework for understanding the aims, limitations and methods for testing of computational models across all structural levels. We identify distinct modelling strategies that can deliver important and complementary insights into the nature of problems the basal ganglia have evolved to solve, and describe methods that are used to solve them.

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Regular ArticleAnalysis of Striatal Dynamics: The Existence of Two Modes of Behaviour

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Regular Article
Analysis of Striatal Dynamics: The Existence of Two Modes of Behaviour