Dopamine-modulated dynamic cell assemblies generated by the GABAergic striatal microcircuit

Abstract

The striatum, principal input structure of the basal ganglia, is crucial to both motor control and learning. It receives convergent input from all over 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 spiketrain 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 loss of dopamine cells.

Active Inference: A Process Theory

“…We have previously considered the intimate (monotonic) relationship between expected precision and expected utility in this context (see Friston et al, 2014, for a fuller discussion). The role of the neuromodulator dopamine in encoding precision is also consistent with its multiplicative effect in equation 2.7, to nuance the selection among competing policies (Fiorillo et al, 2003;Frank, Scheres, & Sherman, 2007;Humphries, Wood, & Gurney, 2009;Humphries, Khamassi, & Gurney, 2012;Solway & Botvinick, 2012;Mannella & Baldassarre, 2015). We will return to this later.…”

Opposite Effects of Stimulant and Antipsychotic Drugs on Striatal Fast-Spiking Interneurons

“…However, our results are in line with recent detailed modeling studies that have indicated unexpected, counter-intuitive properties of striatal microcircuits. In particular, the GABAergic input from FSIs seems not to greatly reduce overall MSN firing (Moyer and Wolf, 2009) and may even increase it (Humphries et al, 2009). FSIs are well placed to determine the fine timing of MSN spiking (Koos et al, 2004;Berke, 2009;Wilson, 2009) and may serve to control the correlations between members of MSN ensembles (Humphries et al, 2009) more than their gross firing rate.…”

“…In particular, the GABAergic input from FSIs seems not to greatly reduce overall MSN firing (Moyer and Wolf, 2009) and may even increase it (Humphries et al, 2009). FSIs are well placed to determine the fine timing of MSN spiking (Koos et al, 2004;Berke, 2009;Wilson, 2009) and may serve to control the correlations between members of MSN ensembles (Humphries et al, 2009) more than their gross firing rate. Another unanticipated property of FSIs is that, though coupled together by gap junctions, they do not normally show high synchrony (Berke, 2008;Hjorth et al, 2009;Humphries et al, 2009).…”

“…FSIs are well placed to determine the fine timing of MSN spiking (Koos et al, 2004;Berke, 2009;Wilson, 2009) and may serve to control the correlations between members of MSN ensembles (Humphries et al, 2009) more than their gross firing rate. Another unanticipated property of FSIs is that, though coupled together by gap junctions, they do not normally show high synchrony (Berke, 2008;Hjorth et al, 2009;Humphries et al, 2009). We have found evidence that the striatal FSI network may switch from an unstable to a stable state as actions are selected, with gap junctions contributing to the stabilization of FSI firing (Lau et al, 2009).…”

“…Parvalbumin-positive (PV + ) fast-spiking interneurons (FSIs) form a major striatal cell class that has come under increasing recent scrutiny. Both in vitro and anesthetized in vivo studies have shown that FSIs can provide powerful perisomatic inhibition of MSNs (Koos and Tepper, 1999;Mallet et al, 2005), whereas studies in behaving animals (Berke, 2008;Berke, 2009;Gage et al, 2008) and detailed network simulations (for example, Humphries et al, 2009;Moyer and Wolf, 2009) indicate that FSI:MSN interactions are more complex than simple inhibition (see Discussion). The exact role of FSIs in normal striatal information processing is unknown, although there is evidence that they are involved in the suppression of unwanted actions .…”