Identifying control ensembles for information processing within the cortico-basal ganglia-thalamic circuit.

In situations featuring uncertainty about action-reward contingencies, mammals can flexibly adopt strategies for decision-making that are tuned in response to environmental changes. Although the cortico-basal ganglia thalamic (CBGT) network has been identified as contributing to the decision-making process, it features a complex synaptic architecture, comprised of multiple feed-forward, reciprocal, and feedback pathways, that complicate efforts to elucidate the roles of specific CBGT populations in the process by which evidence is accumulated and influences behavior. In this paper we apply a strategic sampling approach, based on Latin hypercube sampling, to explore how variations in CBGT network properties, including subpopulation firing rates and synaptic weights, map to variability of parameters in a normative drift diffusion model (DDM), representing algorithmic aspects of information processing during decision-making. Through the application of canonical correlation analysis, we find that this relationship can be characterized in terms of three low-dimensional control ensembles within the CBGT network that impact specific qualities of the emergent decision policy: responsiveness (a measure of how quickly evidence evaluation gets underway, associated with overall activity in corticothalamic and direct pathways), pliancy (a measure of the standard of evidence needed to commit to a decision, associated largely with overall activity in components of the indirect pathway of the basal ganglia), and choice (a measure of commitment toward one available option, associated with differences in direct and indirect pathways across action channels). These analyses provide mechanistic predictions about the roles of specific CBGT network elements in tuning the way that information is accumulated and translated into decision-related behavior.

The credit assignment problem in cortico-basal ganglia-thalamic networks: A review, a problem and a possible solution.

The question of how cortico-basal ganglia-thalamic (CBGT) pathways use dopaminergic feedback signals to modify future decisions has challenged computational neuroscientists for decades. Reviewing the literature on computational representations of dopaminergic corticostriatal plasticity, we show how the field is converging on a normative, synaptic-level learning algorithm that elegantly captures both neurophysiological properties of CBGT circuits and behavioral dynamics during reinforcement learning. Unfortunately, the computational studies that have led to this normative algorithmic model have all relied on simplified circuits that use abstracted action-selection rules. As a result, the application of this corticostriatal plasticity algorithm to a full model of the CBGT pathways immediately fails because the spatiotemporal distance between integration (corticostriatal circuits), action selection (thalamocortical loops) and learning (nigrostriatal circuits) means that the network does not know which synapses should be reinforced to favor previously rewarding actions. We show how observations from neurophysiology, in particular the sustained activation of selected action representations, can provide a simple means of resolving this credit assignment problem in models of CBGT learning. Using a biologically realistic spiking model of the full CBGT circuit, we demonstrate how this solution can allow a network to learn to select optimal targets and to relearn action-outcome contingencies when the environment changes. This simple illustration highlights how the normative framework for corticostriatal plasticity can be expanded to capture macroscopic network dynamics during learning and decision-making.

Neural activity during a simple reaching task in macaques is counter to gating and rebound in basal ganglia-thalamic communication.

Task-related activity in the ventral thalamus, a major target of basal ganglia output, is often assumed to be permitted or triggered by changes in basal ganglia activity through gating- or rebound-like mechanisms. To test those hypotheses, we sampled single-unit activity from connected basal ganglia output and thalamic nuclei (globus pallidus-internus [GPi] and ventrolateral anterior nucleus [VLa]) in monkeys performing a reaching task. Rate increases were the most common peri-movement change in both nuclei. Moreover, peri-movement changes generally began earlier in VLa than in GPi. Simultaneously recorded GPi-VLa pairs rarely showed short-time-scale spike-to-spike correlations or slow across-trials covariations, and both were equally positive and negative. Finally, spontaneous GPi bursts and pauses were both followed by small, slow reductions in VLa rate. These results appear incompatible with standard gating and rebound models. Still, gating or rebound may be possible in other physiological situations: simulations show how GPi-VLa communication can scale with GPi synchrony and GPi-to-VLa convergence, illuminating how synchrony of basal ganglia output during motor learning or in pathological conditions may render this pathway effective. Thus, in the healthy state, basal ganglia-thalamic communication during learned movement is more subtle than expected, with changes in firing rates possibly being dominated by a common external source.

Multi-site stimulation of subthalamic nucleus diminishes thalamocortical relay errors in a biophysical network model.

This paper presents results on a computational study of how multi-site stimulation of the subthalamic nucleus (STN), within the basal ganglia, can improve the fidelity of thalamocortical (TC) relay in a parkinsonian network model. In the absence of stimulation, the network model generates activity featuring synchronized bursting by clusters of neurons in the STN and internal segment of the globus pallidus (GPi), as occurs experimentally in parkinsonian states. This activity yields rhythmic inhibition from GPi to TC neurons, which compromises TC relay of excitatory inputs. We incorporate two types of multi-site STN stimulation into the network model. One stimulation paradigm features coordinated reset pulses that are on for different subintervals of each period at different sites. The other is based on a filtered version of the local field potential recorded from the STN population. Our computational results show that both types of stimulation significantly diminish TC relay errors; the former reduces the rhythmicity of the net GPi input to TC neurons and the latter reduces, but does not eliminate, STN activity. Both types of stimulation represent promising directions for possible therapeutic use with Parkinson's disease patients.

Calcium-activated nonspecific cation current and synaptic depression promote network-dependent burst oscillations.

Central pattern generators (CPGs) produce neural-motor rhythms that often depend on specialized cellular or synaptic properties such as pacemaker neurons or alternating phases of synaptic inhibition. Motivated by experimental evidence suggesting that activity in the mammalian respiratory CPG, the preBötzinger complex, does not require either of these components, we present and analyze a mathematical model demonstrating an unconventional mechanism of rhythm generation in which glutamatergic synapses and the short-term depression of excitatory transmission play key rhythmogenic roles. Recurrent synaptic excitation triggers postsynaptic Ca(2+)-activated nonspecific cation current (I(CAN)) to initiate a network-wide burst. Robust depolarization due to I(CAN) also causes voltage-dependent spike inactivation, which diminishes recurrent excitation and thus attenuates postsynaptic Ca(2+) accumulation. Consequently, activity-dependent outward currents-produced by Na/K ATPase pumps or other ionic mechanisms-can terminate the burst and cause a transient quiescent state in the network. The recovery of sporadic spiking activity rekindles excitatory interactions and initiates a new cycle. Because synaptic inputs gate postsynaptic burst-generating conductances, this rhythm-generating mechanism represents a new paradigm that can be dubbed a 'group pacemaker' in which the basic rhythmogenic unit encompasses a fully interdependent ensemble of synaptic and intrinsic components. This conceptual framework should be considered as an alternative to traditional models when analyzing CPGs for which mechanistic details have not yet been elucidated.

Thalamocortical relay fidelity varies across subthalamic nucleus deep brain stimulation protocols in a data-driven computational model.

The therapeutic effectiveness of deep brain stimulation (DBS) of the subthalamic nucleus (STN) may arise through its effects on inhibitory basal ganglia outputs, including those from the internal segment of the globus pallidus (GPi). Changes in GPi activity will impact its thalamic targets, representing a possible pathway for STN-DBS to modulate basal ganglia-thalamocortical processing. To study the effect of STN-DBS on thalamic activity, we examined thalamocortical (TC) relay cell responses to an excitatory input train under a variety of inhibitory signals, using a computational model. The inhibitory signals were obtained from single-unit GPi recordings from normal monkeys and from monkeys rendered parkinsonian through arterial 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine injection. The parkinsonian GPi data were collected in the absence of STN-DBS, under sub-therapeutic STN-DBS, and under therapeutic STN-DBS. Our simulations show that inhibition from parkinsonian GPi activity recorded without DBS-compromised TC relay of excitatory inputs compared with the normal case, whereas TC relay fidelity improved significantly under inhibition from therapeutic, but not sub-therapeutic, STN-DBS GPi activity. In a heterogeneous model TC cell population, response failures to the same input occurred across multiple TC cells significantly more often without DBS than in the therapeutic DBS case and in the normal case. Inhibitory signals preceding successful TC relay were relatively constant, whereas those before failures changed more rapidly. Computationally generated inhibitory inputs yielded similar effects on TC relay. These results support the hypothesis that STN-DBS alters parkinsonian GPi activity in a way that may improve TC relay fidelity.

High frequency stimulation of the subthalamic nucleus eliminates pathological thalamic rhythmicity in a computational model.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) or the internal segment of the globus pallidus (GPi) has recently been recognized as an important form of intervention for alleviating motor symptoms associated with Parkinson's disease, but the mechanism underlying its effectiveness remains unknown. Using a computational model, this paper considers the hypothesis that DBS works by replacing pathologically rhythmic basal ganglia output with tonic, high frequency firing. In our simulations of parkinsonian conditions, rhythmic inhibition from GPi to the thalamus compromises the ability of thalamocortical relay (TC) cells to respond to depolarizing inputs, such as sensorimotor signals. High frequency stimulation of STN regularizes GPi firing, and this restores TC responsiveness, despite the increased frequency and amplitude of GPi inhibition to thalamus that result. We provide a mathematical phase plane analysis of the mechanisms that determine TC relay capabilities in normal, parkinsonian, and DBS states in a reduced model. This analysis highlights the differences in deinactivation of the low-threshold calcium T -current that we observe in TC cells in these different conditions. Alternative scenarios involving convergence of thalamic signals in the cortex are also discussed, and predictions associated with these results, including the occurrence of rhythmic rebound bursts in certain TC cells in parkinsonian states and their drastic reduction by DBS, are stated. These results demonstrate how DBS could work by increasing firing rates of target cells, rather than shutting them down.