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

Abstract

During action selection, mammals exhibit a high degree of flexibility in adapting their decisions in response to environmental changes. Although the cortico-basal ganglia-thalamic (CBGT) network is implicated in this adaptation, it features a synaptic architecture comprising multiple feed-forward, reciprocal, and feedback pathways, complicating efforts to elucidate the roles of specific CBGT populations in the process of evidence accumulation during decision-making. In this paper we apply a strategic sampling approach, based on Latin hypercube sampling, to explore how CBGT network properties, including subpopulation firing rates and synaptic weights, map to parameters of a normative drift diffusion model (DDM) representing algorithmic aspects of information accumulation 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 impacting specific qualities of the emergent decision policy: responsiveness (associated with overall activity in corticothalamic and direct pathways), pliancy (associated largely with overall activity in components of the indirect pathway of the basal ganglia), and choice (associated with differences in direct and indirect pathways across action channels). These analyses provide key mechanistic predictions about the roles of specific CBGT network elements in shifting different aspects of decision policies.

Competing neural representations of choice shape evidence accumulation in humans

“…CBGT circuits can control decision parameters under uncertainty. Both theoretical (9,12,14,(19)(20)(21) and experimental (18) evidence suggest that the CBGT circuits play a critical role in the evidence accumulation process (for a review see 36 (22)). The canonical CBGT circuit (Fig.…”

“…Values show encoded weights for a left action. Network schematic adapted from ( 19 ). B) Firing rate profiles for D1-SPNs (left panel) and D2-SPNs (right panel) prior to stimulus onset (t=0) for a left choice.…”

“…Our model, however, has very specific predictions with regards to disruptions of each pathway within an action representation. Disrupting the balance of dSPN and iSPN efficacy should selectively impact the drift-rate (and, to a degree, onset bias; see (45)), while non-specific disruption of global iSPN efficacy across action representations should selectively disrupt boundary height (and, to a degree, accumulation onset time; see again (45)). Based on the behavioral outcomes here, as well as previous studies (7)), Thus, increasing the difference between dSPN and iSPN firing in the channel representing the new optimal-action, say by selective excitation of the relevant dSPNs, should speed up the time to resolve the credit assignment problem during learning.…”

“…coding (27)(28)(29)(30)(31) also imply that action representations them-66 selves compete. Altogether, this neuroanatomical evidence 67 suggests that competition both between and within CBGT 68 pathways controls the rate of evidence accumulation during 69 decision making (12,15,19).…”

“…

coding (27)(28)(29)(30)(31) also imply that action representations them-66 selves compete. Altogether, this neuroanatomical evidence 67 suggests that competition both between and within CBGT 68 pathways controls the rate of evidence accumulation during 69 decision making (12,15,19).

70To illustrate this process, we designed a spiking neural 71 network model of the CBGT circuits, shown in Fig. 1A, with 72 dopamine-dependent plasticity occurring at the corticostriatal synapses (17,38).

…”