Between the primate and ‘reptilian’ brain: Rodent models demonstrate the role of corticostriatal circuits in decision making

Abstract

Decision making can be defined as the flexible integration and transformation of information from the external world into action. Recently, the development of novel genetic tools and new behavioral paradigms has made it attractive to study behavior of all kinds in rodents. By some perspectives, rodents are not an acceptable model for the study of decision making due to their simpler behavior often attributed to their less extensive cortical development when compared to non-human primates. We argue that decision making can be approached with a common framework across species. We review insights from comparative anatomy that suggest the expansion of cortical-striatal connectivity is a key development in evolutionary increases in behavioral flexibility. We briefly review studies that establish a role for corticostriatal circuits in integrative decision making. Finally, we provide an overview of a few recent, highly complementary rodent decision making studies using genetic tools, revealing with new cellular and temporal resolution how, when and where information can be integrated and compared in striatal circuits to influence choice.

Dopamine Does Double Duty in Motivating Cognitive Effort

“…(Collins and Frank, 2014). B) Optogenetic stimulation of striatal D1 and D2 cells in rats, during decision-making in a two-alternative reward-learning task, produce dose-dependent shifts in preference towards the contralateral option in case of D1 cell stimulation (blue) and the ipsilateral option in case of D2 stimulation (red) mimicking shifts in subjective value functions (as reviewed in Lee et al, 2015). …”

Decision Making and Sequential Sampling from Memory

“…The striatum is also well known to support the incremental updating of value representations that is essential for more habitual (rather than episodic) forms of learning (Daw et al, 2006; Eichenbaum and Cohen, 2001; Foerde and Shohamy, 2011; Glimcher and Fehr, 2013; Shohamy, 2011). Thus, the striatum is ideally positioned to funnel value-relevant information from memory to update cortical regions controlling decisions and actions (Kemp and Powell, 1971; Lee et al, 2015; Znamenskiy and Zador, 2013). …”

Towards an integration of deep learning and neuroscience

“…The answers may be influenced by diverse local properties of neurons and networks, such as homeostatic rules of neural structure, gene expression and function (Marder and Goaillard, 2006), the diversity of synapse types, cell-typespecific connectivity (Jiang et al, 2015), patterns of inter-laminar projection, distributions of inhibitory neuron types, dendritic targeting and local dendritic physiology and plasticity (Markram et al, 2015;Bloss et al, 2016;Sandler et al, 2016;Morgan et al, 2016) or local glial networks (Perea et al, 2009). They may also be influenced by the integrated nature of higher-level brain systems, including mechanisms for developmental bootstrapping (Ullman et al, 2012), information routing (Gurney et al, 2001;Stocco et al, 2010), attention (Buschman and and hierarchical decision making (Lee et al, 2015). Mapping these systems in detail is of paramount importance to understanding how the brain works, down to the nanoscale dendritic organization of ion channels and up to the real-time global coordination of cortex, striatum and hippocampus, all of which are computationally relevant in the framework we have explicated here.…”

“…But we also see a large number of specialized structures, including thalamus, hippocampus, basal ganglia and cerebellum (Solari and Stoner, 2011). Some of these structures evolutionarily pre-date (Lee et al, 2015) the cortex, and hence the cortex may have evolved to work in the context of such specialized mecha-nisms. For example, the cortex may have evolved as a trainable module for which the training is orchestrated by these older structures.…”