Glutamatergic inputs in thalamus and cortex can be classified into two categories: Class 1( driver) and Class 2 (modulator). Following the logic that identifying driver pathways in thalamus and cortex permit insights into information processing leads to the conclusion that there are two types of thalamic relay: first order nuclei like the LGN receive driver input from a subcortical source (i.e., retina), whereas higher order nuclei like the pulvinar relay driver input from layer 5 of one cortical area to another. This thalamic division is also seen in other sensory systems: for the somatosensory system, first order is VPM/L and higher order is POm; and for the auditory system, first order is MGBv and higher order is MGBd. Furthermore, this first and higher order classification extends beyond sensory systems. Indeed, it appears that most of thalamus by volume consists of higher order relays. Many, and perhaps all, direct driver connections between cortical areas are paralleled by an indirect cortico-thalamo-cortical (transthalamic) driver route involving higher order thalamic relays. Such thalamic relays represent a heretofore unappreciated role in cortical functioning, and this assessment challenges and extends conventional views both regarding the role of thalamus and mechanisms of corticocortical communication. Evidence for this transthalamic circuit as well as speculations as to why these two parallel routes exist will be offered.
All thalamic inputs that are relayed to cortex come in axons that also send a branch to motor structures. Thus, cortex receives information from sensory receptors about the body and the world and about subcortical activity from first order thalamic relays (see Sherman abstract) and about cortical processing of those inputs from higher order relays. In addition cortex also receives from all of these inputs copies of instructions for upcoming actions (efference copies) that are on their way to execution in the motor branches. That is, essentially all the information that cortex receives from thalamus, i.e. most of the information that cortex receives, concerns sensorimotor contingencies (O'Regan and No, 2001, Behav. Brain Sci, 24,939-973), not purely sensory information. 'Sensory' here refers to past, 'motor' to future events. Wolpert & Miall, (1996, Neural Netw., 9:1265-1279) discuss how efference copies generate "forward models" about upcoming actions. Thalamus, as a gate controlling information transfer to cortex, controls generation of ubiquitous cortical forward models. Vukadinovic (2012, EJN, 34,1031-9) relates the thalamic gate to control of forward models, arguing that a closed gate prevents actions from being recognized as generated by the organism, i.e. the self, and suggesting that this links functional and structural abnormalities of the thalamus to some symptoms of schizophrenia; Rolfs et al. (2011, Nature Neuroscience 14: 252-256) demonstrate the role of forward models in attention. The ubiquity of efference copies in thalamocortical circuits suggests that key problems of the self and of attention depend on readily identifiable thalamocortical pathways.
Interactions between frontal cortex and basal ganglia are instrumental in supporting motivated control over action and learning. Computational models have been proposed at multiple levels of description, from biophysics up to algorithmic approaches. I will describe recent attempts to link across levels of description to develop on the one hand, mechanistic neural models with sufficient detail to make predictions about electrophysiology, pharmacology and genetic manipulations, and on the other hand, higher level computational descriptions which often have normative interpretations and, pragmatically, are more suited to quantitatively fit behavioral data. By fitting outputs of neural models with reduced versions, one can derive predictions about how parametric variation of particular neural mechanisms should give rise to observable change in latent computational parameters -- even if the two levels are not perfectly isomorphic. Examples include the impact of dopamine on learning and choice incentive, prefrontal-subthalamic modulation of decision thresholds, and hierarchical control over actions across multiple corticostriatal circuits. In each case, the (optimistic) result is a better understanding of the domain than that afforded by either level of model alone.
I will describe a range of models, from cellular to cortical scales, that illuminate how we accumulate evidence and make simple decisions. Large networks composed of individual spiking neurons can capture biophysical details of synaptic transmission and neuromodulation, but their complexity renders them opaque to analysis. Employing methods of mean field and dynamical systems theory, I will argue that these high-dimensional stochastic differential equations can be approximately to simple drift-diffusion (DD) processes like those used to fit behavioral data in cognitive psychology. The DD models are analytically tractable, coincide with optimal methods from statistical decision theory, and prompt new experiments as well as questions on why we fail to optimize. If time permits, I will describe work in progress on a multi-area model of attention and descision making.
The talk will draw on joint work with Fuat Balci, Rafal Bogacz, Jonathan Cohen, Philip Eckhoff, Sam Feng, Mike Schwemmer, Eric Shea-Brown, Patrick Simen, Marieke van Vugt, KongFatt Wong-Lin and Miriam Zacksenhouse.
Research supported by NIMH and AFOSR.
Neural integrators -- what do we need and what can we get away with?
Consciousness as a decision to engage