Thalamocortical Circuit Motifs: A General Framework

Abstract

The role of the thalamus in cortical sensory transmission is well known, but its broader role in cognition is less appreciated. Recent studies have shown thalamic engagement in dynamic regulation of cortical activity in attention, executive control, and perceptual decision-making, but the circuit mechanisms underlying such functionality are unknown. Because the thalamus is composed of excitatory neurons that are devoid of local recurrent excitatory connectivity, delineating long-range, input-output connectivity patterns of single thalamic neurons is critical for building functional models. We discuss this need in relation to existing organizational schemes such as core versus matrix and first-order versus higher-order relay nuclei. We propose that a new classification is needed based on thalamocortical motifs, where structure naturally informs function. Overall, our synthesis puts understanding thalamic organization at the forefront of existing research in systems and computational neuroscience, with both basic and translational applications.

Keywords: cognition; cortex; lateral geniculate nucleus; mediodorsal nucleus; thalamocortical; thalamus.

Figure 1.

Examples of various classification attempts that include partial descriptions of thalamocortical motifs. A: Core and matrix (output characteristics). The geniculocortical projection, an example of a core system, mainly targets middle cortical layers in a highly topographical manner (left), whereas the projection from the centromedian nucleus, an exemplar of a matrix system, diffusely targets upper cortical layers, chiefly layer 1 (right) and typically innervates multiple cortical areas. B: Drivers and modulators (output characteristics). Thalamocortical afferents can be either driver or modulator. C: LGN vs MD (input and output characteristics), in addition to the input differences shown in D, LGN X and Y cells receive input from very few retinal axons, often only one, and their outputs chiefly innervate excitatory cells in V1 (left), whereas some MD neurons, which receive significant convergent driver input, predominantly activate inhibitory cells in prefrontal cortex (PFC, right). D: X and Y streams through the cat’s LGN (both input and output characterization). On the input side, retinal Y axons form simple synapses onto dendritic shafts, whereas retinal X axons form triadic synaptic complexes (inset). On the output side, X axons innervate only V1, chiefly in the lower half of layer 4, whereas Y axons innervate the upper half of layer 4 and branch to innervate V2 as well. E: First and higher order (FO and HO; predominantly input characteristics) thalamocortical circuits. Driver input to FO cell derive from a a subcortical source (i.e., retina to LGN), whereas HO thalamocortical cells receive driver input from layer 5 of cortex (e.g, pulvinar from layer 5 of V1).

Figure 2:

Towards a classification system based on thalamo-cortical motifs. In panels A and B, individual and idealized single thalamic neurons are shown to illustrate the notion of the motif being a single-cell attribute (A) Example of a well-characterized thalamocortical motif, involving the retino-geniculo-cortical pathway, with the set of computations required for retinal signal transmission. X and Y geniculate neurons of the cat receive retinal inputs with minimal convergence, and therefore show similar responses to those of the retina. An important feature of this system is that the retinogeniculate connections are relatively stable in adult animals, rendering geniculate neurons stably tuned to visual features. On the output side, because geniculate neurons providing predominantly driving excitatory inputs to visual cortex, cortical responses can be largely explained as weighted sums of the thalamic output (B)Two examples of inferred motifs within the mediodorsal thalamus of the mouse. These connections are derived from statistical dependencies between the mediodorsal neurons and prefrontal ones, which are recorded in a context-switching task (Rikhye et al., 2018). These statistical dependencies have also been tested through pathway-specific optogenetic manipulations. Mediodorsal neural types can be segregated based on inputs, which are explained by the degree of their cortical input convergence. Specifically, a subset of neurons encodes a set of task-relevant prefrontal cue-selective signals over a broad temporal scale (left), and another encoding the same type of task-relevant variable but on a shorter timescale (right). Given that these response profiles shift on a session-by-session basis depending on how the context is experimentally configured, a reasonable interpretation is that the cortico-thalamic connections are highly plastic. These same mediodorsal types also segregate based on output, with the high input convergence neurons exhibiting predominantly suppressive effects on prefrontal cortical activity, while the low input convergence neurons exhibiting predominantly modulatory excitatory effects. By modulation, we do not necessarily mean that the effect is implemented through a neuromodulator, but rather that it controls the gain of effective recurrent connections in the prefrontal cortex (see equations within the figure). (C) A putative classification space for thalamocortical motifs, with the relevant geniulate and mediodorsal types plotted within. The number and distribution of points within this space are currently unknown, but we expect that their pattern will inform function and comparisons across species. Notation: the embedded equations describe the input-output transformations of the thalamocortical motifs. Lower boldface symbols denote vectors and upper boldface symbols denote matrices. r_cortex: cortical output, f(): cortical non-linearity, r^_cortex: recurrent drive, r_thal: thalamic input, g(): non-linearity over thalamic input for the middle motif, b: scaling parameter for the thalamic term in the last motif. The key idea for this formalism is that the thalamic input shows up as very different terms in the cortical computation performed. This is what is precisely meant by relay vs. non-relay functions of the thalamus.