Interneuron Types as Attractors and Controllers

Abstract

Cortical interneurons display striking differences in shape, physiology, and other attributes, challenging us to appropriately classify them. We previously suggested that interneuron types should be defined by their role in cortical processing. Here, we revisit the question of how to codify their diversity based upon their division of labor and function as controllers of cortical information flow. We suggest that developmental trajectories provide a guide for appreciating interneuron diversity and argue that subtype identity is generated using a configurational (rather than combinatorial) code of transcription factors that produce attractor states in the underlying gene regulatory network. We present our updated three-stage model for interneuron specification: an initial cardinal step, allocating interneurons into a few major classes, followed by definitive refinement, creating subclasses upon settling within the cortex, and lastly, state determination, reflecting the incorporation of interneurons into functional circuit ensembles. We close by discussing findings indicating that major interneuron classes are both evolutionarily ancient and conserved. We propose that the complexity of cortical circuits is generated by phylogenetically old interneuron types, complemented by an evolutionary increase in principal neuron diversity. This suggests that a natural neurobiological definition of interneuron types might be derived from a match between their developmental origin and computational function.

Keywords: attractor network; cardinal specification; configurational code; gene regulatory network; interneuron development; transcription factors.

Figure 1

Distinct interneuron subtypes specialize in targeting different domains of pyramidal cells and each other. Different interneuron subtypes target distinct regions of the axo-somato-dendritic axes of pyramidal cells. Here we show a few major classes that differ not only in their targeting but also in their molecular markers, intrinsic properties, and morphology. Somatically targeting neurons can be classified into two large classes of parvalbumin (PV)-or cholecystokinin (CCK)-expressing basket cells. Chandelier cells (ChCs) target the axon initial segment. Somatostatin (SST) interneurons target the dendrites, while vasoactive intestinal peptide (VIP) interneurons target mainly SST and, to a lesser degree, PV interneurons. Neurogliaform (NGF) cells use volume transmission to provide slow inhibition to superficial layers. Inset shows depiction of interneuron targeting to pyramidal cells.

Figure 2

Diagram of cortical circuit motifs and interneuronal circuit control. (a) Cortical networks. These networks comprise complex, cell-type-specific circuits, and repeated circuit motifs based on interneuron connectivity are embedded within these. The left panel shows that most neurons are connected to multiple partners, obscuring clear patterns. The right panel shows two distinct circuit motifs centered around interneurons that may not be obvious when considered in the context of the cortical jungle. (b) Oscillatory control. The top panels show pyramidal cell ensembles that are controlled by interneurons. The middle trace shows a local field potential, representing the network state in the hippocampus. The bottom panel shows the firing of four different interneuron types that can be described in reference to the local field potential, with each subtype firing during different network states and in phase relationships. The timing of interneurons can control oscillations at the timescale of milliseconds to hundreds of milliseconds. (c) Flow control. The top panels show pyramidal cell ensembles that are controlled by interneurons. The middle panel marks the timing of four behavioral events: entry, exit, reward, and cancel. The bottom panel shows that the firing of four different cortical interneurons can be described in reference to these events on the behavioral timescale of seconds. Interneurons may provide control in the information flow by gating, gain modulation, veto, and other circuit operations. Abbreviations: CCK, cholecystokinin; ChC, chandelier cell; NGF, neurogliaform; PV, parvalbumin; SST, somatostatin; VIP, vasoactive intestinal polypeptide.

Figure 3

Survey of transcription factor (TF) expression across development showing the expression trajectory of the top 20 TFs across four sequential time points within each of the four cardinal GABAergic interneuron classes, as well as the Nos1-expressing GABAergic projection neuron type. Note that, with a few exceptions, the expression of each of these factors is highly dynamic and evolves across development in a manner consistent with the emergence of attractor dynamics underlying the maturation of each interneuron subtype. Abbreviations: E, embryonic day; P, postnatal day.

Figure 4

The developmental landscape reflects attractor dynamics. (a) Diagram showing the landscape of development as the energy function of an attractor gene regulatory network, with cells rolling down through bifurcating valleys. At the bottom, the basins of attraction provide robustness to external perturbations and confer distinct stability properties, depending on the height of the energy barrier between interneuron subtypes. (b) Schematic showing how distinct transcription factor manipulations generate distinct development landscapes, reducing barriers between attractor states and/or making them unstable. Abbreviations: NGF, nerve growth factor; PV, parvalbumin; SST, somatostatin; VIP, vasoactive intestinal polypeptide.

Figure 5

Genetic regulatory network motifs may create cell-type attractors. (a) In this hypothetical topology of a complex genetic regulatory network, transcription factors (TFs) promote or suppress each other’s expression. (b) The complexity of this network’s topology may be reduced to specific motifs in which clusters of TFs promote each other’s expression while suppressing other clusters. (c) This topology can give rise to multi-stable dynamics with valleys representing individual cell states and balls individual interneurons. (d) The dominant dynamics of regulatory networks reflect positive feedback loops within clusters of TFs and negative feedback to other clusters, with each cluster corresponding to a configurational code for a specific cell type.