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. 2009 Dec 22;106(51):21894-9.
doi: 10.1073/pnas.0908383106. Epub 2009 Nov 16.

Hierarchical computation in the canonical auditory cortical circuit

Craig A Atencio  1 , Tatyana O Sharpee, Christoph E Schreiner
Affiliations

Hierarchical computation in the canonical auditory cortical circuit

Craig A Atencio et al. Proc Natl Acad Sci U S A. .

Abstract

Sensory cortical anatomy has identified a canonical microcircuit underlying computations between and within layers. This feed-forward circuit processes information serially from granular to supragranular and to infragranular layers. How this substrate correlates with an auditory cortical processing hierarchy is unclear. We recorded simultaneously from all layers in cat primary auditory cortex (AI) and estimated spectrotemporal receptive fields (STRFs) and associated nonlinearities. Spike-triggered averaged STRFs revealed that temporal precision, spectrotemporal separability, and feature selectivity varied with layer according to a hierarchical processing model. STRFs from maximally informative dimension (MID) analysis confirmed hierarchical processing. Of two cooperative MIDs identified for each neuron, the first comprised the majority of stimulus information in granular layers. Second MID contributions and nonlinear cooperativity increased in supragranular and infragranular layers. The AI microcircuit provides a valid template for three independent hierarchical computation principles. Increases in processing complexity, STRF cooperativity, and nonlinearity correlate with the synaptic distance from granular layers.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Laminar distribution of STRF response properties. (A) STAs at different cortical depths. Red indicates increasing responsiveness, and blue indicates decreasing responsiveness. Depth is indicated to the left of the STAs, and layer is indicated to the right (II/IIIa: supragranular; IIIb/IV: granular; V/VI: infragranular). Value ranges are the same for all STAs. (B) Latency depth profile for all neurons (mean/SEM in nonoverlapping 0.25-mm bins). Dashed vertical lines indicate laminar boundaries. (C) Latency in supragranular, granular, and infragranular layers. (D) Phase-locking precision. (E) firing rate. (F) Spectrotemporal separability of STAs. (G) Feature selectivity. (t test with Bonferroni correction, *, P < 0.05; **, P < 0.01).
Fig. 2.
Fig. 2.
MIDs and their nonlinearities. (A and B) First (MID1, A) and second (MID2, B) MIDs at different depths. Each row represents a neuron. (C) MID1 1D nonlinearities. Ordinate: spike rate; abscissa: similarity between the MID and stimulus. Dashed line indicates average firing rate. (D) MID2 1D nonlinearities. (E) 2D MID nonlinearities. Abscissas of C, D, and E and the ordinate of E are in units of standard deviations.
Fig. 3.
Fig. 3.
Population analysis of MIDs and nonlinearity structure across cortical depth. Data were binned according to cortical depths (filled circles; mean and SEM) and divided into supragranular, granular, and infragranular regions (gray bars). t test with Bonferroni correction, *, P < 0.05; **, P < 0.01. (A and B) Spectrotemporal separability of MID1 (A) and MID2 (B). (C) Asymmetry index of MID1 nonlinearity. (D) Separability of 2D nonlinearities. (E) Synergy of MID information. (F) Contribution of MID1 information relative to the joint MID1 and MID2 information.
Fig. 4.
Fig. 4.
Hierarchical trend analysis of laminar receptive field organization. Columns indicate granular (Gran), supragranular (Supra), and infragranular (Infra) layers. The circle size represents the relative magnitude of each parameter. Circles of different sizes represent significantly different values. A parameter was consistent (Y) with a laminar hierarchical trend if it changed monotonically from granular, to supragranular, to infragranular layers (∼ = inconclusive, implying a nonmonotonic granular to nongranular layer change; N = not consistent).
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