Supranormal orientation selectivity of visual neurons in orientation-restricted animals

Orientation selectivity properties for the affine Gaussian derivative and the affine Gabor models for visual receptive fields

This paper presents an in-depth theoretical analysis of the orientation selectivity properties of simple cells and complex cells, that can be well modelled by the generalized Gaussian derivative model for visual receptive fields, with the purely spatial component of the receptive fields determined by oriented affine Gaussian derivatives for different orders of spatial differentiation. A detailed mathematical analysis is presented for the three different cases of either: (i) purely spatial receptive fields, (ii) space-time separable spatio-temporal receptive fields and (iii) velocity-adapted spatio-temporal receptive fields. Closed-form theoretical expressions for the orientation selectivity curves for idealized models of simple and complex cells are derived for all these main cases, and it is shown that the orientation selectivity of the receptive fields becomes more narrow, as a scale parameter ratio κ , defined as the ratio between the scale parameters in the directions perpendicular to vs. parallel with the preferred orientation of the receptive field, increases. It is also shown that the orientation selectivity becomes more narrow with increasing order of spatial differentiation in the underlying affine Gaussian derivative operators over the spatial domain. A corresponding theoretical orientation selectivity analysis is also presented for purely spatial receptive fields according to an affine Gabor model, showing that: (i) the orientation selectivity becomes more narrow when making the receptive fields wider in the direction perpendicular to the preferred orientation of the receptive field; while (ii) an additional degree of freedom in the affine Gabor model does, however, also strongly affect the orientation selectivity properties.

Coherent cortical representations develop after experience via feedforward-recurrent circuit alignment

Sensory cortical areas guide behavior by transforming stimulus-driven inputs into selective responses representing relevant features. A classic example is the representation of edge orientations in the visual cortex 1-4 , where layer 4 (L4) neurons co-activated by an orientation provide feedforward inputs to specific functional modules in layer 2/3 (L2/3) that share strong recurrent connections 5-7 . The aligned state of feedforward-recurrent interactions is critical for amplifying selective cortical responses 8-12 , but how it develops remains unclear. Using simultaneous electrophysiology and calcium imaging in visually naïve animals we find that coactivity of L4 neurons and L2/3 modular responses elicited by oriented gratings lacks the tight relationship to orientation preference found in experienced animals. One factor that could contribute to the lack of functionally specific coactivity is high variability in naïve L4 neuron responses that decreases significantly following experience. But a computational model of feedforward-recurrent interaction suggests that high variability alone is insufficient to explain the naïve state and provides a biological signature of feedforward-recurrent misalignment that we confirm with whole-cell recordings: dynamic changes in orientation tuning of L2/3 subthreshold responses shortly after stimulus onset. In conclusion, we provide diverse evidence for a realignment of feedforward-recurrent interactions following experience that is critical for building reliable sensory representations with interlaminar temporal coherence.