Information about the spatial structure of tactile stimuli is conveyed by slowly adapting type 1 (SA1) and rapidly adapting (RA) afferents innervating the skin. Here, we investigate how the spatial properties of the stimulus shape the afferent response. To that end, we present an analytical framework to characterize SA1 and RA responses to a wide variety of spatial patterns indented into the skin. This framework comprises a model of the tissue deformation produced by any three-dimensional indented spatial pattern, along with an expression that converts the deformation at the receptor site into a neural response. We evaluated 15 candidate variables for the relevant receptor deformation and found that physical quantities closely related to local membrane stretch were most predictive of the observed afferent responses. The main outcome of this study is an accurate working model of SA1 and RA afferent responses to indented spatial patterns.

Deep neural networks have revolutionized computer vision, and their object representations across layers match coarsely with visual cortical areas in the brain. However, whether these representations exhibit qualitative patterns seen in human perception or brain representations remains unresolved. Here, we recast well-known perceptual and neural phenomena in terms of distance comparisons, and ask whether they are present in feedforward deep neural networks trained for object recognition. Some phenomena were present in randomly initialized networks, such as the global advantage effect, sparseness, and relative size. Many others were present after object recognition training, such as the Thatcher effect, mirror confusion, Weber’s law, relative size, multiple object normalization and correlated sparseness. Yet other phenomena were absent in trained networks, such as 3D shape processing, surface invariance, occlusion, natural parts and the global advantage. These findings indicate sufficient conditions for the emergence of these phenomena in brains and deep networks, and offer clues to the properties that could be incorporated to improve deep networks.

Neurons in area 3b have been previously characterized using linear spatial receptive fields with spatially separated excitatory and inhibitory regions. Here, we expand on this work by examining the relationship between excitation and inhibition along both spatial and temporal dimensions and comparing these properties across anatomical areas. To that end, we characterized the spatiotemporal receptive fields (STRFs) of 32 slowly adapting type 1 (SA1) and 21 rapidly adapting peripheral afferents and of 138 neurons in cortical areas 3b and 1 using identical random probe stimuli. STRFs of peripheral afferents consist of a rapidly appearing excitatory region followed by an in-field (replacing) inhibitory region. STRFs of SA1 afferents also exhibit flanking (surround) inhibition that can be attributed to skin mechanics. Cortical STRFs had longer time courses and greater inhibition compared with peripheral afferent STRFs, with less replacing inhibition in area 1 neurons compared with area 3b neurons. The greater inhibition observed in cortical STRFs point to the existence of underlying intracortical mechanisms. In addition, the shapes of excitatory and inhibitory lobes of both peripheral and cortical STRFs remained mostly stable over time, suggesting that their feature selectivity remains constant throughout the time course of the neural response. Finally, the gradual increase in the proportion of surround inhibition from the periphery to area 3b to area 1, and the concomitant decrease in response linearity of these neurons indicate the emergence of increasingly feature-specific response properties along the somatosensory pathway.

Our everyday visual experience frequently involves searching for objects in clutter. Why are some searches easy and others hard? It is generally believed that the time taken to find a target increases as it becomes similar to its surrounding distractors. Here, I show that while this is qualitatively true, the exact relationship is in fact not linear. In a simple search experiment, when subjects searched for a bar differing in orientation from its distractors, search time was inversely proportional to the angular difference in orientation. Thus, rather than taking search reaction time (RT) to be a measure of target-distractor similarity, we can literally turn search time on its head (i.e. take its reciprocal 1/RT) to obtain a measure of search dissimilarity that varies linearly over a large range of target-distractor differences. I show that this dissimilarity measure has the properties of a distance metric, and report two interesting insights come from this measure: First, for a large number of searches, search asymmetries are relatively rare and when they do occur, differ by a fixed distance. Second, search distances can be used to elucidate object representations that underlie search - for example, these representations are roughly invariant to three-dimensional view. Finally, search distance has a straightforward interpretation in the context of accumulator models of search, where it is proportional to the discriminative signal that is integrated to produce a response. This is consistent with recent studies that have linked this distance to neuronal discriminability in visual cortex. Thus, while search time remains the more direct measure of visual search, its reciprocal also has the potential for interesting and novel insights.