Predicting the timing of spikes evoked by tactile stimulation of the hand.

What does the hand tell the brain? Tactile stimulation of the hand evokes remarkably precise patterns of neural activity, suggesting that the timing of individual spikes may convey information. However, many aspects of the transformation of mechanical deformations of the skin into spike trains remain unknown. Here we describe an integrate-and-fire model that accurately predicts the timing of individual spikes evoked by arbitrary mechanical vibrations in three types of mechanoreceptive afferent fibers that innervate the hand. The model accounts for most known properties of the three fiber types, including rectification, frequency-sensitivity, and patterns of spike entrainment as a function of stimulus frequency. These results not only shed light on the mechanisms of mechanotransduction but can be used to provide realistic tactile feedback in upper-limb neuroprostheses.

Responses to compound objects in monkey inferotemporal cortex: the whole is equal to the sum of the discrete parts.

It is commonly thought that neurons in monkey inferotemporal cortex are conjunction selective--that a neuron will respond to an image if and only if it contains a required combination of parts. However, this view is based on the results of experiments manipulating closely adjacent or confluent parts. Neurons may have been sensitive not to the conjunction of parts as such but to the presence of a unique feature created where they abut. Here, we compare responses to two sets of images, one composed of spatially separate and the other of abutting parts. We show that the influences of spatially separate parts combine, to a very close approximation, according to a linear rule. Nonlinearities are more prominent--although still weak--in responses to images composed of abutting parts.

Global image dissimilarity in macaque inferotemporal cortex predicts human visual search efficiency.

Finding a target in a visual scene can be easy or difficult depending on the nature of the distractors. Research in humans has suggested that search is more difficult the more similar the target and distractors are to each other. However, it has not yielded an objective definition of similarity. We hypothesized that visual search performance depends on similarity as determined by the degree to which two images elicit overlapping patterns of neuronal activity in visual cortex. To test this idea, we recorded from neurons in monkey inferotemporal cortex (IT) and assessed visual search performance in humans using pairs of images formed from the same local features in different global arrangements. The ability of IT neurons to discriminate between two images was strongly predictive of the ability of humans to discriminate between them during visual search, accounting overall for 90% of the variance in human performance. A simple physical measure of global similarity--the degree of overlap between the coarse footprints of a pair of images--largely explains both the neuronal and the behavioral results. To explain the relation between population activity and search behavior, we propose a model in which the efficiency of global oddball search depends on contrast-enhancing lateral interactions in high-order visual cortex.

Representing the forest before the trees: a global advantage effect in monkey inferotemporal cortex.

Hierarchical stimuli (large shapes composed of small shapes) have long been used to study how humans perceive the global and the local content of a scene--the forest and the trees. Studies using these stimuli have revealed a global advantage effect: humans consistently report global shape faster than local shape. The neuronal underpinnings of this effect remain unclear. Here we demonstrate a correlate and possible mechanism in monkey inferotemporal cortex (IT). Inferotemporal neurons signal the global content of a hierarchical display approximately 30 ms before they signal its local content. This is a specific expression of a general principle, related to spatial scale or spatial frequency rather than to hierarchical level, whereby the representation of a large shape develops in IT before that of a small shape. These findings provide support for a coarse-to-fine model of visual scene representation.

Dynamic gain changes during attentional modulation.

Attention causes a multiplicative effect on firing rates of cortical neurons without affecting their selectivity (Motter, 1993; McAdams & Maunsell, 1999a) or the relationship between the spike count mean and variance (McAdams & Maunsell, 1999b). We analyzed attentional modulation of the firing rates of 144 neurons in the secondary somatosensory cortex (SII) of two monkeys trained to switch their attention between a tactile pattern recognition task and a visual task. We found that neurons in SII cortex also undergo a predominantly multiplicative modulation in firing rates without affecting the ratio of variance to mean firing rate (i.e., the Fano factor). Furthermore, both additive and multiplicative components of attentional modulation varied dynamically during the stimulus presentation. We then used a standard conductance-based integrate-and-fire model neuron to ascertain which mechanisms might account for a multiplicative increase in firing rate without affecting the Fano factor. Six mechanisms were identified as biophysically plausible ways that attention could modify the firing rate: spike threshold, firing rate adaptation, excitatory input synchrony, synchrony between all inputs, membrane leak resistance, and reset potential. Of these, only a change in spike threshold or in firing rate adaptation affected model firing rates in a manner compatible with the observed neural data. The results indicate that only a limited number of biophysical mechanisms can account for observed attentional modulation.

Spatiotemporal receptive fields of peripheral afferents and cortical area 3b and 1 neurons in the primate somatosensory system.

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.

A continuum mechanical model of mechanoreceptive afferent responses to indented spatial patterns.

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.