Biomedical subjects

David J Tolhurst

Publications and source records attributed to David J Tolhurst.

6 recordsLinked to original sources

PubMed2005

Do we know what the early visual system does?

We can claim that we know what the visual system does once we can predict neural responses to arbitrary stimuli, including those seen in nature. In the early visual system, models based on one or more linear receptive fields hold promise to achieve this goal as long as the models include nonlinear mechanisms that control responsiveness, based on stimulus context and history, and take into account the nonlinearity of spike generation. These linear and nonlinear mechanisms might be the only essential determinants of the response, or alternatively, there may be additional fundamental determinants yet to be identified. Research is progressing with the goals of defining a single "standard model" for each stage of the visual pathway and testing the predictive power of these models on the responses to movies of natural scenes. These predictive models represent, at a given stage of the visual pathway, a compact description of visual computation. They would be an invaluable guide for understanding the underlying biophysical and anatomical mechanisms and relating neural responses to visual perception.

Animals↗

PubMed2004

Independent components of color natural scenes resemble V1 neurons in their spatial and color tuning.

It has been hypothesized that mammalian sensory systems are efficient because they reduce the redundancy of natural sensory input. If correct, this theory could unify our understanding of sensory coding; here, we test its predictions for color coding in the primate primary visual cortex (V1). We apply independent component analysis (ICA) to simulated cone responses to natural scenes, obtaining a set of colored independent component (IC) filters that form a redundancy-reducing visual code. We compare IC filters with physiologically measured V1 neurons, and find great spatial similarity between IC filters and V1 simple cells. On cursory inspection, there is little chromatic similarity; however, we find that many apparent differences result from biases in the physiological measurements and ICA analysis. After correcting these biases, we find that the chromatic tuning of IC filters does indeed resemble the population of V1 neurons, supporting the redundancy-reduction hypothesis.

Color Perception↗

PubMed2003

Organization of the visual cortex in human albinism.

In albinism there is an abnormal projection of part of the temporal retina to the visual cortex contralateral to the eye. This projection, together with the normally routed fibers from nasal retina, provides a cortical hemisphere with visual input from more than the normal hemifield of visual space. In many mammalian models of albinism, a possible sensory mismatch in the visual cortex is avoided either by reorganization of the thalamocortical connections to give the abnormal input an exclusive cortical representation, or by the abnormal input being substantially suppressed. In this study we examine, with fMRI, how the human visual cortex topographically maps its input in albinism. We find that the input from temporal retina is not substantially suppressed and forms a retinotopic mapping that is superimposed on the mapping of the nasal retina in striate and extrastriate areas. The abnormal routing of temporal fibers is not total, with the line of decussation shifting to between 6 and 14 degrees into temporal retina. Our results indicate that the abnormal input to visual cortex in human albinism does not undergo topographic reorganization between the thalamus and cortex. Furthermore, the abnormal input is not significantly suppressed in either striate or extrastriate areas. The topographic mapping that we report in human does not conform, therefore, to the commonly observed patterns in other mammals but takes the form of the "true albino" pattern that has been reported rarely in cat and in the only other individual primate studied.

Adult↗

PubMed2003

Relay of visual information to the lateral geniculate nucleus and the visual cortex in albino ferrets.

The abnormal organization of the central visual pathways in the albino ferret has been characterized anatomically and physiologically. Recordings in dorsal lateral geniculate nucleus of the albino ferret show that lamina A1, which receives an aberrant projection from the contralateral eye, contains an extensive representation of the ipsilateral visual hemifield with receptive fields located up to 35 degrees from the vertical meridian. This is not the case in pigmented ferrets, for which the vast majority of units, activated through either the contralateral or ipsilateral eye, have receptive fields confined to the contralateral hemifield. The few fields found in the ipsilateral hemifield are driven through the contralateral eye and none is more than 10 degrees from the midline. Cortical topography was studied by making closely spaced electrode penetrations across the area 17/18 border. In pigmented animals, the reversal of topography at the border is characterized by units with receptive fields centered a few degrees into the ipsilateral hemifield. In 22 of 25 albinos, the "Boston" aberrant topography was found: the representation of the vertical meridian is within area 17, rather than at the area 17/18 border. Instead, at the area 17/18 border, there is a reversal in the topographic progression at up to 30 degrees into the ipsilateral hemifield. This pattern was most pronounced in the upper visual field. In agreement with the "Boston" physiology, injections of retrograde tracer made in area 17 usually label neurons in either lamina A or the part of lamina A1 that is aberrantly innervated by the contralateral eye. A column of labeled cells extending through all geniculate layers is rarely seen in albinos, although this is commonly the pattern in pigmented ferrets.

Action Potentials↗

PubMed2003

The receptive-field organization of simple cells in primary visual cortex of ferrets under natural scene stimulation.

The responses of simple cells in primary visual cortex to sinusoidal gratings can primarily be predicted from their spatial receptive fields, as mapped using spots or bars. Although this quasilinearity is well documented, it is not clear whether it holds for complex natural stimuli. We recorded from simple cells in the primary visual cortex of anesthetized ferrets while stimulating with flashed digitized photographs of natural scenes. We applied standard reverse-correlation methods to quantify the average natural stimulus that invokes a neuronal response. Although these maps cannot be the receptive fields, we find that they still predict the preferred orientation of grating for each cell very well (r = 0.91); they do not predict the spatial-frequency tuning. Using a novel application of the linear reconstruction method called regularized pseudoinverse, we were able to recover high-resolution receptive-field maps from the responses to a relatively small number of natural scenes. These receptive-field maps not only predict the optimum orientation of each cell (r = 0.96) but also the spatial-frequency optimum (r = 0.89); the maps also predict the tuning bandwidths of many cells. Therefore, our first conclusion is that the tuning preferences of the cells are primarily linear and constant across stimulus type. However, when we used these maps to predict the actual responses of the cells to natural scenes, we did find evidence of expansive output nonlinearity and nonlinear influences from outside the classical receptive fields, orientation tuning, and spatial-frequency tuning.

Action Potentials↗

PubMed2003

Coding of the contrasts in natural images by visual cortex (V1) neurons: a Bayesian approach.

Individual V1 neurons respond dynamically over only limited ranges of stimulus contrasts, yet we can discriminate contrasts over a wide range. Different V1 neurons cover different parts of the contrast range, and the information they provide must be pooled somehow. We describe a probabilistic pooling model that shows that populations of neurons with contrast responses like those in cat and monkey V1 would most accurately code contrasts in the range actually found in natural scenes. The pooling equation is similar to Bayes's equation; however, explicit inclusion of prior probabilities in the inference increases coding accuracy only slightly.

Animals↗