Biomedical subjects

D L Ringach

Publications and source records attributed to D L Ringach.

10 recordsLinked to original sources

PubMed2002

Dynamics of spatial frequency tuning in macaque V1.

Spatial frequency tuning in the lateral geniculate nucleus of the thalamus (LGN) and primary visual cortex (V1) differ substantially. LGN responses are largely low-pass in spatial frequency, whereas the majority of V1 neurons have bandpass characteristics. To study this transformation in spatial selectivity, we measured the dynamics of spatial frequency tuning using a reverse correlation technique. We find that a large proportion of V1 cells show inseparable responses in spatial frequency and time. In several cases, tuning becomes more selective over the course of the response, and the preferred spatial frequency shifts from low to higher frequencies. Many responses also show suppression at low spatial frequencies, which correlates with the increases in response selectivity and the shifts of preferred spatial frequency. These results indicate that suppression plays an important role in the generation of bandpass selectivity in V1.

Action Potentials↗

PubMed1999

Contrast's effect on spatial summation by macaque V1 neurons.

Stimulation outside the receptive field of a primary visual cortical (V1) neuron reveals intracortical neural interactions. However, previous investigators implicitly or explicitly considered the extent of cortical spatial summation and, therefore, the size of the classical receptive field to be fixed and independent of stimulus characteristics or of surrounding context. On the contrary, we found that the extent of spatial summation in macaque V1 neurons depended on contrast, and was on average 2.3-fold greater at low contrast. This adaptive increase in spatial summation at low contrast was seen in cells throughout V1 and was independent of surround inhibition.

Adaptation, Physiological↗

PubMed1998

Tuning of orientation detectors in human vision.

A new method to study the tuning of orientation detectors in the human visual system is proposed. The stimulus consists of a sequence of flashed sinusoidal gratings of random orientations and spatial phases shown at a fast presentation rate. The subject's task is to report, as fast as possible, when the presence of a particular orientation (horizontal, vertical, or oblique) is seen in the stimulus sequence by pressing a button. The data are analyzed by calculating the empirical distribution of orientations present in the stimulus sequence within an optimal time-window before the button was pressed. The resulting orientation distributions show a "Mexican hat" shape, which resembles the distributions obtained in some single neurons of monkey primary visual cortex using a similar method (Ringach et al., 1997). The findings are consistent with the idea of "lateral inhibition" between neighboring detectors in the orientation domain.

Female↗

PubMed1997

Dynamics of orientation tuning in macaque primary visual cortex.

Orientation tuning of neurons is one of the chief emergent characteristics of the primary visual cortex, V1. Neurons of the lateral geniculate nucleus, which comprise the thalamic input to V1, are not orientation-tuned, but the majority of V1 neurons are quite selective. How orientation tuning arises within V1 is still controversial. To study this problem, we measured how the orientation tuning of neurons evolves with time using a new method: reverse correlation in the orientation domain. Orientation tuning develops after a delay of 30-45 milliseconds and persists for 40-85 ms. Neurons in layers 4C alpha or 4C beta, which receive direct input from the thalamus, show a single orientation preference which remains unchanged throughout the response period. In contrast, the preferred orientations of output layer neurons (in layers 2, 3, 4B, 5 or 6) usually change with time, and in many cases the orientation tuning may have more than one peak. This difference in dynamics is accompanied by a change in the sharpness of orientation tuning; cells in the input layers are more broadly tuned than cells in the output layers. Many of these observed properties of output layer neurons cannot be explained by simple feedforward models, whereas they arise naturally in feedback networks. Our results indicate that V1 is more than a bank of static oriented filters; the dynamics of output layer cells appear to be shaped by intracortical feedback.

Action Potentials↗

PubMed1997

A subspace reverse-correlation technique for the study of visual neurons.

A new discrete-time reverse-correlation scheme for the study of visual neurons is proposed. The visual stimulus is generated by drawing with uniform probability, at each refresh time, an image from a finite set S of orthonormal images. We show that if the neuron can be modeled as a spatiotemporal linear filter followed by a static nonlinearity, the cross-correlation between the input image sequence and the cell's spike train output gives the projection of the receptive field onto the subspace spanned by S. The technique has been applied to the analysis of simple cells in the primary visual cortex of cats and macaque monkeys. Experimental results are presented where S spans a subspace of spatially low-pass signals. Advantages of the proposed scheme over standard white-noise techniques include improved signal to noise ratios, increased spatial resolution, and the possibility to restrict the study to particular subspaces of interest.

Animals↗

PubMed1997

Predictions of a recurrent model of orientation selectivity.

Recurrent models of orientation selectivity in the visual cortex postulate that an initially broad tuning given by the pattern of geniculate afferents is substantially sharpened by intracortical feedback. We show that these models can be tested on the basis of their predicted responses to certain visual stimuli, without the need for pharmacological or physiological manipulations. First, we consider a detailed recurrent model proposed by Somers, Nelson and Sur [(1995) Journal of Neuroscience, 15, 5448-5465] and show that it can be simplified to a single equation: a center-surround feedback filter in the orientation domain. Then, we explore the responses of the simplified model to stimuli containing two or more orientations. We find that the model exhibits peculiar responses to stimuli containing two orientations, such as plaids or crosses: if the component orientations differ by less than 45 deg the model cannot distinguish between them; if the orientations differ by more than 45 deg the model overestimates their angle by as much as 30 deg. Moreover, the model cannot signal the presence of three orientations separated by 60 deg (it responds as if there were only two orientations), and the addition of two-dimensional visual noise to an oriented stimulus results in strong spurious responses at the orthogonal orientation. We argue that the effects of attraction and repulsion between orientations and the emergence of responses at off-optimal orientations are common to a wide class of feedback models of orientation selectivity. These models could thus be tested by measuring the visual responses of cortical neurons to stimuli containing multiple orientations.

Feedback↗

PubMed1996

Binocular eye movements caused by the perception of three-dimensional structure from motion.

We report that the perception of three-dimensional structure from monocular two-dimensional images changing over time--the kinetic depth effect (KDE)--can evoke binocular eye movements consistent with a three-dimensional percept. We used a monocular KDE stimulus that induced a vivid perception of a rigid three-dimensional sphere rotating in space. The gaze directions of both eyes were measured while observers pursued the motion of a patch on the surface of the perceived sphere as it went through a complete revolution. We found that the eyes converged when the patch was perceived on the front surface of the KDE sphere and diverged when the patch was perceived in the back. The pattern, magnitude and dynamics of binocular eye movements observed in the KDE experiment resembled those obtained when subjects viewed binocularly a light-emitting diode (LED) rotating in space and to the responses obtained with a dynamic stereogram simulating a rotating random dot sphere. Thus, the perception of three-dimensional structure from motion, stereopsis, or motion and stereopsis combined, were effective in guiding binocular eye movements.

Convergence, Ocular↗

PubMed1996

Spatial and temporal properties of illusory contours and amodal boundary completion.

Spatial and temporal properties of illusory contours and amodal completion were investigated using a shape discrimination task. Performance was characterized as accuracy of angular discrimination of the inducing figures ("pacmen") in a two-alternative forced choice (2AFC) paradigm. First, we compared performance when four "pacmen" were organized into Kanizsa-like figures (squares and small deformations of squares) which produced the percept of illusory contours (ICs), with performance obtained with all four "pacmen" facing in the same direction, when no illusory contours were seen. Then, we found that it was possible to interfere with boundary completion and degrade performance with masking lines placed between the inducers of a Kanizsa figure. From these experiments we concluded that performance in the shape discrimination task depended on boundary completion. Next, the dependence of contour-dependent performance on the spatial scale of the figures was examined. Threshold angular discrimination was approximately scale-invariant and subjects were able to integrate visual information across gaps as large as 13 deg of visual angle. Performance in the shape recognition task for illusory and amodally completed figures was also measured. Similar accuracy was obtained either when the boundaries were modally or amodally completed. Finally, we used shape discrimination in conjunction with backward masking to explore the dynamics of boundary completion. Two different phases of the boundary completion process were observed. The first phase was revealed when the inducers were locally masked, and took approximately 117 msec. A second phase lasted an additional 140-200 msec after the inducers were masked.

Discrimination, Psychological↗

PubMed1995

A 'tachometer' feedback model of smooth pursuit eye movements.

A new model of smooth pursuit eye movements is presented. We begin by formally analyzing the stability of the proportional-derivative (PD) model of smooth pursuit eye movements using Pontryagin's theory. The PD model is the linearized version of the nonlinear Krauzlis-Lisberger (KL) model. We show that the PD model fails to account for the experimentally observed dependence of the eye velocity damping ratio and the oscillation period on the total delay in the feedback loop. To explain the data, a new 'tachometer' feedback model, based on an efference copy signal of eye acceleration, is proposed and analyzed by computer simulation. The model predicts some salient features of monkey pursuit data and suggests a functional role for the extraretinal input to the medial superior temporal area (MST).

Animals↗

PubMedDate not supplied

Computational modeling of orientation tuning dynamics in monkey primary visual cortex.

In the primate visual pathway, orientation tuning of neurons is first observed in the primary visual cortex. The LGN cells that comprise the thalamic input to V1 are not orientation tuned, but some V1 neurons are quite selective. Two main classes of theoretical models have been offered to explain orientation selectivity: feedforward models, in which inputs from spatially aligned LGN cells are summed together by one cortical neuron; and feedback models, in which an initial weak orientation bias due to convergent LGN input is sharpened and amplified by intracortical feedback. Recent data on the dynamics of orientation tuning, obtained by a cross-correlation technique, may help to distinguish between these classes of models. To test this possibility, we simulated the measurement of orientation tuning dynamics on various receptive field models, including a simple Hubel-Wiesel type feedforward model: a linear spatiotemporal filter followed by an integrate-and-fire spike generator. The computational study reveals that simple feedforward models may account for some aspects of the experimental data but fail to explain many salient features of orientation tuning dynamics in V1 cells. A simple feedback model of interacting cells is also considered. This model is successful in explaining the appearance of Mexican-hat orientation profiles, but other features of the data continue to be unexplained.

Action Potentials↗