Jason M. Samonds

Comparison of recordings from microelectrode arrays and single electrodes in the visual cortex

Advances in microelectrode neural recording systems have made it possible to record extracellular activity from a large number of neurons simultaneously. A substantial body of work is associated with traditional single-electrode extracellular recording, and the robustness of the recording method has

Cooperation between Area 17 Neuron Pairs Enhances Fine Discrimination of Orientation

We examined 66 complex cells in area 17 of cats that were paralyzed and anesthetized with propofol and N2O. We studied changes in ensemble responses for small (<10°) and large (>10°) differences in orientation. Examination of temporal resolution and discharge history revealed advantages in discrimination from both dependent (e.g., synchronization) and independent (e.g., bursting) interspike interval properties. For 27 pairs of neurons, we found that the average cooperation (the advantage gained from the joint activity) was 57.6% for fine discrimination of orientation but <5% for gross discrimination. Dependency (probabilistic quantification of the interaction between the cells) was measured between 29 pairs of neurons while varying orientation. On average, the dependency tuning for orientation was 35.5% narrower than the average firing rate tuning. The changes in dependency around the peak orientation (at which the firing rate remains relatively constant) lead to substantial cooperation that can improve discrimination in this region. The narrow tuning of dependency and the cooperation provide evidence to support a population-encoding scheme that is based on biologically plausible mechanisms and that could account for hyperacuities.

Cooperative and Competitive Interactions Facilitate Stereo Computations in Macaque Primary Visual Cortex

Inferring depth from binocular disparities is a difficult problem for the visual system because local features in the left- and right-eye images must be matched correctly to solve this “stereo correspondence problem.” Cortical architecture and computational studies suggest that lateral interactions among neurons could help resolve local uncertainty about disparity encoded in individual neurons by incorporating contextual constraints. We found that correlated activity among pairs of neurons in primary visual cortex depended both on disparity-tuning relationships and the stimuli displayed within the receptive fields of the neurons. Nearby pairs of neurons with distinct disparity tuning exhibited a decrease in spike correlation at competing disparities soon after response onset. Distant neuronal pairs of similar disparity tuning exhibited an increase in spike correlation at mutually preferred disparities. The observed correlated activity and response dynamics suggests that local competitive and distant cooperative interactions improve disparity tuning of individual neurons over time. Such interactions could represent a neural substrate for the principal constraints underlying cooperative stereo algorithms.

Nonlocal origin of response suppression from stimulation outside the classic receptive field in area 17 of the cat

A stimulus located outside the classic receptive field (CRF) of a striate cortical neuron can markedly influence its behavior. To study this phenomenon, we recorded from two cortical sites, recorded and peripheral, with separate electrodes in cats anesthetized with Propofol and nitrous oxide. The receptive fields of each site were discrete (2-7.3 deg between centers). A control orientation tuning (OT) curve was measured for a single recorded cell with a drifting grating. The OT curve was then remeasured while stimulating simultaneously the cells CRF as well as the peripheral site with a stimulus optimized for that location. For 22/60 cells, the peripheral stimulus suppressed the peak response and/or shifted the center of mass of the OT curve. For 19 of these 22 cells, we then reversibly blocked stimulus-driven activity at the peripheral site by iontophoretic application of GABA (0.5 M). For 6/19 cells, the response returned to control levels, implying that for these cells the inhibitory influence arose from the blocked site. The responses of nine cells remained reduced during inactivation of the peripheral site, suggesting that influence was generated outside the region of local block in area 17. This is consistent with earlier findings suggesting that modulatory influences can originate from higher cortical areas. Three cells had mixed results, suggesting multiple origins of influence. The response of each cell returned to suppressed levels after dissipation of the GABA and returned to baseline values when the peripheral stimulus was removed. These findings support a cortical model in which a cells response is modulated by an inhibitory network originating from beyond the receptive field that supplants convergence of excitatory lateral geniculate neurons. The existence of cells that exhibit no change in peripherally inhibited responses during the GABA application suggests that peripheral influences may arise from outside area 17, presumably from other cortical areas (e.g. area 18).

Relative luminance and binocular disparity preferences are correlated in macaque primary visual cortex, matching natural scene statistics

Humans excel at inferring information about 3D scenes from their 2D images projected on the retinas, using a wide range of depth cues. One example of such inference is the tendency for observers to perceive lighter image regions as closer. This psychophysical behavior could have an ecological basis because nearer regions tend to be lighter in natural 3D scenes. Here, we show that an analogous association exists between the relative luminance and binocular disparity preferences of neurons in macaque primary visual cortex. The joint coding of relative luminance and binocular disparity at the neuronal population level may be an integral part of the neural mechanisms for perceptual inference of depth from images.

Sample skewness as a statistical measurement of neuronal tuning sharpness

We propose using the statistical measurement of the sample skewness of the distribution of mean firing rates of a tuning curve to quantify sharpness of tuning. For some features, like binocular disparity, tuning curves are best described by relatively complex and sometimes diverse functions, making it difficult to quantify sharpness with a single function and parameter. Skewness provides a robust nonparametric measure of tuning curve sharpness that is invariant with respect to the mean and variance of the tuning curve and is straightforward to apply to a wide range of tuning, including simple orientation tuning curves and complex object tuning curves that often cannot even be described parametrically. Because skewness does not depend on a specific model or function of tuning, it is especially appealing to cases of sharpening where recurrent interactions among neurons produce sharper tuning curves that deviate in a complex manner from the feedforward function of tuning. Since tuning curves for all neurons are not typically well described by a single parametric function, this model independence additionally allows skewness to be applied to all recorded neurons, maximizing the statistical power of a set of data. We also compare skewness with other nonparametric measures of tuning curve sharpness and selectivity. Compared to these other nonparametric measures tested, skewness is best used for capturing the sharpness of multimodal tuning curves defined by narrow peaks (maximum) and broad valleys (minima). Finally, we provide a more formal definition of sharpness using a shape-based information gain measure and derive and show that skewness is correlated with this definition.

Real-time visualization of neural synchrony for identifying coordinated cell assemblies.

We introduce a synchrony map that translates the fine temporal organization of multi-unit responses in the visual cortex into an easily interpreted spatial display. We test the synchrony map on microelectrode array recordings in Area 17 of anesthetized and paralyzed cats. We first examine the synchrony map using averaged data and probability calculations to demonstrate orientation-dependent changes in synchrony. We then demonstrate how the synchrony map can be implemented for real-time visualization of synchrony among neural assemblies.

Spike train analysis reveals cooperation between Area 17 neuron pairs that enhances fine discrimination of orientation

We recorded from 22 pairs of neurons neurons in cats anesthetized with Propofol and N2O and paralyzed with Pavulon following established guidelines. We used type analysis (Johnson et al., 2001) to calculate the Resistor Average Kullback-Leibler distance between ensemble responses to fine ( 10deg, >0.1c/deg) variations of OR and SF from the optimal parameter (see panel below). This “distance” provides an estimate of the reduction in classification error between responses (i.e., reduction in error = 2-distance). Discharge history was incorporated into types by testing for a stable Markov order (i.e., where discharge history ceases to contribute) using conditional types on previous bins for distance calculations. PURPOSE Bursting in Area 17 is tuned more sharply than spike rate for orientation (OR) and spatial frequency (SF) (Cattaneo et al., 1981a,b). Burst length is reduced at non-optimal orientations (DeBusk et al., 1997) and leads to less efficient synaptic coupling (Snider et al., 1998). We describe how these interspike interval (ISI) properties could contribute to discriminations between spike trains for fine and gross differences in OR and SF. We also describe changes in neural dependency as a function of OR, SF, contrast, and time to demonstrate how cooperative information (synergy) arises and is transmitted.

Object Categorization: Neural Encoding of Scene Statistics for Surface and Object Inference

Features associated with an object or its surfaces in natural scenes tend to vary coherently in space and time. In psychological literature, these coherent covariations have been described as important for neural systems to acquire models of objects and object categories. From a statistical inference perspective, such coherent covariation can provide a mechanism to learn statistical priors in natural scenes that are useful for probabilistic inference. In this article, we present some neurophysiological experimental observations in the early visual cortex that provide insights into how correlation structures in visual scenes are being encoded by neuronal tuning and connections among neurons. The key insight is that correlated structures in visual scenes result in correlated neuronal activities, which shapes the tuning properties of individual neurons and the connections between them, embedding Gestalt-related computational constraints or priors for surface inference. Extending these concepts to the inferotemporal cortex suggests a representational framework that is distinct from the traditional feed-forward hierarchy of invariant object representation and recognition. In this framework, lateral connections among view-based neurons, learned from the temporal association of the object views observed over time, can form a linked graph structure with local dependency, akin to a dense aspect graph in computer vision. This web-like graph allows view-invariant object representation to be created using sparse feed-forward connections, while maintaining the explicit representation of the different views. Thus, it can serve as an effective prior model for generating predictions of future incoming views to facilitate object inference.

Evidence of Stereoscopic Surface Disambiguation in the Responses of V1 Neurons

Abstract For the important task of binocular depth perception from complex natural‐image stimuli, the neurophysiological basis for disambiguating multiple matches between the eyes across similar features has remained a long‐standing problem. Recurrent interactions among binocular disparity‐tuned neurons in the primary visual cortex (V1) could play a role in stereoscopic computations by altering responses to favor the most likely depth interpretation for a given image pair. Psychophysical research has shown that binocular disparity stimuli displayed in 1 region of the visual field can be extrapolated into neighboring regions that contain ambiguous depth information. We tested whether neurons in macaque V1 interact in a similar manner and found that unambiguous binocular disparity stimuli displayed in the surrounding visual fields of disparity‐selective V1 neurons indeed modified their responses when either bistable stereoscopic or uniform featureless stimuli were presented within their receptive field centers. The delayed timing of the response behavior compared with the timing of classical surround suppression and multiple control experiments suggests that these modulations are carried out by slower disparity‐specific recurrent connections among V1 neurons. These results provide explicit evidence that the spatial interactions that are predicted by cooperative algorithms play an important role in solving the stereo correspondence problem.