Visual stimuli contain a limited amount of information that could potentially be used to perform a given visual task. At successive stages of visual processing, some of this information is lost and some is transmitted to higher stages. This article describes a new analysis, based on the concept of the ideal observer in signal detection theory, that allows one to trace the (low of discrimination information through the initial physiological stages of visual processing, for arbitrary spatio-chromatic stimuli. This ideal-observer analysis provides a rigorous means of measuring the information content of visual stimuli and of assessing the contribution of specific physiological mechanisms to discrimination performance. Here, the analysis is developed for the physiological mechanisms up to the level of the photoreceptor. It is shown that many psychophysical phenomena previously attributed to neural mechanisms may be explained by variations in the information content of the stimuli and by preneural mechanisms. The purpose of vision is to extract and represent information about the physical environment from the light that is emitted, transmitted, or reflected by objects and surfaces. In order to extract useful information, a visual system must be able to encode

Neurons in cortical slices emit spikes or bursts of spikes regularly in response to a suprathreshold current injection. This behavior is in marked contrast to the behavior of cortical neurons in vivo, whose response to electrical or sensory input displays a strong degree of irregularity. Correlation measurements show a significant degree of synchrony in the temporal fluctuations of neuronal activities in cortex. We explore the hypothesis that these phenomena are the result of the synchronized chaos generated by the deterministic dynamics of local cortical networks. A model of a "hypercolumn " in the visual cortex is studied. It consists of two populations of neurons, one inhibitory and one excitatory. The dynamics of the neurons is based on a Hodgkin-Huxley type model of excitable voltage-clamped cells with several cellular and synaptic conductances. A slow potassium current is included in the dynamics of the excitatory population to reproduce the observed adaptation of the spike trains emitted by these neurons. The pattern of connectivity has a spatial structure which is correlated with the internal organization of hypercolumns in orientation columns. Numerical simulations of the model show that in an appropriate parameter range, the network settles in a synchronous chaotic state, characterized by a strong temporal variability ofthe neural activity which is correlated across the hypercolumn. Strong inhibitory feedback is essential for the stabilization of this state. These results show that the cooperative dynamics of large neuronal networks are capable of generating variability and synchrony similar to those observed in cortex. Auto-correlation and cross-correlation functions of

It is not clear how information related to cognitive or psychological processes is carried by or represented in the responses of single neurons. One provocative proposal is that precisely timed spike patterns play a role in carrying such information. This would require that these spike patterns have the potential for carrying information that would not be available from other measures such as spike count or latency. We examined exactly timed (1-ms precision) triplets and quadruplets of spikes in the stimulus-elicited responses of lateral geniculate nucleus (LGN) and primary visual cortex (V1) neurons of the awake fixating rhesus monkey. Large numbers of these precisely timed spike patterns were found. Information theoretical analysis showed that the precisely timed spike patterns carried only information already available from spike count, suggesting that the number of precisely timed spike

for learn- for a wide range of visual behaviors. Familiar objects ing a wide variety of behaviors (Chen and Wise, 1995; activated fewer neurons than did novel objects, but Knight et al., 1995; Asaad et al., 1998; Parker et al., these neurons were more narrowly tuned, and the ob- 1998). Thus, it is a good candidate for studying the effects of experience. ject representation was more resistant to the effects We used a modified version of a delayed matching- of degradation, after experience. These results dem- to-sample (DMS) task that required monkeys to discrimi- onstrate a neural correlate of visual learning in the PF nate and remember each of a set of five objects (see cortex of adult monkeys. Experimental Procedures). Monkeys were briefly shown a sample object, then after a short delay, a test object. If the test object matched the sample, monkeys were Introductio

The brain is perhaps the most complex system to have ever been subjected to rigorous scientific investigation. The scale is staggering: over 1011 neurons, each making an average of 10 3 synapses, with computation occurring on scales ranging from a single dendritic spine, to an entire cortical area. Slowly, we are beginning to acquire experimental tools that can gather the massive amounts of data needed to characterize this system. However, to understand and interpret these data will also require substantial strides in inferential and statistical techniques. This dissertation attempts to meet this need, extending and applying the modern tools of latent variable modeling to problems in neural data analysis. It is divided

this article that the choice of a variability model has a major, nontrivial impact on the encoding properties of the neural population. The immense variability of individual response parameters, such as tuning widths or correlation coef#cients, has also been neglected in most previous work. Although these parameter variations are always found in empirical data, they were considered functionally insignificant, and hence theoretical studies have almost always assumed uniform parameters throughout the population. We will show here that this uniform case is unfavorable in the sense that the introduction of parameter variability improves the encoding performance

Neurons in the visual cortex of monkeys respond selectively to the disparity between the images in the two eyes. Recent recordings have shown that some of the disparity-selective neurons in the primary visual cortex and the posterior parietal cortex are modulated by the distance of fixation. A population of such gain-modulated, disparity-selective neurons forms a set of basis functions of horizontal disparity and distance of fixation that can be used as an intermediate representation for computing egocentric distance. This distributed representation is consistent with psychophysical studies of human depth perception; in contrast, neurons explicitly tuned to distance are not consistent with how we perceive distance. In a population model that includes noise in the firing rates of neurons, the perceived distance is

Cortical neurons have been reported to use both rate and temporal codes. Here we describe a novel mode in which each neuron generates exactly 0 or 1 action potentials, but not more, in response to a stimulus. We used cell-attached recording, which ensured single-unit isolation, to record responses in rat auditory cortex to brief tone pips. Surprisingly, the majority of neurons exhibited binary behavior with few multi-spike responses; several dramatic examples consisted of exactly one spike on 100 % of trials, with no trial-to-trial variability in spike count. Many neurons were tuned to stimulus frequency. Since individual trials yielded at most one spike for most neurons, the information about stimulus frequency was encoded in the population, and would not have been accessible to later stages of processing that only had access to the activity of a single unit. These binary units allow a more efficient population

Several models have been proposed for how the brain measures velocity from the output of motion-energy units. These models make some unrealistic assumptions such as the use of Gabor-shaped temporal filters, which are non causal, or flat spatial spectra, which are invalidated by existing data. We present a Bayesian model of velocity perception, which makes more realistic assumptions and allows the estimation of local retinal velocity regardless of the specific mathematical form of the spatial and temporal filters used. The model is consistent with several aspects of speed perception, such as the dependence of perceived speed

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