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. In a number of systems including wind detection in the cricket, visual motion perception and coding of arm movement direction in the monkey and place cell response to position in the rat hippocampus, firing rates in a population of tuned neurons are correlated with a vector quantity. We examine and compare several methods that allow the coded vector to be reconstructed from measured firing rates. In cases where the neuronal tuning curves resemble cosines, linear reconstruction methods work as well as more complex statistical methods requiring more detailed information about the responses of the coding neurons. We present a new linear method, the optimal linear estimator (OLE), that on average provides the best possible linear reconstruction. This method is compared with the more familiar vector method and shown to produce more accurate reconstructions using far fewer recorded neurons. Introduction To determine how information is represented by nervous systems, we need to understand ...

In the classical feed-forward, modular view of visual processing, the primary visual cortex (area V1) is a module that serves to extract local features such as edges and bars. Representation and recognition of objects are thought to be functions of higher extrastriate cortical areas. This paper presents neurophysiological data that show the later part of V1 neurons' responses reflecting higher order perceptual computations related to Ullman's (Cognition 1984;18:97 -- 159) visual routines and Marr's (Vision NJ: Freeman 1982) full primal sketch, 2 1 2 D sketch and 3D model. Based on theoretical reasoning and the experimental evidence, we propose a possible reinterpretation of the functional role of V1. In this framework, because of V1 neurons' precise encoding of orientation and spatial information, higher level perceptual computations and representations that involve high resolution details, fine geometry and spatial precision would necessarily involve V1 and be reflected in the later...

Processing of retinal images is carried out in the myriad dendritic arborizations of cortical neurons. Such processing involves complex dendritic integration of numerous inputs, and the subsequent output is transmitted to multiple targets by extensive axonal arbors. Thus far, details of this intricate processing remained unexaminable. This report describes the usefulness of real-time optical imaging in the study of population activity and the exploration of cortical dendritic processing. In contrast to single-unit recordings, optical sig-nals primarily measure the changes in transmembrane po-tential of a population of neuronal elements, including the often elusive subthreshold synaptic potentials that impinge on the extensive arborization of cortical cells. By using small visual stimuli with sharp borders and real-time imaging of cortical responses, we found that shortly

Abstract. Networks of compartmental model neurons were used to investigate the biophysical basis of the synchronization observed between sparsely-connected neurons in neocortex. A model of a single column in layer 5 consisted of 100 model neurons: 80 pyramidal and 20 inhibitory. The pyramidal cells had conductances that caused intrinsic repetitive bursting at different frequencies when driven with the same input. When connected randomly with a connection density of lo%, a single model column displayed synchronous oscillatory action potentials in response to stationary, uncorrelated Poisson spike-train inputs. Synchrony required a high ratio of inhibitory to excitatory synaptic strength; the optimal ratio was 4: 1, within the range observed in cortex. The synchrony was insensitive to variation in amplitudes of postsynaptic potentials and synaptic delay times, even when the mean synaptic delay times were varied over the range 1 to 7 ms. Synchrony was found to be sensitive to the strength of reciprocal inhibition between the inhibitory neurons in one column: Too weak or too strong reciprocal inhibition degraded intra-columnar synchrony. The only parameter that affected the oscillation frequency of the network was the strength of the external driving input which could shift the frequency between 35 to 60 Hz. The same results were obtained using a model column of 1000 neurons with a connection density of 5%, except that the oscillation became more regular. Synchronization between cortical columns was studied in a model consisting of two columns with 100 model

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Selectivity and spatial distribution of signals from the receptive field surround in macaque V1 neurons. J Neurophysiol 88: 2547–2556, 2002; 10.1152/jn.00693.2001. The responsiveness of neurons in V1 is modulated by stimuli placed outside their classical receptive fields. This nonclassical surround provides input from a larger portion of the visual scene than originally thought, permitting integration of information at early levels in the visual processing stream. Signals from the surround have been reported variously to be suppressive and facilitatory, selective and unselective. We tested the specificity of influences from the surround by studying the interactions between drifting sinusoidal gratings carefully confined to conservatively defined center and surround regions. We found that the surround influence was always suppressive when the surround grating was at the neuron’s preferred orientation. Suppression tended to be stronger when the surround grating also moved in the neuron’s preferred direction, rather than its

this paper. This means that all neurons included in this study (1) responded to lines or edges much longer than the receptive field (neurons with strong end stopping were excluded), and (2) did not respond, or responded much less, when a large uniform stimulus a

and adaptation in the domain of orientation processing are among the most studied topics in the literature. However, little effort has been devoted to explaining the diverse array of experimental findings via a physiologically based model. We have started to address this issue in the framework of the recurrent model of V1 orientation selectivity and found that reported changes in V1 orientation tuning curves after learning and adaptation can both be explained with the model. Specifically, the sharpening of orientation tuning curves near the trained orientation after learning can be accounted for by slightly reducing net excitatory connections to cells around the trained orientation, while the broadening and peak shift of the tuning curves after adaptation can be reproduced by appropriately scaling down both excitation and inhibition around the adapted orientation. In addition, we investigated the perceptual consequences of the tuning curve changes induced by learning and adaptation using signal detection theory. We found that in the case of learning, the physiological changes can account for the psychophysical data well. In the case of adaptation, however, there is a clear discrepancy between the psychophysical data from alert human subjects and the physiological data from anesthetized animals. Instead, human adaptation studies can be better accounted for by the learning data from behaving animals. Our work suggests that adaptation in behaving subjects may be viewed as a short-term form of learning.

Recent studies have suggested that V1 neurons extract figures from their backgrounds, in that they respond better to interior features of figures than to equivalent features of background stimuli. This is reportedly true even when the figure boundaries are distant from the borders of the classical receptive field (RF). To test the role of V1 neurons in figure-ground segregation, we recorded their responses to texture figures on texture backgrounds, centered on the RF. The texture elements of the figures remained identical across trials, and figure boundaries were defined by orientation differences between the elements in the background texture relative to elements in the figure. For nearly all neurons (98/102), responses to a large texture figure did not differ from the responses to a uniform-texture background. Although many neurons gave enhanced responses to texture boundaries, this occurred only when the boundaries were within or close to the RF borders. Similar effects were found in V2. For neurons in V1, the limited spatial extent of the contextual modulation was not increased either at low stimulus contrast or when the animal was rewarded for detecting an orientation-defined figure. Thus, V1 neurons appear to signal texture boundaries rather than figures per se. Unexpectedly, many V1 neurons gave significant long-latency responses to texture stimuli located entirely outside the classical RF, up to 5° from the RF border in some cases. However, these responses did not depend on the stimulus forming a figure that contained the RF. Although V1 neurons are influenced by stimuli outside the classical RF, they do not appear to segregate figures from ground. Key words: figure-ground segregation; surface perception; striate cortex; visual perception; contextual modulation; nonclassical receptive field; texture segregation The responses of V1 neurons to a stimulus in the receptive field (RF) are known to be influenced by stimuli outside the RF