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The nature and origin of the temporal irregularity in the electrical activity of cortical neurons in vivo are still not well understood. We consider the hypothesis that this irregularity is due to a balance of excitatory and inhibitory currents into the cortical cells. We study a network model with excitatory and inhibitory populations of simple binary units. The internal feedback is mediated by relatively large synaptic strengths, so that the magnitude of the total excitatory as well as inhibitory feedback is much larger than the neuronal threshold. The connectivity is random and sparse. The mean number of connections per unit is large but small compared to the total number of cells in the network. The network also receives a large, temporally regular input from external sources. An analytical solution of the mean-field theory of this model which is exact in the limit of large network size is presented. This theory reveals a new cooperative stationary state of large networks, which we term a balanced state. In this state, a balance between the excitatory and inhibitory inputs emerges dynamically for a wide range of parameters, resulting in a net input whose temporal fluctuations are of the same order as its mean. The internal synaptic inputs act as a strong negative feedback, which linearizes the population responses to the external drive despite the strong nonlinearity of the individual cells. This feedback also greatly stabilizes 1 the system's state and enables it to track a time-dependent input on time scales much shorter than the time constant of a single cell. The spatio-temporal statistics of the balanced state is calculated. It is shown that the auto-correlations decay on a short time scale yielding an approximate Poissonian temporal s...

Recent neurophysiological studies have shown that primary visual cortex, or V1, does more than passively process image features using the feedforward filters suggested by Hubel and Wiesel. It also uses horizontal interactions to group features preattentively into object representations, and feedback interactions to selectively attend to these groupings. All neocortical areas, including V1, are organized into layered circuits. We present a neural model showing how the layered circuits in areas V1 and V2 enable feedforward, horizontal, and feedback interactions to complete perceptual groupings over positions that do not receive contrastive visual inputs, even while attention can only modulate or prime positions that do not receive such inputs. Recent neurophysiological data about how grouping and attention occur and interact in V1 are simulated and explained, and testable predictions are made. These simulations show how attention can selectively propagate along an object grouping and protect it from competitive masking, and how contextual stimuli can enhance or suppress groupings in a contrast-sensitive manner.

The origin of orientation selectivity in visual cortical responses is a central problem for understanding cerebral cortical circuitry. In cats, many experiments suggest that orientation selectivity arises from the arrangement of lateral geniculate nucleus (LGN) afferents to layer 4 simple cells. However, this explanation is not sufficient to account for the contrast invariance of orientation tuning. To understand contrast invariance, we first characterize the input to cat simple cells generated by the oriented arrangement of LGN afferents. We demonstrate that it has two components: a spatial-phase-specific component (i.e., one that depends on receptive field spatial phase), which is tuned for orientation, and a phase-nonspecific component, which is untuned. Both components grow with contrast. Second, we show that a correlation-based intracortical circuit,

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tion that pre- 1992). These authors found that stimuli at nonoptimal ventedthem from firing in response to the visual stimu- orientations suppressed the background activity of cor- lus. The visually evoked excitatory postsynaptic po- tical cells elevated by glutamate application or by the tentials (EPSPs) recorded during the period of cortical presentation of a conditioning stimulus at the preferred suppression, therefore, reflected largely the thalamic orientation. In addition, when GABA A -mediated inhibi- input. In 16 neurons that received monosynaptic input tion was blocked pharmacologically, the orientation se- from the thalamus, cortical suppression left 46% of lectivity of many cortical neurons was dramatically re- normal visual response on average (12%--86% in duced (Sillito, 1975; Daniels and Pettigrew, 1975; Tsumoto range). In those c

A detailed neural model is presented of how the laminar circuits of visual cortical areas V1 and V2 implement context-sensitive binding processes such as perceptual grouping and attention. The model proposes how specific laminar circuits allow the responses of visual cortical neurons to be determined not only by the stimuli within their classical receptive fields, but also to be strongly influenced by stimuli in the extra-classical surround. This context-sensitive visual processing can greatly enhance the analysis of visual scenes, especially those containing targets that are low contrast, partially occluded, or crowded by distractors. We show how interactions of feedforward, feedback and horizontal circuitry can implement several types of contextual processing simultaneously, using shared laminar circuits. In particular, we present computer simulations which suggest how top-down attention and preattentive perceptual grouping, two processes that are fundamental for visual binding, can interact, with attentional enhancement selectively propagating along groupings of both real and illusory contours, thereby showing how attention can selectively enhance object representations. These simulations also illustrate how attention may have a stronger facilitatory

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In most neural network models, synapses are treated as static weights that change only on the slow time scales of learning. It is well known, however, that synapses are highly dynamic, and show use-dependent plasticity over a wide range of time scales. Moreover, synaptic transmission is an inherently stochastic process: a spike arriving at a presynaptic terminal triggers release of a vesicle of neurotransmitter from a release site with a probability that can be much less than one. We consider

Extracellular recordings indicate that mechanisms that control contrast gain of neuronal discharge are found in the retina, thalamus and cortex. In addition, the cortex is able to adapt its contrast response function to match the average local contrast. Here we examine the neuronal mechanism of contrast adaptation by direct intracellular recordings in vivo. Both simple (n = 3) and complex cells (n = 4) show contrast adaptation during intracellular recording. For simple cells, that the amplitude of fluctuations in membrane potential induced by a drifting grating stimulus follows a contrast response relation similar to lateral geniculate relay cells, and does not reflect the high gain and adaptive properties seen in the action potential discharge of the neurons. We found no evidence of significant shunting inhibition that could explain these results. In complex cells there was no change in the mean membrane potential for different contrast stimuli or different states of adaptation, despite marked changes in discharge rate. We use a simplified electronic model to discuss the central features of our results and to explain the disparity between the contrast response functions of the membrane potential and action potential discharge in simple cells.