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Global oscillation regime change by gated inhibition

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Abstract

The role of sensory inputs in the modelling of synchrony regimes is exhibited by means of networks of spiking cells where the relative strength of the inhibitory interaction is controlled by the activation of a linear unit working as a gating variable. Adaptation to stimulus size is determined by the value of a changing length scale, modelled by the time-varying radius of a circular receptive field. In this set-up, ‘consolidation’ time intervals relevant to attentional effects are shown to depend on the dynamics governing the evolution of the introduced length scale.

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The visual system is constantly inundated with information received by the eyes, only a fraction of which seems to reach visual awareness. This selection process is one of the functions ascribed to visual attention. Although many studies have investigated the role of attention in shaping neuronal representations in the visual cortex, few have focused on attentional modulation of neuronal signals related to visual motion. Here we report that the responses of direction-selective neurons in monkey visual cortex are greatly influenced by attention, and that this modulation occurs as early in the cortical hierarchy as the level of the middle temporal visual area (MT). Our finding demonstrates a stronger and earlier influence of attention on motion processing along the dorsal visual pathway than previously recognized.
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Five important trends have emerged from recent work on computational models of focal visual attention that emphasize the bottom-up, image-based control of attentional deployment. First, the perceptual saliency of stimuli critically depends on the surrounding context. Second, a unique 'saliency map' that topographically encodes for stimulus conspicuity over the visual scene has proved to be an efficient and plausible bottom-up control strategy. Third, inhibition of return, the process by which the currently attended location is prevented from being attended again, is a crucial element of attentional deployment. Fourth, attention and eye movements tightly interplay, posing computational challenges with respect to the coordinate system used to control attention. And last, scene understanding and object recognition strongly constrain the selection of attended locations. Insights from these five key areas provide a framework for a computational and neurobiological understanding of visual attention.
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The ability of oscillating networks to synchronize despite significant separation in space, and thus time, is of biological significance, given that human gamma activity can synchronize over distances of several millimeters to centimeters during perceptual and learning tasks. We use computer simulations of networks consisting of excitatory pyramidal cells (e-cells) and inhibitory interneurons (i-cells), modeling two tonically driven assemblies separated by large (>or=8 ms) conduction delays. The results are as follows. 1) Two assemblies separated by large conduction delays can fire synchronously at beta frequency (with i-cells firing at gamma frequency) under two timing conditions: e-cells of (say) assembly 2 are still inhibited "delay + spike generation milliseconds" after the e-cell beat of assembly 1; this means that the e-cell inhibitory postsynaptic potential (IPSP) cannot be significantly shorter than the delay (2-site effect). This implies for a given decay time constant that the interneuron --> pyramidal cell conductances must be large enough. The e-cell IPSP must last longer than the i-cell IPSP, i.e., the interneuron --> pyramidal cell conductance must be sufficiently large and the interneuron --> interneuron conductance sufficiently small (local effect). 2) We define a "long-interval doublet" as a pair of interneuron action potentials-separated by approximately "delay milliseconds"-in which a) the first spike is induced by tonic inputs and/or excitation from nearby e-cells, while b) the second spike is induced by (delayed) excitation from distant e-cells. "Long-interval population doublets" (long-interval doublets of the i-cell population) are necessary for synchronized firing in our networks. Failure to produce them leads to almost anti-phase activity at gamma frequency. 3) An (almost) anti-phase oscillation is the most stable oscillation pattern of two assemblies that are separated by axonal conduction delays of approximately one-half a gamma period (delays from 8 to 17 ms in our simulations) and that are firing at gamma frequency. 4) Two assemblies separated by large conduction delays can synchronize their activity with the help of interneuron plasticity. They can also synchronize without pyramidal cell --> pyramidal cell connections being present. The presence of pyramidal cell --> pyramidal cell connections allows, however, for synchronization if other parameters are at inappropriate values for synchronization to occur. 5) Synchronization of two assemblies separated by large conduction delays with the help of interneuron plasticity is not simply due to slowing down of the oscillation frequency. It is reached with the help of a "synchronizing-weak-beat," which induces sudden changes in the oscillation period length of the two assemblies.
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After the detection of a target (T1) in a rapid stream of visual stimuli, there is a period of 400-600 msec during which a subsequent target (T2) is missed. This impairment in performance has been labeled the attentional blink. Recent theories propose that the attentional blink reflects a bottleneck in working memory consolidation such that T2 cannot be consolidated until after T1 is consolidated, and T2 is therefore masked by subsequent stimuli if it is presented while T1 is being consolidated. In support of this explanation, Giesbrecht & Di Lollo (1998) found that when T2 is the final item in the stimulus stream, no attentional blink is observed, because there are no subsequent stimuli that might mask T2. To provide a direct test of this explanation of the attentional blink, in the present study we used the P3 component of the event-related potential waveform to track the processing of T2. When T2 was followed by a masking item, we found that the P3 wave was completely suppressed during the attentional blink period, indicating that T2 was not consolidated in working memory. When T2 was the last item in the stimulus stream, however, we found that the P3 wave was delayed but not suppressed, indicating that T2 consolidation was not eliminated but simply delayed. These results are consistent with a fundamental limit on the consolidation of information in working memory.
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Feedback connections are a prominent feature of cortical anatomy and are likely to have a significant functional role in neural information processing. We present a neural network model of cortical feedback that successfully simulates neurophysiological data associated with attention. In this domain, our model can be considered a more detailed, and biologically plausible, implementation of the biased competition model of attention. However, our model is more general as it can also explain a variety of other top-down processes in vision, such as figure/ground segmentation and contextual cueing. This model thus suggests that a common mechanism, involving cortical feedback pathways, is responsible for a range of phenomena and provides a unified account of currently disparate areas of research.
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By simultaneous recording from neural responses at many different loci at the same time, we can understand the interaction between neurons, and thereby gain insight into the network properties of neural processing, instead of the functioning of individual neurons. Here we will discuss a method for recording in behaving animals that uses chronically implanted micro-electrodes that allow one to track neural responses over a long period of time. In a majority of cases, multiunit activity, which is the aggregate spiking activity of a number of neurons in the vicinity of an electrode tip, is recorded through these electrodes, and occasionally single neurons can be isolated. Here we compare the properties of multiunit responses to the responses of single neurons in the primary visual cortex. We also discuss the advantages and disadvantages of the multiunit signal as opposed to a signal of single neurons. We demonstrate that multiunit recording provides a reliable and useful technique in cases where the neurons at the electrodes have similar response properties. Multiunit recording is therefore especially valuable when task variables have an effect that is consistent across the population of neurons. In the primary visual cortex, this is the case for figure-ground segregation and visual attention. Multiunit recording also has clear advantages for cross-correlation analysis. We show that the cross-correlation function between multiunit signals gives a reliable estimate of the average single-unit cross-correlation function. By the use of multiunit recording, it becomes much easier to detect relatively weak interactions between neurons at different cortical locations.