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We investigated whether synchrony between neuronal spike trains is affected by the animal's attentional state. Cross-correlation functions between pairs of spike trains in the second somatosensory cortex (SII) of three macaque monkeys trained to switch attention between a visual task and a tactile task were computed. We previously showed that the majority of recorded neuron pairs (66%) in SII cortex fire synchronously while the animals performed either task and that in a subset of neuron pairs (17%), the degree of synchrony was affected by the animal's attentional state. Of the neuron pairs that showed changes in synchrony with attention, about 80% showed increased synchrony when the animal attended to the tactile stimulus. Here, we show that peak correlation typically occurred at a delay <25 ms; most commonly the delay was close to zero. Half-widths of the correlation peaks were distributed between a few milliseconds and hundreds of milliseconds, with the majority lying <100 ms and the mode of the distribution around 20-30 ms. Maximal change in synchrony occurred mainly during the periods when the stimulus was present, and synchrony usually increased when attention was on the tactile stimulus. If periods of elevated firing rates around the motor response times were removed from the analysis, the percentage of pairs that changed the degree of synchrony with attention more than doubled (from 35 to 72%). The observed effects did not depend on details of the statistical criteria or of the time window used in the analysis.

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Unitary event analysis is a new method for detecting episodes of synchronized neural activity (Riehle, Grün, Diesmann, & Aertsen, 1997). It detects time intervals that contain coincident firing at higher rates than would be expected if the neurons fired as independent inhomogeneous Poisson processes; all coincidences in such intervals are called unitary events (UEs). Changes in the frequency of UEs that are correlated with behavioral states may indicate synchronization of neural firing that mediates or represents the behavioral state. We show that UE analysis is subject to severe limitations due to the underlying discrete statistics of the number of coincident events. These limitations are particularly stringent for low (0-10 spikes/s) firing rates. Under these conditions, the frequency of UEs is a random variable with a large variation relative to its mean. The relative variation decreases with increasing firing rate, and we compute the lowest firing rate, at which the 95% confidence interval around the mean frequency of UEs excludes zero. This random variation in UE frequency makes interpretation of changes in UEs problematic for neurons with low firing rates. As a typical example, when analyzing 150 trials of an experiment using an averaging window 100 ms wide and a 5 ms coincidence window, firing rates should be greater than 7 spikes per second.

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A potentially powerful information processing strategy in the brain is to take advantage of the temporal structure of neuronal spike trains. An increase in synchrony within the neural representation of an object or location increases the efficacy of that neural representation at the next synaptic stage in the brain; thus, increasing synchrony is a candidate for the neural correlate of attentional selection. We investigated the synchronous firing of pairs of neurons in the secondary somatosensory cortex (SII) of three monkeys trained to switch attention between a visual task and a tactile discrimination task. We found that most neuron pairs in SII cortex fired synchronously and, furthermore, that the degree of synchrony was affected by the monkey's attentional state. In the monkey performing the most difficult task, 35% of neuron pairs that fired synchronously changed their degree of synchrony when the monkey switched attention between the tactile and visual tasks. Synchrony increased in 80% and decreased in 20% of neuron pairs affected by attention.

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We propose a neural model for object-oriented attention in which various visual stimuli (shapes, colors, letters, etc.) are represented by competing, mutually inhibitory, cell assemblies. The model's response to a sequence of cue and target stimuli mimics the neural responses in infero temporal (IT) visual cortex of monkeys performing a visual search task: enhanced response during the display of the stimulus, which decays but remains above a spontaneous rate after the cue disappears. When, subsequently, a display consisting of the target and several distractors is presented, the activity of all stimulus-driven cells is initially enhanced. After a short period of time, however, the activity of the cell assembly representing the cue stimulus is enhanced while the activity of the distractors decays because of mutual competition and a small top-down "expectational" input. The model fits the measured delayed activity in IT-cortex, recently reported by Chelazzi, Miller, Duncan, and Desimone (1993a), and we suggest that such a process, which is largely independent of the number of distractors, may be used by the visual system for selecting an expected target (appearing at an uncertain location) among distractors.

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Recent physiological studies show that the spatial context of visual stimuli enhances the response of cells in primary visual cortex to weak stimuli and suppresses the response to strong stimuli. A model of orientation-tuned neurons was constructed to explore the role of lateral cortical connections in this dual effect. The differential effect of excitatory and inhibitory current and noise conveyed by the lateral connections explains the physiological results as well as the psychophysics of pop-out and contour completion. Exploiting the model's property of stochastic resonance, the visual context changes the model's intrinsic input variability to enhance the detection of weak signals.

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We propose a model for the neuronal implementation of selective visual attention based on temporal correlation among groups of neurons. Neurons in primary visual cortex respond to visual stimuli with a Poisson distributed spike train with an appropriate, stimulus-dependent mean firing rate. The spike trains of neurons whose receptive fields do not overlap with the "focus of attention" are distributed according to homogeneous (time-independent) Poisson process with no correlation between action potentials of different neurons. In contrast, spike trains of neurons with receptive fields within the focus of attention are distributed according to non-homogeneous (time-dependent) Poisson processes. Since the short-term average spike rates of all neurons with receptive fields in the focus of attention covary, correlations between these spike trains are introduced which are detected by inhibitory interneurons in V4. These cells, modeled as modified integrate-and-fire neurons, function as coincidence detectors and suppress the response of V4 cells associated with non-attended visual stimuli. The model reproduces quantitatively experimental data obtained in cortical area V4 of monkey by Moran and Desimone (1985).

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We propose a model for the neuronal implementation of selective visual attention based on the temporal structure of neuronal activity. In particular, we set out to explain the electrophysiological data from areas V4 and IT in monkey cortex of Moran and Desimone [(1985) Science, 229, 782-784] using the "temporal tagging" hypothesis of Crick and Koch [(1990a) Cold Spring Harbor Symposiums in Quantitative Biology, LV, 953-962; (1990b) Seminars in the neurosciences (pp. 1-36)]. Neurons in primary visual cortex respond to visual stimuli with a Poisson distributed spike train with an appropriate, stimulus-dependent mean firing rate. The firing rate of neurons whose receptive fields overlap with the "focus of attention" is modulated with a periodic function in the 40 Hz range, such that their mean firing rate is identical to the mean firing rate of neurons in "non-attended" areas. This modulation is detected by inhibitory interneurons in V4 and is used to suppress the response of V4 cells associated with non-attended visual stimuli. Using very simple single-cell models, we obtain quantitative agreement with Moran and Desimone's (1985) experiments.

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The only animal of which the complete neural circuitry is known at the submicroscopical level is the nematode Caenorhabditis elegans. This anatomical knowledge is complemented by functional insight from electrophysiological experiments in the related nematode Ascaris lumbricoides, which show that Ascaris motor neurons transmit signals electrotonically and not with unattenuated spikes. We developed a mathematical model for electrotonic neural networks and applied it to the motor nervous system of nematodes. This enabled us to reproduce experimental results in Ascaris quantitatively. In particular, our computed result of the velocity v approximately equal to 6 cm/s of neural excitations in the Ascaris interneurons supports the simple hypothesis that the so-called rapidly moving muscular wave is produced by a neural excitation traveling at the same speed in the interneuron as the muscular wave. In C. elegans, the computed velocity v approximately equal to 8-30 cm/s of signals in the interneurons is much larger than the observed velocity v approximately equal to 0.2 cm/s of the body wave. Therefore, the hypothesis that the muscular wave is produced by a synchronous neural excitation wave cannot hold for C. elegans. We argue that stretch receptor control is the most likely mechanism for the generation of body waves used in the locomotion of C. elegans. Extending the simulation to larger groups of neurons, we found that the neural system of C. elegans can operate purely electrotonically. We demonstrate that the same conclusion cannot be drawn for the nervous system of Ascaris, because in the long (l approximately equal to 30 cm) interneurons the electrotonic signals would be too strongly attenuated. This conclusion is not in contradiction with the experimental findings of electrotonic signal propagation in the motor neurons of Ascaris because the latter are shorter (l approximately equal to 5 cm) than the interneurons.

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We show that a frequently used numerical implementation of von Neumann boundary conditions (zero inflowing current) in cable theory is incorrect. Correct implementations are given and it is shown that they yield results in good agreement with known analytical solutions.

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1. We study the relationship between structure and function in inhibitory long-range interactions in visual cortex. The sharpening of orientation tuning with "cross-orientation inhibition" is used as an example to discuss anisotropies that are generated by long-range connections. 2. In this study, as opposed to the detailed cortex model described in a previous report, a model of the cortical orientation column structure is proposed in which cortical cells are described only by their orientation preference. 3. We present results using different geometric arrangements of orientation columns. In the simplest case, straight parallel orientation columns were used. We also utilized more realistic, curved columns generated by a simple algorithm. The results were confirmed by the study of a patch of real column structure, determined experimentally by Swindale et al. 4. A given cell receives functionally defined cross-orientation inhibition if the cell receives inhibitory input that is strongest along its nonpreferred orientation. On the other hand, a cell is said to receive structurally defined cross-orientation inhibition if the inhibition arises from source cells with an orientation preference orthogonal to that of the target cell. Even though those definitions seem to describe similar situations, we show that, in the general case, structurally defined cross-orientation inhibition does not efficiently sharpen orientation selectivity. In particular, for straight and parallel columns, structurally defined cross-orientation inhibition results in unequal amounts of inhibition for whole cell populations with different preferred orientations. 5. In more realistic column structures, we studied the question of whether structural cross-orientation inhibition could be implemented in a more efficient way. However, for the majority of cells, it is demonstrated that their nonpreferred stimulus will not preferably excite "cross-oriented" cells. Thus structural cross-orientation inhibition is not efficient in real cortical columns. 6. We propose a new mechanism called circular inhibition. In this connection scheme, a target cell receives inhibitory input from source cells that are located at a given distance (the same for all cells) from the target cell. Circular inhibition can be regarded as two-dimensional long-range lateral inhibition. As opposed to structural cross-orientation inhibition, this mechanism does not introduce unwanted anisotropies in the orientation tuning of the target cells. It is also conceptually much simpler and developmentally advantageous. It is shown that this connection scheme results in a net functional cross-orientation inhibition in all realistic column geometries. The inhibitory tuning strength obtained with circular inhibition is weak and similar to that measured in reality.(ABSTRACT TRUNCATED AT 400 WORDS)

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We develop a model of the undulatory locomotion of nematodes, in particular that of Caenorhabditis elegans, based on mechanics. The model takes into account the most important forces acting on a moving worm and allows the computer simulation of a creeping nematode. These forces are produced by the interior pressure in the liquid-filled body cavity, the elasticity of the cuticle, the excitation of certain sets of muscles and the friction between the body and its support.We propose that muscle excitation patterns can be generated by stretch receptor control. By solving numerically the equations of motion of the model of the nematode, we demonstrate that these muscle excitation patterns are suitable for the propulsion of the animal.