Abstract—A model is presented that reproduces spiking and bursting behavior of known types of cortical neurons. The model combines the biologically plausibility of Hodgkin–Huxley-type dynamics and the computational efficiency of integrate-and-fire neurons. Using this model, one can simulate tens of thousands of spiking cortical neurons in real time (1 ms resolution) using a desktop PC. Index Terms—Bursting, cortex, Hodgkin–Huxley, PCNN, quadratic integrate-and-fire, spiking, thalamus.

We present a cortical-based model for computing the perceptual salience of contours embedded in noisy images. It has been suggested (Gilbert, 1992; Field, Hayes & Hess, 1993) that horizontal intra-cortical connections in primary visual cortex may modulate contrast detection thresholds and pre-attentive "popout ". In our model, horizontal connections mediate context-dependent facilitatory and inhibitory interactions among oriented cells. Strongly facilitated cells undergo temporal synchronization; and perceptual salience is determined by the level of synchronized activity. The model accounts for a range of reported psychophysical and physiological effects of contour salience (Polat & Sagi, 1993, 1994; Kapadia, Ito, Gilbert & Westheimer, 1995; Field et al., 1993; Kovács, Polat & Norcia, 1996; Pettet, McKee & Grzywacz, 1996). In particular, the model proposes that intrinsic properties of synchronization account for the increased salience of smooth, closed contours (Kovács & Julesz, 1993, ...

Synchrony among multicortical EEGs 2 Freeman, Gaál & Jörnsten Information transfer and integration among functionally distinct areas of cerebral cortex of oscillatory activity requires some degree of phase synchrony of the trains of action potentials that carry the information prior to the integration. However, propagation delays are obligatory. Delays vary with the lengths and conduction velocities of the axons carrying the information, causing phase dispersion. In order to determine how synchrony is achieved despite dispersion, we recorded EEG signals from multiple electrode arrays on five cortical areas in cats and rabbits, that had been trained to discriminate visual or auditory conditioned stimuli. Analysis by time-lagged correlation, multiple correlation and PCA, showed that maximal correlation was at zero lag and averaged.7, indicating that 50 % of the power in the gamma range among the five areas was at zero lag irrespective of phase or frequency. There were no stimulus-related episodes of transiently increased phase locking among the areas, nor EEG "bursts " of transiently increased amplitude above the sustained level of synchrony. Three operations were identified to account for the sustained correlation. Cortices broadcast their outputs over divergent-convergent axonal

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An increasingly large body of data exists which demonstrates that oscillations of frequency 12#80 Hz are a consequence of, or are inextricably linked to, the behaviour of inhibitory interneurons in the central nervous system. Z.Z. Z. This frequency range covers the EEG bands beta 1 (12-20 Hz), beta 2 (20-30 Hz) and gamma (30-80 Hz). The pharmacological profile of both spontaneous and sensory-evoked EEG potentials reveals a very strong influence on Z these rhythms by drugs which have direct effects on GABA receptor-mediated synaptic transmission general A .Z. anaesthetics, sedative#hypnotics or indirect effects on inhibitory neuronal function opiates, ketamine . In addition, a number of experimental models of, in particular, gamma-frequency oscillations, have revealed both common denominators for oscillation generation and function, and subtle differences in network dynamics between the different frequency ranges. Powerful computer and mathematical modelling techniques based around both clinical and experimental observations have recently provided invaluable insight into the behaviour of large networks of interconnected neurons. In particular, the mechanistic profile of oscillations generated as an emergent property of such networks, and the mathematical derivation of this complex phenomenon have much to contribute to our understanding of how and why neurons oscillate. This review will provide the reader with a brief outline of the basic properties of inhibition-based oscillations in the CNS by combining research from laboratory models, large-scale neuronal network simulations, and mathematical analysis.

prediction is made for clinical testing that wave packets are precursor to states of awareness. They are not by themselves accessible to experience, as may be the macroscopic states initiated by global state transitions. Keywords: EEG; meaning; mesoscopic neurodynamics; phase cone; state transition; wave packet. 1. Introduction Brain systems operate on many levels of organization, each with its own scales of time and space. Dynamics applies to every level from the atomic to the molecular, and from macromolecular organelles to the neurons that incorporate them. In turn neurons form populations, these form the subassemblies in brains, and so on to embodied brains interacting intentionally with material, interpersonal, and social environments. Each level is macroscopic to that below it and microscopic to that above it. Among the most di#cult tasks scientists face are those of conceiving and describing the exchanges between levels, seeing that the measures of time 3 and distance ar

It has been suggested that long-range intrinsic connections in striate cortex may play a role in contour extraction (Gilbert et al., 1996). A number of recent physiological and psychophysical studies have examined the possible role of long range connections in the modulation of contrast detection thresholds (Polat and Sagi, 1993,1994; Kapadia et al., 1995; Kovács and Julesz, 1994) and various pre-attentive detection tasks (Kovács and Julesz, 1993; Field et al., 1993). We have developed a network architecture based on the anatomical connectivity of striate cortex, as well as the temporal dynamics of neuronal processing, that is able to reproduce the observed experimental results. The network has been tested on real images and has applications in terms of identifying salient contours in automatic image processing systems. 1 INTRODUCTION Vision is an active process, and one of the earliest, preattentive actions in visual processing is the identification of the salient contours in a scene...

Abstract. Synchronous rhythmic spiking in neuronal networks can be brought about by the interaction between E-cells and I-cells (excitatory and inhibitory cells): The I-cells gate and synchronize the E-cells, and the E-cells drive and synchronize the I-cells. We refer to rhythms generated in this way as “PING ” (Pyramidal-Interneuronal Gamma) rhythms. The PING mechanism requires that the drive II to the I-cells be sufficiently low; the rhythm is lost when II gets too large. This can happen in (at least) two different ways. In the first mechanism, the I-cells spike in synchrony, but get ahead of the E-cells, spiking without being prompted by the E-cells. We call this phase walkthrough of the I-cells. In the second mechanism, the I-cells fail to synchronize, and their activity leads to complete suppression of the E-cells. Noisy spiking in the E-cells, generated by noisy external drive, adds excitatory drive to the I-cells and may lead to phase walkthrough. Noisy spiking in the I-cells adds inhibition to the E-cells, and may lead to suppression of the E-cells. An analysis of the conditions under which noise leads to phase walkthrough of the I-cells or suppression of the E-cells shows that PING rhythms at frequencies far below the gamma range are robust to noise only if network parameter values are tuned very carefully. Together with an argument explaining why the PING mechanism

We present a model of contour extraction in which the perceptual salience of contours arises from long-range interactions between orientation-selective filters. Ullman [19], Zucker [22, 23] and colleagues have previously shown that salient contours may be extracted from noisy images by using a number of heuristic features. Our algorithm is based on cortical mechanisms, and simulations show close agreement with results from recent anatomical, physiological and psychophysical studies including recent results of Field et al. [3], Kovács et al. [10, 11, 12], and Kapadia et al., [8]. The performance of the algorithm is demonstrated on a range of psychophysical stimuli and real images. 1. Introduction Contour extraction has been the focus of many previous computational studies, yet it remains a difficult problem in practice. As pointed out by Sha'ashua and Ullman [19], the relative salience of the contours in an image suggests something about the cortical mechanisms used to extract them. Sh...

of spontaneous and thalamically evoked gamma oscillations in electrical stimulation of subdivisions of the medial genicuauditory cortex. J. Neurophysiol. 79: 2875–2884, 1998. The pur- late nucleus (MG) inhibits spontaneous gamma oscillations pose of this study was to investigate interactions among laminar (Barth and MacDonald 1996), whereas similar stimulation cell populations producing spontaneous and evoked high-frequency of the nonspecific posterior intralaminar nucleus (PIL) (Ç40 Hz) gamma oscillations in auditory cortex. Electrocortical oscillations were recorded using a 64-channel epipial electrode (Barth and MacDonald 1996; Brett and Barth 1997) and the array and a 16-channel linear laminar electrode array while electri- auditory sector of the thalamic reticular nucleus (MacDonald cal stimulation was delivered to the posterior intralaminar (PIL) et al. 1997) evokes intense and focal gamma oscillations in nucleus. Spontaneous gamma oscillations, and those evoked by auditory cortex. PIL stimulation, are confined to a location overlapping primary and Our laboratory has developed methods for performing secondary auditory cortex. Current source-density and principal components analysis of laminar recordings at this site indicate that the auditory evoked potential (AEP) complex is characterized by