In model networks of E-cells and I-cells (excitatory and inhibitory neurons) , synchronous rhythmic spiking often comes about from the interplay between the two cell groups: the E-cells synchronize the I-cells and vice versa. Under ideal conditions --- homogeneity in relevant network parameters, and all-to-all connectivity for instance --- this mechanism can yield perfect synchronization.

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

Delay-related sustained activity in the prefrontal cortex of primates, a neurological analogue

The availability of efficient and reliable simulation tools is one of the mission-critical technologies in the fast-moving field of computational neuroscience. Research indicates that higher brain functions emerge from large and complex cortical networks and their interactions. The large number of elements (neurons) combined with the high connectivity (synapses) of the biological network and the specific type of interactions impose severe constraints on the explorable system size that previously have been hard to overcome. Here we present a collection of new techniques combined to a coherent simulation tool removing the fundamental obstacle in the computational study of biological neural networks: the enormous number of synaptic contacts per neuron. Distributing an individual simulation over multiple computers enables the investigation of networks orders of magnitude larger than previously possible. The

this paper we demonstrate a prototype network of spiking neurons with realistic synaptic dynamics configured to operate as a competitive network. While this network can operate in the mean-rate mode to produce winner-take-all behavior, the temporal dependence of inhibition produces a variable selectivity to synchronous events as well as locking the output more closely in time to the input spikes. Beyond synchrony filtering, however, the network connectivity (recurrent local excitation combined with inhibition) also encourages the production of synchronous outputs

Neurons are sensitive to correlations among synaptic inputs. However, analytical models that explicitly include correlations are hard to solve analytically, so their influence on a neuron’s response has been difficult to ascertain. To gain some intuition on this problem, we studied the firing times of two simple integrate-and-fire model neurons driven by a correlated binary variable that represents the total input current. Analytic expressions were obtained for the average firing rate and coefficient of variation (a measure of spike-train variability) as functions of the mean, variance, and correlation time of the stochastic input. The results of computer simulations were in excellent agreement with these expressions. In these models, an increase in correlation time in general produces an increase in both the average firing rate and the variability of the output spike trains. However, the magnitude of the changes depends differentially on the relative values of the input mean and variance: the increase in firing rate is higher when the variance is large relative to the mean, whereas the increase in variability is higher when the variance is relatively small. In addition, the firing rate always tends to a finite limit value as the correlation time increases toward infinity, whereas the coefficient of variation typically diverges. These results suggest that temporal correlations may play a major role in determining the variability as well as the intensity of neuronal spike trains.

Working memory has been linked to elevated single neuron discharge in monkeys and to oscillatory changes in the human EEG, but the relation between these effects has remained largely unexplored. We addressed this question by measuring local field potentials and single unit activity simultaneously from multiple electrodes placed in extrastriate visual cortex while monkeys were performing a working memory task. We describe a significant enhancement in theta band energy during the delay period. Theta oscillations had a systematic effect on single neuron activity, with neurons emitting more action potentials near their preferred angle of each theta cycle. Sample-selective de-lay activity was enhanced if only action potentials emitted near the preferred theta angle were consid-ered. Our results suggest that extrastriate visual cor-tex is involved in short-term maintenance of informa-tion and that theta oscillations provide a mechanism for structuring the recurrent interaction between neurons in different brain regions that underlie working memory.

Cortical neurons have been reported to use both rate and temporal codes. Here we describe a novel mode in which each neuron generates exactly 0 or 1 action potentials, but not more, in response to a stimulus. We used cell-attached recording, which ensured single-unit isolation, to record responses in rat auditory cortex to brief tone pips. Surprisingly, the majority of neurons exhibited binary behavior with few multi-spike responses; several dramatic examples consisted of exactly one spike on 100 % of trials, with no trial-to-trial variability in spike count. Many neurons were tuned to stimulus frequency. Since individual trials yielded at most one spike for most neurons, the information about stimulus frequency was encoded in the population, and would not have been accessible to later stages of processing that only had access to the activity of a single unit. These binary units allow a more efficient population

Tiesinga – Stimulus competition by inhibitory interference 1 Stimulus competition by inhibitory interference When two stimuli are present in the receptive field of a V4 neuron, the firing rate response is between the weakest and strongest response elicited by each of the stimuli alone (Reynolds et al, 1999, Journal of Neuroscience 19:1736-1753). When attention is directed towards the stimulus eliciting the strongest response (the preferred stimulus), the response to the pair is increased, whereas the response decreases when attention is directed to the other stimulus (the poor stimulus). When attention is directed to either of the two stimuli presented alone, the firing rate remains the same or increases slightly. These experimental results were reproduced in a model of a V4 neuron under the assumption that attention modulates the activity of local interneuron networks. The V4 model neuron received stimulus-specific asynchronous excitation from V2 and synchronous inhibitory inputs from two local interneuron networks in V4. Each interneuron network was driven by stimulus-specific excitatory inputs from V2 and was modulated by a projection from the frontal eye fields. Stimulus competition was present because of a delay in arrival time of synchronous volleys from each interneuron network. For small delays, the firing rate was close to the rate elicited by the preferred stimulus alone, whereas for larger delays it approached the firing rate of the poor stimulus. When either stimulus was presented alone the neuron’s response was not altered by the change in delay. The model suggests that top-down attention biases the competition between V2 columns for control of V4 neurons by changing the relative timing of inhibition rather than by changes in the degree of synchrony of interneuron networks. The mechanism proposed here for attentional modulation of firing rate – gain modulation by inhibitory interference – is likely to have more general applicability to cortical information processing. Tiesinga – Stimulus competition by inhibitory interference 2

Many studies conducted in the Neuroscience field suggest that synchronous and oscillatory activity plays an essential role in the neural processing among cortical areas. A recent doctrine, the temporal correlation hypothesis, attempts to integrate the synchronous activities of neurons at distributed areas of the cortex to represent separate objects, overcoming what is known as the binding problem. The segregation of phase helps preserve the integrity of the synchronized neural activity as it propagates to deeper layers of the cortex. Here, the timing is crucial especially in the case where synchronized spike volleys must meet after crossing different paths in the cortex. The purpose of this work is to show that cortical circuits can act as a phase-locked segregation mechanism to desynchronize the neural responses associated with different objects. In particular, the inhibitory inter-neurons that are found in cortex give the desired behavior. As neural correlate, we employ the spike response model (SRM) on top of a localist, connectionist architecture designed for representing symbolic and relational information.