Neural synchrony

Figure 24.3 Temporal analysis of responses measured simultaneously in five different antennal lobe neurons in the moth Manduca sexta. The matrices show patterns of neural synchrony evoked by either of two pheromone components or a binary mixture at two concentrations. The number of synchronous events was averaged over 20 trials and calculated for 500 ms from stimulus onset. The gray scale ranges from 0 to 3.8 coincident spikes per stimulus. The horizontal displays below the matrices show the averaged spiking rate in each single neuron (gray scale ranges from 0 to 5.5 spikes per stimulus period). Neural synchrony was influenced not only by the odor quality but also by both stimulus intensity and blend interactions (redrawn from Christensen eta ., 2000).

A recent theoretical study has suggested that persistent activity in the PFG is considered to be an attractor state, in that relatively small amounts of variation in this state lead it back to the same state. This idea has been examined in detail theoretically, especially by Amit, who described persistent activity in terms of dynamical attractors (Amit and Brunei, 1997 Rolls et al., 2008). The spontaneous state and stimulus-selective memory states are assumed to represent multiple attractors, such that a memory state can be switched on or off by transient inputs. This formulation is plausible, insomuch as stimulus-selective persistent firing patterns are dynamically stable in time. These properties of attractors result from interactions in neuronal circuits. Neural synchrony is a general mechanism for dynamically linking together cells coding task-relevant information (Salmas and Sejnowski, 2001). The dynamics of neuronal activities and the representations they reflect are two sides of a coin. 

Dhamala M., Jirsa V.K., Ding M. Enhancement of neural synchrony by time delay. Phys Rev Lett, 2004 92(7), 074-104. 

Spencer KM, Nestor PG, Niznikiewicz MA, Salisbury DF, Shenton ME, et al. 2003. Abnormal neural synchrony in schizophrenia. J Neurosci 23 7407-7411. 

Uhlhaas PJ, Singer W. 2006. Neural synchrony in brain disorders Relevance for cognitive dysfunctions and pathophysiology. Neuron 52 155-168. 

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Globally coupled ensemble as a model of neural synchrony.358... 

The model of globally coupled oscillators is commonly used as a simplest model of neural synchrony. We illustrate this using a computationally efficient neuronal model, proposed by Rulkov [42, 43]. In this model a neuron is described by a 2D map. In spite of its simplicity, this model reproduces most regimes exhibited by the full Hodgkin-Huxley model, but at essentially lower computational costs, thus allowing detailed analysis of the dynamics of large ensembles. The model reads... 

A large number of measures can be derived from recordings of the spontaneous EEG. Time (index) or voltage (power)-based rates of typical frequency bands, as well as ratios between certain frequency bands (e.g., between the a and P band or the 0 and p band) within a selected time interval are most commonly used to quantify EEG patterns. Period analysis quantifies the number of waves that occur in the various frequency bands within a distinct time interval of the EEG record and is supposed to be more sensitive to task-related changes than spectral analysis (Lorig, 1989). Other parameters that describe the covariation of a given signal at different electrodes are coherence and neural synchrony. These measures inform on the functional link between brain areas (Oken et al., 2006). 

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