Biomedical subjects

Andrea Benucci

Publications and source records attributed to Andrea Benucci.

3 recordsLinked to original sources

PubMed2004

High-order events in cortical networks: a lower bound.

It is commonly believed that information processing in cortical networks involves the collective spiking activity of neuronal assemblies. Nevertheless, due to current technical limitations in multielectrodes recording methods, it is not possible to tackle this issue with direct experimental measurements. In this study we simulate spiking activity of large ensembles of cells focusing on the temporal correlation properties of the neuronal dynamics, and demonstrate that transient, fast synchronization of large groups of cells is a natural phenomenon of the cortical activity. To prove this result we use a statistical approach (based on combinatorics), and knowledge derived from a previous research work [A. Benucci et al., Phys. Rev. E 68, 041905 (2003)]. We quantify the degree of synchronous activity by computing a lower bound for the fraction of cells participating in fast (few milliseconds) synchronous events. Finally we discuss the implications of the results found in terms of cortical coding mechanisms.

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PubMed2004

Two-state membrane potential fluctuations driven by weak pairwise correlations.

Physiological experiments demonstrate the existence of weak pairwise correlations of neuronal activity in mammalian cortex (Singer, 1993). The functional implications of this correlated activity are hotly debated (Roskies et al., 1999). Nevertheless, it is generally considered a widespread feature of cortical dynamics. In recent years, another line of research has attracted great interest: the observation of a bimodal distribution of the membrane potential defining up states and down states at the single cell level (Wilson & Kawaguchi, 1996; Steriade, Contreras, & Amzica, 1994; Contreras & Steriade, 1995; Steriade, 2001). Here we use a theoretical approach to demonstrate that the latter phenomenon is a natural consequence of the former. In particular, we show that weak pairwise correlations of the inputs to a compartmental model of a layer V pyramidal cell can induce bimodality in its membrane potential. We show how this relationship can account for the observed increase of the power in the gamma-frequency band during up states, as well as the increase in the standard deviation and fraction of time spent in the depolarized state (Anderson, Lampl, Reichova, Carandini, & Ferster, 2000). In order to quantify the relationship between the correlation properties of a cortical network and the bistable dynamics of single neurons, we introduce a number of new indices. Subsequently, we demonstrate that a quantitative agreement with the experimental data can be achieved, introducing voltage-dependent mechanisms in our neuronal model such as Ca(2+)- and Ca(2+)-dependent K(+) channels. In addition, we show that the up states and down states of the membrane potential are dependent on the dendritic morphology of cortical neurons. Furthermore, bringing together network and single cell dynamics under a unified view allows the direct transfer of results obtained in one context to the other and suggests a new experimental paradigm: the use of specific intracellular analysis as a powerful tool to reveal the properties of the correlation structure present in the network dynamics.

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PubMed2003

Existence of high-order correlations in cortical activity.

Neurons collect signals originating from a large number of other cells. The variability of this integrated population activity at the millisecond time scale is a critical constraint on the degree of signal integration and processing performed by single neurons. Optical imaging, EEG, and fMRI studies have indicated that cortical activity shows a high degree of variability at a time scale of hundreds of ms. However, currently no experimental methods are available to directly assess the variability in the activity of populations of neurons at a time scale closer to that of the characteristic time constants of neurons, i.e., around 10 ms. Here we integrate pertinent experimental data in one rigorous mathematical framework to demonstrate that (1) the high temporal variability in the spiking activity of individual neurons, (2) the second-order correlation properties of the spiking activity of cortical neurons, and (3) the correlations of the subthreshold dynamics, all impose high amplitude, fast variability in the population activity of cortical neurons. This implies that higher order correlations, a necessary condition for temporal coding models, must be a central feature of cortical dynamics.

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