Activated cortical states: Experiments, analyses and models
Introduction
Several experimental preparations show the propensity of cerebral cortex to generate spontaneous activity without any specific stimulus. For example, a high level of ongoing activity was reported in visual cortex in vivo, and remarkably, it was of the same order of magnitude as visually-evoked responses (Arieli et al., 1996). Isolating the cortical tissue in vivo by creating cortical “slabs” first lead to a silent network, but activity recurs after a few days (Burns and Webb, 1979, Timofeev et al., 2000). In vitro cortical networks can also display self-sustained activity, as found in cortical slices (Sanchez-Vives and McCormick, 2000, Cossart et al., 2003) or in organotypic cultures of cortical neurons (Plenz and Aertsen, 1996).
Models of cortical networks have attempted to generate activity comparable to experiments, and several types of models were proposed, ranging from integrate and fire networks (Amit and Brunel, 1997, Brunel, 2000) up to conductance-based network models (Compte et al., 2000, Timofeev et al., 2000, Vogels and Abbott, 2005, Kumar et al., in press). In particular, a recent study (Vogels and Abbott, 2005) provided relatively small networks (≃10,000 neurons) displaying self-sustained activity, which were used to investigate the effect of “internal dynamics” on signal propagation.
In the present paper, we evaluate our understanding of cortical activated states from a computational neuroscience point of view. We start by reviewing the electrophysiological characteristics of activated cortical states based on recordings and analyses performed in awake cat association cortex. We next turn to models and evaluate to which extent these models can reproduce all experimental measurements.
Section snippets
Experimental characterization of activated cortical states
The term “activated” state refers to cortical activity in states of vigilance corresponding to the awake animal. The electroencephalogram (EEG) during such activated states is typically “desynchronized”, i.e., of low amplitude and irregular activity dominated by fast frequencies (15–60 Hz; see Fig. 1, Awake, EEG). Intracellularly, neurons are depolarized and fire tonic and irregular discharges (Fig. 1, Awake, Intra). This level of irregularity is also apparent in multiunit activity (Fig. 1,
Single-cell model of activated states
The first type of model was directly linked to experimental data and was aimed at evaluating the plausibility of conductance measurements. The conductance measurements during wakefulness (Fig. 2D) were integrated into a single-compartment model with Hodgkin–Huxley kinetics (Rudolph et al., 2007). The type of model considered was the point-conductance model (Destexhe et al., 2001), which consists in a single-compartment neuron subject to fluctuating conductances:
Network models of activated states
We now turn to network models that autonomously generate active states. We consider here a model derived from the Vogels and Abbott (2005) study of self-sustained irregular activity states in networks of spiking neurons with conductance-based synapses. The model consisted of 4000 neurons, which were separated into two populations of excitatory and inhibitory neurons, forming 80% and 20% of the neurons, respectively. All neurons were connected randomly using a connection probability of 2%.
The
Comparison of models to experimental data
We analyzed the model similarly to experimental data (see Section 2). First, we computed the spontaneous firing rates which were around 15 Hz on average (Fig. 6A). This value is similar to the experimental data. However, there was no significant difference between excitatory and inhibitory cells in this model because both cell types had similar parameters and connectivity. Second, we analyzed the statistics of interspike intervals (ISI), which are exponentially distributed (Fig. 6B), exactly as
Discussion
In this paper, we have provided a summary of analyses of activated states in cerebral cortex, both at the single-cell and network level (Section 2). We gave an overview of single-cell models of such states (Section 3) and a network model of self-sustained active cortical states (Section 4). In Section 5, we provided comparisons between these models and experimental data.
This analysis showed that many features of activated cortical states are well reproduced by models. These include the
Acknowledgements
Research supported by CNRS, ANR, ACI, HFSP and the European Community (FACETS grant FP6 15879).
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