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 (Roskiesetal., 1999).Nevertheless, it is generally considered a wide spread 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 γ 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.

Encoding of sensory events in internal states of the brain requires that this information can be decoded by other neural structures. The encoding of sensory events can involve both the spatial organization of neuronal activityanditstemporaldynamics. Here we investigate the issue of decoding in the context of a recently proposed encoding scheme: the temporal population code. In this code, the geometric properties of visual stimuli become encoded into the temporal response characteristics of the summed activities of a population of cortical neurons. For its decoding, we evaluate a model based on the structure and dynamics of cortical microcircuits that is proposed for computations on continuous temporal streams: the liquid state machine. Employing the original proposal of the decoding network results in a moderate performance. Our analysis shows that the temporal mixing of subsequent stimuli results in a joint representation that compromises their classification. To overcome this problem, we investigate a number of initialization strategies. Whereas we observe that a deterministically initialized network results in the best performance, we find that in case the network is never reset, that is, it continuously processes the sequence of stimuli, the classification performance is greatly hampered by the mixing of information from past and present stimuli. We conclude that this problem of the mixing of temporally segregated information is not specific to this particular decoding model but relates to a general problem that any circuit that processes continuous streams of temporal information needs to solve. Furthermore, as both the encoding and decoding components of our network have been independently proposed as models of the cerebral cortex, our results suggest that the brain could solve the problem of temporal mixing by applying reset signals at stimulus onset, leading to a temporal segmentation of a continuous input stream.