Event Abstract Back to Event Cortical activity demystified: a unifying theory that explains state switching in cortex Alexander Lerchner1* and Peter E. Latham1 1 UCL, Gatsby Computational Neuroscience Unit, United Kingdom An increasing number of experiments show that neocortical activity not only exhibits different states, but can rapidly switch between them. Examples include up and down states [1], synchronized and de-synchronized activity [2], high and low spiking precision [3], and large and small subthreshold correlations [4]. Although there is agreement that these states have important implications for computations, it has not been clear what mechanisms underlie them. Here we provide a unifying explanation by showing that different inputs applied to the same cortical network can generate all the states, and transitions between the states, that have been observed experimentally. For the model underlying our theory, we need to assume only a small number of well-established properties that are shared by all local networks in the cortex. To analyze the model, we use an extended mean-field theory with temporal fluctuations. As with most mean-field theories of this type, we combine subthreshold activity with spiking activity to derive a self-consistent set of equations. Importantly, the equations contain only a small number of parameters, and even these have values that are constrained by biology, so the network can exhibit only a restricted range of behaviors. The main outcome of the theory is that the activity of the network depends primarily on the external input, and far less so on single-neuron properties or connectivity. For constant input, our theory (and simulations) recovers previous results indicating that networks exhibit weak correlations and irregular spiking activity. Inputs with intermediate structures lead to states in which sub-threshold correlations increase during sudden stimulus increases, accompanied by more precise spike timing during such events. And finally, brief inputs that drive the network only occasionally result in a dynamic state exhibiting membrane-potential "bumps" that are strongly correlated across neurons. These bumps lead to almost perfect synchrony for both excitation and inhibition, but with a characteristic lag of inhibition behind excitation of a few milliseconds. In this regime, the theory predicts that membrane potentials are highly correlated, and the first spikes after stimulus onsets are precisely timed. Our results show that both single-neuron and population activity in cortex are constrained by fundamental dynamics of local cortical networks, which in turn depend on the structure of the network input in a non-trivial manner. The theory predicts that cortical networks can have only a small number of behaviors. It is nontrivial, then, that the behaviors predicted by our theory (and verified with detailed network simulations) are consistent with experimental observations. Beyond providing a mechanistic explanation for a wealth of existing data, our theory can guide design and data analysis for future experiments that aim to probe detailed function and micro-structure of cortical networks.