Balanced neural architecture and the idling brain - Doiron, Brent; Litwin-Kumar, Ashok (Frontiers in Computational Neuroscience, 2014)
A signature feature of cortical spike trains is their trial-to-trial variability. This variability is large in spontaneous conditions and is reduced when cortex is driven by a stimulus or task. Models of recurrent cortical networks with unstructured, yet balanced, excitation and inhibition generate variability consistent with evoked conditions. However, these models lack the long timescale fluctuations and large variability present in spontaneous conditions. We propose that global network architectures which support a large number of stable states (attractor networks) allow balanced networks to capture key features of neural variability in both spontaneous and evoked conditions. We illustrate this using balanced spiking networks with clustered assembly, feedforward chain, and ring structures. By assuming that global network structure is related to stimulus preference, we show that signal correlations are related to the magnitude of correlations in the spontaneous state. In our models, the dynamics of spontaneous activity encompasses much of the possible evoked states, consistent with many experimental reports. Finally, we contrast the impact of stimulation on the trial-to-trial variability in attractor networks with that of strongly coupled spiking networks with chaotic firing rate instabilities, recently investigated by Ostojic (2014). We find that only attractor networks replicate an experimentally observed stimulus-induced quenching of trial-to-trial variability. In total, the comparison of the trial-variable dynamics of single neurons or neuron pairs during spontaneous and evoked activity can be a window into the global structure of balanced cortical networks.