Brent Doiron

TL;DR: This work computing the spike train correlation coefficient of unconnected pairs of in vitro cortical neurons receiving correlated inputs shows that this relationship between output correlation and firing rate is robust to input heterogeneities.

TL;DR: A simplified model shows how stimuli bias networks toward particular activity states, thereby reducing firing rate variability as observed experimentally in many cortical areas, and relates cortical architecture to the reported variability in spontaneous and evoked spiking activity.

TL;DR: It is demonstrated, using a large-scale cortical network model, that realistic synaptic plasticity rules coupled with homeostatic mechanisms lead to the formation of neuronal assemblies that reflect previously experienced stimuli.

TL;DR: This work examines three separate mechanisms that modulate spike train correlations: changes in input correlations, internal fluctuations and the transfer function of single neurons in feedforward pathways and shows how the same approach can explain the modulation of correlations in recurrent networks.

TL;DR: This work combines computational models and in vivo recordings to study the relationship between the spatial structure of connectivity and correlated variability in neural circuits and shows that incorporating distance-dependent connectivity improves the extent to which balanced network theory can explain correlated neural variability.