Synaptic input correlations leading to membrane potential decorrelation of spontaneous activity in cortex

• Published in: The Journal of neuroscience 2013-09, Vol.33 (38), p.15075-15085
• Main Authors: Graupner, Michael, Reyes, Alex D
• Format: Article
• Language: English
• Subjects: Action Potentials - physiology; Analysis of Variance; Animals; Animals, Newborn; Cerebral Cortex - cytology; Computer Simulation; Electric Stimulation; Female; In Vitro Techniques; Male; Mice; Models, Neurological; Nerve Net - physiology; Neurons - physiology; Patch-Clamp Techniques; Statistics as Topic; Synapses - physiology; Synaptic Potentials; Synaptic Transmission - physiology
• ISSN: 0270-6474; 1529-2401; 1529-2401
• DOI: 10.1523/JNEUROSCI.0347-13.2013

Correlations in the spiking activity of neurons have been found in many regions of the cortex under multiple experimental conditions and are postulated to have important consequences for neural population coding. While there is a large body of extracellular data reporting correlations of various strengths, the subthreshold events underlying the origin and magnitude of signal-independent correlations (called noise or spike count correlations) are unknown. Here we investigate, using intracellular recordings, how synaptic input correlations from shared presynaptic neurons translate into membrane potential and spike-output correlations. Using a pharmacologically activated thalamocortical slice preparation, we perform simultaneous recordings from pairs of layer IV neurons in the auditory cortex of mice and measure synaptic potentials/currents, membrane potentials, and spiking outputs. We calculate cross-correlations between excitatory and inhibitory inputs to investigate correlations emerging from the network. We furthermore evaluate membrane potential correlations near resting potential to study how excitation and inhibition combine and affect spike-output correlations. We demonstrate directly that excitation is correlated with inhibition thereby partially canceling each other and resulting in weak membrane potential and spiking correlations between neurons. Our data suggest that cortical networks are set up to partially cancel correlations emerging from the connections between neurons. This active decorrelation is achieved because excitation and inhibition closely track each other. Our results suggest that the numerous shared presynaptic inputs do not automatically lead to increased spiking correlations.