Organization of neuronal population activity in auditory cortex.

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• Kenneth D Harris, Imperial College London
• Tuesday 21 June 2011, 11:30-12:30
• Cambridge University Engineering Department, CBL Rm #438 (http://blg.eng.cam.ac.uk/t/bin/view/Public/Directions).

If you have a question about this talk, please contact Dr. Cristina Savin.

Recordings of single neurons have yielded great insights into how sensory information is represented in the neocortex. However, any one neuron functions as part of a population whose combined activity underlies cortical information processing. This talk will describe some results obtained by recording from populations and individual morphologically identified neurons, in rat auditory cortex. Auditory cortical populations produced structured activity patterns both in response to acoustic stimuli, and spontaneously without sensory input. Population spike time patterns were broadly conserved across multiple sensory stimuli and spontaneous events, exhibiting a generally conserved sequential organization lasting for approximately 100ms. Both spontaneous and evoked events exhibited sparse, spatially localized activity in layer 2/3 pyramidal cells, and densely distributed activity in larger layer 5 pyramidal cells and putative interneurons. Laminar propagation differed however, with spontaneous activity spreading upward from deep layers and slowly across columns, but sensory responses initiating in presumptive thalamorecipient layers, spreading rapidly across columns. In both unanesthetized and urethanized rats, global activity fluctuated between “desynchronized” state characterized by low amplitude, high-frequency local field potentials and a “synchronized” state of larger, lower-frequency waves. Computational studies suggested that responses could be modelled by a simple dynamical system model fitted to the spontaneous activity immediately preceding stimulus presentation, reflecting a nonlinear self-exciting system in synchronized states and an approximately linear system in desynchronized states.

This talk is part of the Computational Neuroscience series.

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