Cerebral Cortex Advance Access published online on February 16, 2009
Cerebral Cortex, doi:10.1093/cercor/bhn240
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The Operating Regime of Local Computations in Primary Visual Cortex
1 School of Computer Science and Electrical Engineering and Bernstein Center for Computational Neuroscience, Technische Universität Berlin, 10587 Berlin, Germany, 2 Department of Computer Science and Electrical Engineering, University of Rostock, 18059 Rostock, Germany, 3 Department of Medicine, Neuroscience, and Motor Control Group (Neurocom), Faculty of Sciences of the Health, University of A Coruña, 15006 A Coruña, Spain, 4 Department of Brain and Cognitive Sciences and Picower Center for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA, 5 Department of Anatomy and Neurobiology, University of California, Irvine, CA 92697, USA
Address correspondence to Klaus Obermayer, School of Computer Science and Electrical Engineering, Technische Universität Berlin, FR2-1, Franklinstraße 28/29, 10587 Berlin, Germany. Email: oby{at}cs.tu-berlin.de.
In V1, local circuitry depends on the position in the orientation map: close to pinwheel centers, recurrent inputs show variable orientation preferences; within iso-orientation domains, inputs are relatively uniformly tuned. Physiological properties such as cell's membrane potentials, spike outputs, and temporal characteristics change systematically with map location. We investigate in a firing rate and a Hodgkin–Huxley network model what constraints these tuning characteristics of V1 neurons impose on the cortical operating regime. Systematically varying the strength of both recurrent excitation and inhibition, we test a wide range of model classes and find the likely models to account for the experimental observations. We show that recent intracellular and extracellular recordings from cat V1 provide the strongest evidence for a regime where excitatory and inhibitory recurrent inputs are balanced and dominate the feed-forward input. Our results are robust against changes in model assumptions such as spatial extent and strength of lateral inhibition. Intriguingly, the most likely recurrent regime is in a region of parameter space where small changes have large effects on the network dynamics, and it is close to a regime of "runaway excitation," where the network shows strong self-sustained activity. This could make the cortical response particularly sensitive to modulation.
Key Words: Bayesian data analysis • computational model • network dynamics • orientation tuning • reverse correlation
Marcel Stimberg and Klaus Wimmer have contributed equally to this work.