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- M. Stimberg, K. Wimmer,
R. Martin, J. Mariño, J. Schummers, D. C. Lyon, M. Sur, and K. Obermayer.
Operating regimes for cortical computation.
.
In Proceedings of the 7th Meeting of the German Neuroscience Society /
31th Göttingen Neurobiology Conference 2007, 2007.
In the primary visual cortex (V1), information processing in local
neuronal circuits is influenced by the spatial location in the orientation
preference map (ranging from orientation domain to pinwheel regions). A
signature of these influences is the dependence of the orientation tuning of
a cell's conductance input, its membrane potential and its spike output on
the map location. Here we use a firing rate network model and a
physiologically more realistic Hodgkin-Huxley based network model to analyze
how much evidence recent intracellular measurements from cat V1 [1] provide
for the different cortical operating regimes and whether the available data
allows to single out the most likely operating point. Using data of a
neuron's spike output, its membrane potential, its total excitatory, and its
total inhibitory input conductance, we find that the experimental data most
strongly support a regime where the afferent input is well tuned and where
the local recurrent synaptic network provides significant excitatory and
inhibitory inputs when compared to the feedforward drive (see Figure). This
result is highly robust against changes in basic model assumptions, because
neither Mexican-hat type interactions nor a particular spatial range of the
lateral excitatory vs. inhibitory connections have to be invoked to draw this
conclusion. The analysis also shows that the tuning properties of the total
excitatory conductance and the membrane potential are most informative about
the relative strengths of feedforward vs. recurrent inputs. Location
invariant spike tuning, however, can be achieved for a fairly wide range of
model parameters. Furthermore, our analysis predicts that - due to the strong
recurrency - the most likely operating point is close to a ''line of
instability'' across which the cortical network becomes unstable and the
neural activity increases dramatically. [1] Mariño, J. et al., Nat
Neurosci 8, 194ff (2005).
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