title: Local model for contextual modulation in the cerebral cortex.
abstract: A neural response to a sensory stimulus in cerebral cortex is modulated when other stimuli are presented simultaneously. The other stimuli can modulate responses even when they do not drive the neural output alone, indicating a non-linear summation of synaptic activity. The mechanisms of the nonlinearity have remained unclear. Here, I explore a model which considers both network and intracellular processes, and which can account for various types of contextual modulation. The processes include synaptic sensitivity function, determination of inhibition strength, dendritic decay of membrane voltage, and summation of excitatory and inhibitory membrane voltages. First, the model assumes that excitatory and inhibitory units have the same input sensitivity function, which is more broadly tuned than the output tuning function. Second, a central property of the model is that inhibition is a fraction of excitation, determined by covariance between the input and the sensitivity function. With proper fraction, a model neuron sums apparently decorrelated input, regardless of correlations in the original input. Third, the model assumes that synaptic input lands anisotropically on the dendrites, which together with passive dendritic decay cause exponential decay in summation along the input space. This explains the difference between input sensitivity function and output tuning function, and thus accounts for the division between driving classical and modulating extra-classical receptive fields. The model simulations replicate single-cell area summation function, far surround facilitation, and a shift in tuning function due to contextual stimulation. The model is very general, and should be applicable to various interactions between cortical representations.
authors: Brain Research Unit, Low Temperature Laboratory, Aalto University School of Science, Espoo, Finland. vanni@neuro.hut.fi
authors: Simo
authors: S
authors: Vanni
mesh_terms: D002540:Cerebral Cortex / Q000502:physiology; D008959:Models, Neurological; D016571:Neural Networks, Computer