The universal fuzzy logical framework of neural circuits and its application in modeling primary visual cortex.

Analytical study of large-scale nonlinear neural circuits is a difficult task. Here we analyze the function of neural systems by probing the fuzzy logical framework of the neural cells' dynamical equations. Although there is a close relation between the theories of fuzzy logical systems and neural systems and many papers investigate this subject, most investigations focus on finding new functions of neural systems by hybridizing fuzzy logical and neural system. In this paper, the fuzzy logical framework of neural cells is used to understand the nonlinear dynamic attributes of a common neural system by abstracting the fuzzy logical framework of a neural cell. Our analysis enables the educated design of network models for classes of computation. As an example, a recurrent network model of the primary visual cortex has been built and tested using this approach.

Synchronized oscillation in a modular neural network composed of columns.

The columnar organization is a ubiquitous feature in the cerebral cortex. In this study, a neural network model simulating the cortical columns has been constructed. When fed with random pulse input with constant rate, a column generates synchronized oscillations, with a frequency varying from 3 to 43 Hz depending on parameter values. The behavior of the model under periodic stimulation was studied and the input-output relationship was non-linear. When identical columns were sparsely interconnected, the column oscillator could be locked in synchrony. In a network composed of heterogeneous columns, the columns were organized by intrinsic properties and formed partially synchronized assemblies.

Modeling the acceleration sensitive neurons in the pigeon optokinetic system.

Recent physiological findings revealed that about one-third of motion-sensitive neurons in the pigeon's pretectal nucleus encoded the acceleration of visual motion. Here we propose a microcircuit hypothesis, in which the slow adaptive depressions play a significant role in response generating, to account for the origin of the three important properties of the acceleration-sensitive neurons: the plateau-shaped speed-tuning curves, the opposite-signed after-responses (OSARs) and the acceleration sensitivities. The flat plateau within the speed-tuning curves and the OSARs to motion offset observed in experiments are reproduced successfully in simulations, and the simulative responses of the acceleration-sensitive neurons to step changes, ramp changes in stimulus speed and sine wave modulations of stimulus speed are qualitatively consistent with physiological observations. Thus, a biologically plausible substrate for the neurons' classification and the origin of the three properties are provided.