Academic literature on the topic 'Excitatory-inhibitory Network Model'

Current theories and models of brain rhythm generation are based on (1) the excitability of individual neurons and whole networks, (2) the structural and functional connectivity of neuronal ensembles, (3) the dynamic interaction of excitatory and inhibitory network components, and (4) the importance of transient local and global states. From the interplay of the above, systemic network properties arise which account for activity overdrive or suppression, and critical-level synchronization. Under certain conditions or states, small-to-large scale neuronal networks can be entrained into excessive and/or hypersynchronous electrical brain activity (epileptogenesis). In this chapter we demonstrate with artificial neuronal network simulations how physiological brain oscillations (delta, theta, alpha, beta and gamma range, and transients thereof, including sleep spindles and larger sleep waves) are generated and how epileptiform phenomena can potentially emerge, as observed at a macroscopic scale on scalp and intracranial EEG recordings or manifested with focal and generalized, aware and unaware, motor and nonmotor or absence seizures in man. Fast oscillations, ripples and sharp waves, spike and slow wave discharges, sharp and rhythmical slow waves, paroxysmal depolarization and DC shifts or attenuation and electrodecremental responses seem to underlie key mechanisms of epileptogenesis across different scales of neural organization and bear clinical implications for the pharmacological and surgical treatment of the various types of epilepsy.

Recent research demonstrating that the perceived contrast of a small central grating patch can be strongly influenced by the presence of another grating in an annular surround implied the presence of two types of lateral interaction networks, one excitatory and one inhibitory. The present paper describes the development of a model for these networks, under the conditions where both center and surround contain gratings of the same spatial frequency and orientation. Two different interconnection networks were studied. In the feed-forward system, the gain of each member of a 2-D array of contrast

Character translation can be accomplished by a heteroassociative memory in an artificial neural network. Because of the similarity among the characters, the special features of the patterns are important in pattern recognition. In this paper, a neural-network model based on the interpattem association (IPA) concept is presented.1 Generalized logical rules are developed to construct the excitatory and inhibitory interconnections in the heteroassociative memory. An adaptive optical neural network using high-resolution liquid-crystal televisions2 is used to translate between English letters and C

Due to the nature of Spiking Neural Networks (SNNs), it is challenging to be trained by biologically plausible learning principles. The multi-layered SNNs are with non-differential neurons, temporary-centric synapses, which make them nearly impossible to be directly tuned by back propagation. Here we propose an alternative biological inspired balanced tuning approach to train SNNs. The approach contains three main inspirations from the brain: Firstly, the biological network will usually be trained towards the state where the temporal update of variables are equilibrium (e.g. membrane potential

The incoherent optical neuron (ION) model uses two separate incoherent optical device responses to subtract inhibitory inputs from excitatory inputs for general neural networks. The ION model comprises two elements: an inhibitory element and a nonlinear element. A Hughes liquid crystal light valve (LCLV) can implement both elements. Its nonmonotonic response can be used to approximate a linear, negative slope characteristic for inhibitory inputs as well as a nonlinear positive slope characteristic for the neuron hard-clipping or soft-threshold response. An optical beam is used to provide a sui