A simulator for Freeman K-sets in Java

Abstract

Abstract-Freeman K-sets form a hierarchy of models of the dynamics of neuron populations at the mesoscopic (intermediate) level. The topology of connections is modeled by networks of excitatory and inhibitory populations of neurons; the dynamics is approximated by piecewise Iinearization of nonlinear ordinary differential equations (ODE). A simulator for the K-sets of levels Ko to K3 was built in Java in order to remedy some of the deficiencies found in the simulator available built in MATLAB, as defects that cause instabilities in the simulation, the lack of flexibility in the parameterization of the K3-set and the high computational time required to use the simulator due to design and language decisions. Java was chosen because it is a popular, multi-platform, object oriented and computationally efficient language. The software developed in Java also allows easy integration with MATLAB and other environments. An application on clustering of social networking data evaluates the proposed simulator.

Forecasting economic time series using chaotic neural networks

“…They represent a neural population of about 10,000 neurons and are motivated by cortical microcolumns, the structure of columns of neurons in the brain. The K0 sets are governed by a point attractor that remains in equilibrium except when disturbed [Kozma et al 2013][Rosa and Piazentin 2016][Piazentin and Rosa 2015. KI sets are the next level in the hierarchy of models of neuron populations.…”

“…The KI dynamics is a convergence to the non-zero fixed point. If a KI has the necessary connection density, then it is able to maintain a non-zero state of background activity by mutual excitation (or inhibition) [Kozma et al 2013][Rosa and Piazentin 2016][Piazentin and Rosa 2015. KII sets are formed by interactions betweens KI sets with negative feedback.…”

“…The more interesting behavior of KII arises when it is built by KIs with inverse polarities. The connections of the KII have fixed weights, and KIIs form oscillators when these connections are configured [Kozma et al 2013][Rosa and Piazentin 2016][Piazentin and Rosa 2015.…”

Interfaces Cérebro-Computador

“…Conjuntos-K de redes neurais e sua aplicação na classificação de imagética motora: A imagética motora deixa um rastro no sinal da EEG que torna possível a identificação e classificação dos diferentes movimentos motores [52]. Um simulador para os conjuntos-K foi construído em Java e está disponível publicamente [53].…”