Macroscopic complexity from an autonomous network of networks of theta neurons
نویسندگان
چکیده
We examine the emergence of collective dynamical structures and complexity in a network of interacting populations of neuronal oscillators. Each population consists of a heterogeneous collection of globally-coupled theta neurons, which are a canonical representation of Type-1 neurons. For simplicity, the populations are arranged in a fully autonomous driver-response configuration, and we obtain a full description of the asymptotic macroscopic dynamics of this network. We find that the collective macroscopic behavior of the response population can exhibit equilibrium and limit cycle states, multistability, quasiperiodicity, and chaos, and we obtain detailed bifurcation diagrams that clarify the transitions between these macrostates. Furthermore, we show that despite the complexity that emerges, it is possible to understand the complicated dynamical structure of this system by building on the understanding of the collective behavior of a single population of theta neurons. This work is a first step in the construction of a mathematically-tractable network-of-networks representation of neuronal network dynamics.
منابع مشابه
Derivation of a neural field model from a network of theta neurons.
Neural field models are used to study macroscopic spatiotemporal patterns in the cortex. Their derivation from networks of model neurons normally involves a number of assumptions, which may not be correct. Here we present an exact derivation of a neural field model from an infinite network of theta neurons, the canonical form of a type I neuron. We demonstrate the existence of a "bump" solution...
متن کاملOptimization of Oleuropein Extraction from Olive Leaves using Artificial Neural Network
In this work, the artificial neural networks (ANN) technology was applied to the simulation of oleuropein extraction process. For this technology, a 3-layer network structure is applied, and the operation factors such as amount of flow intensity ratio, temperature, residence time, and pH are used as input variables of the network, whereas the extraction yield is considere...
متن کاملApplication of Artificial Neural Networks and Support Vector Machines for carbonate pores size estimation from 3D seismic data
This paper proposes a method for the prediction of pore size values in hydrocarbon reservoirs using 3D seismic data. To this end, an actual carbonate oil field in the south-western part ofIranwas selected. Taking real geological conditions into account, different models of reservoir were constructed for a range of viable pore size values. Seismic surveying was performed next on these models. F...
متن کاملUse of Artificial Neural Networks and PCA to Predict Results of Infertility Treatment in the ICSI Method
Background: Intracytoplasmic sperm injection (ICSI) or microinjection is one of the most commonly used assisted reproductive technologies (ART) in the treatment of patients with infertility problems. At each stage of this treatment cycle, many dependent and independent variables may affect the results, according to which, estimating the accuracy of fertility rate for physicians will be difficul...
متن کاملAn Ant-Colony Optimization Clustering Model for Cellular Automata Routing in Wireless Sensor Networks
High efficient routing is an important issue for the design of wireless sensor network (WSN) protocols to meet the severe hardware and resource constraints. This paper presents an inclusive evolutionary reinforcement method. The proposed approach is a combination of Cellular Automata (CA) and Ant Colony Optimization (ACO) techniques in order to create collision-free trajectories for every agent...
متن کامل