scholarly journals APPLICATION OF UKRAINIAN GRID INFRASTRUCTURE FOR INVESTIGATION OF NONLINEAR DYNAMICS IN LARGE NEURONAL NETWORKS

2015 ◽  
Author(s):  
O. О. Sudakov ◽  
V. L. Maistrenko
Keyword(s):  
Nonlinear Dynamics ◽  
Nervous System ◽  
Brain Tissue ◽  
Computer Simulations ◽  
Neuronal Networks ◽  
Grid Infrastructure ◽  
Large Networks ◽  
Linear Dynamics ◽  
Non Linear

<p>In present work the Ukrainian National Grid (UNG) infrastructure was applied for investigation of synchronization in large networks of interacting neurons. This application is important for solving of modern neuroscience problems related to mechanisms of nervous system activities (memory, cognition etc.) and nervous pathologies (epilepsy, Parkinsonism, etc.). Modern non-linear dynamics theories and applications provides powerful basis for computer simulations<br />of biological neuronal networks and investigation of phenomena which mechanisms hardly could be clarified by other approaches. Cubic millimeter of brain tissue contains about 105 neurons, so realistic (Hodgkin-Huxley model) and phenomenological (Kuramoto-Sakaguchi, FitzHugh-Nagumo, etc. models) simulations require consideration of large<br />neurons numbers.</p>

Role of the autonomic nervous system in generating non-linear dynamics in short-term heart period variability

2006 ◽  
Vol 51 (4) ◽  
pp. 174-177 ◽  
Author(s):  
Alberto Porta ◽  
Stefano Guzzetti ◽  
Ester Borroni ◽  
Raffaello Furlan ◽  
Nicola Montano ◽  
...  
Keyword(s):  
Nervous System ◽  
Autonomic Nervous System ◽  
Heart Period ◽  
Short Term ◽  
Linear Dynamics ◽  
Autonomic Nervous ◽  
Heart Period Variability ◽  
Period Variability ◽  
Non Linear

Low pressure hydrocephalus and ventriculomegaly: hysteresis, non-linear dynamics, and the benefits of CSF diversion

2002 ◽  
Vol 16 (6) ◽  
pp. 555-561 ◽  
Author(s):  
M. S. Lesniak ◽  
R. E. Clatterbuck ◽  
D. Rigamonti ◽  
M. A. Williams
Keyword(s):  
Low Pressure ◽  
Linear Dynamics ◽  
Non Linear ◽  
Csf Diversion

Non-linear dynamics in DIS at NLO

2017 ◽  
Author(s):  
Giovanni Antonio Chirilli
Keyword(s):  
Linear Dynamics ◽  
Non Linear

New concepts on the structure of the neuronal networks: The miniaturization and hierarchical organization of the central nervous system*

1984 ◽  
Vol 4 (2) ◽  
pp. 93-98 ◽  
Author(s):  
Luigi F. Agnati ◽  
Kjell Fuxe
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Neuronal Networks ◽  
Hierarchical Organization ◽  
Memory Storage ◽  
Information Handling ◽  
New Concepts ◽  
The Central Nervous System ◽  
Local Circuits ◽  
The Relationship

The hypothesis is introduced that miniaturization of neuronal circuits in the central nervous system and the hierarchical organization of the various levels, where information handling can take place, may be the key to understand the enormous capability of the human brain to store engrams as well as its astonishing capacity to reconstruct and organize engrams and thus to perform highly sophisticated integrations. The concept is also proposed that in order to understand the relationship between the structural and functional plasticity of the central nervous system it is necessary to postulate the existence of memory storage at the network level, at the local circuit level, at the synaptic level, at the membrane level, and finally at the molecular level. Thus, memory organization is similar to the hierarchical organization of the various levels, where information handling takes place in the nervous system. In addition, each higher level plays a role in the reconstruction and organization of the engrams stored at lower levels. Thus, the trace of the functionally stored memory (i.e. its reconstruction and organization at various levels of storage) will depend not only on the chemicophysical changes in the membranes of the local circuits but also on the organization of the local circuits themselves and their associated neuronal networks.

A novel LSSVM-L Hammerstein model structure for system identification and nonlinear model predictive control of CSTR servo and regulatory control

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Akshaykumar Naregalkar ◽  
Subbulekshmi Durairaj
Keyword(s):  
Linear System ◽  
Regulatory Control ◽  
Hammerstein Model ◽  
Support Vector ◽  
Model Structure ◽  
Linear Dynamics ◽  
Vector Machines ◽  
Non Linear ◽  
Laguerre Filters ◽  
Non Linear System

Abstract A continuous stirred tank reactor (CSTR) servo and the regulatory control problem are challenging because of their highly non-linear nature, frequent changes in operating points, and frequent disturbances. System identification is one of the important steps in the CSTR model-based control design. In earlier work, a non-linear system model comprises a linear subsystem followed by static nonlinearities and represented with Laguerre filters followed by the LSSVM (least squares support vector machines). This model structure solves linear dynamics first and then associated nonlinearities. Unlike earlier works, the proposed LSSVM-L (least squares support vector machines and Laguerre filters) Hammerstein model structure solves the nonlinearities associated with the non-linear system first and then linear dynamics. Thus, the proposed Hammerstein’s model structure deals with the nonlinearities before affecting the entire system, decreasing the model complexity and providing a simple model structure. This new Hammerstein model is stable, precise, and simple to implement and provides the CSTR model with a good model fit%. Simulation studies illustrate the benefit and effectiveness of the proposed LSSVM-L Hammerstein model and its efficacy as a non-linear model predictive controller for the servo and regulatory control problem.

Sign in / Sign up

Export Citation Format

Share Document