scholarly journals Erratum: Phase synchronization and intermittent behavior in healthy and Alzheimer-affected human-brain-based neural network [Phys. Rev. E 99 , 022402 (2019)]

2019 ◽  
Vol 99 (6) ◽  
Author(s):  
R. C. Budzinski ◽  
B. R. R. Boaretto ◽  
T. L. Prado ◽  
S. R. Lopes
2021 ◽  
pp. 102-112
Author(s):  
John Matthias

This chapter outlines a theory of co-evolution of contexts and histories in human culture by making an analogy with the microscopic functionality of the human brain, and in particular Eugene Izhikevich’s idea of polychronization by mapping the network of ‘firing’ events in a biological neural network onto a network of ‘human events’ in the physical network of humans. The article utilizes the new theory to focus on the evolution of sound art by pointing to the multiplicity of origin contexts, and it examines a particular example of sound art installation, The Fragmented Orchestra (Jane Grant, John Matthias, and Nick Ryan) to exemplify the theory of the inter-human cortex.


2020 ◽  
Vol 34 (07) ◽  
pp. 2050050 ◽  
Author(s):  
Fuzhong Nian ◽  
Xinmeng Liu ◽  
Yaqiong Zhang ◽  
Xuelong Yu

Combined with RBF neural network and sliding mode control, the synchronization between drive system and response system was achieved in module space and phase space, respectively (module-phase synchronization). The RBF neural network is used to estimate the unknown nonlinear function in the system. The module-phase synchronization of two fractional-order complex chaotic systems is implemented by the Lyapunov stability theory of fractional-order systems. Numerical simulations are provided to show the effectiveness of the analytical results.


2019 ◽  
Author(s):  
Ranmal A. Samarasinghe ◽  
Osvaldo A. Miranda ◽  
Simon Mitchell ◽  
Isabella Ferando ◽  
Momoko Watanabe ◽  
...  

ABSTRACTHuman brain organoids represent a powerful tool for the study of human neurological diseases particularly those that impact brain growth and structure. However, many neurological diseases lack obvious anatomical abnormalities, yet significantly impact neural network functions, raising the question of whether organoids possess sufficient neural network architecture and complexity to model these conditions. Here, we explore the network level functions of brain organoids using calcium sensor imaging and extracellular recording approaches that together reveal the existence of complex oscillatory network behaviors reminiscent of intact brain preparations. We further demonstrate strikingly abnormal epileptiform network activity in organoids derived from a Rett Syndrome patient despite only modest anatomical differences from isogenically matched controls, and rescue with an unconventional neuromodulatory drug Pifithrin-α. Together, these findings provide an essential foundation for the utilization of human brain organoids to study intact and disordered human brain network formation and illustrate their utility in therapeutic discovery.


2018 ◽  
Vol 5 (1) ◽  
pp. 23-30
Author(s):  
Yu.I. Koryukalov ◽  
◽  
N.S. Sof'ina ◽  
D.V. Sof'in ◽  
N.A. Lebedeva ◽  
...  

Author(s):  
Shaun C. D'Souza

Cognitive neuroscience is the study of how the human brain functions on tasks like decision making, language, perception and reasoning. Deep learning is a class of machine learning algorithms that use neural networks. They are designed to model the responses of neurons in the human brain. Learning can be supervised or unsupervised. Ngram token models are used extensively in language prediction. Ngrams are probabilistic models that are used in predicting the next word or token. They are a statistical model of word sequences or tokens and are called Language Models or Lms. Ngrams are essential in creating language prediction models. We are exploring a broader sandbox ecosystems enabling for AI. Specifically, around Deep learning applications on unstructured content form on the web.


Author(s):  
Vicky Adriani ◽  
Irfan Sudahri Damanik ◽  
Jaya Tata Hardinata

The author has conducted research at the Simalungun District Prosecutor's Office and found the problem of prison rooms that did not match the number of prisoners which caused a lack of security and a lack of detention facilities and risked inmates to flee. Artificial Neural Network which is one of the artificial representations of the human brain that always tries to simulate the learning process of the human brain. The application uses the Backpropagation algorithm where the data entered is the number of prisoners. Then Artificial Neural Networks are formed by determining the number of units per layer. Once formed, training is carried out from the data that has been grouped. Experiments are carried out with a network architecture consisting of input units, hidden units, and output units. Testing using Matlab software. For now, the number of prisoners continues to increase. Predictions with the best accuracy use the 12-3-1 architecture with an accuracy rate of 75% and the lowest level of accuracy using 12-4-1 architecture with an accuracy rate of 25%.


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