Neuronal Modeling/Theoretical Neuroscience

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Section Editors
Prof. Joseph V Martin
Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA

Prof. Ernest Greene
Department of Psychology, University of Southern California, Los Angeles, CA 90089, USA

Prof. Sung Chan Jun
School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), 123 Cheomdangwagi-ro, Buk-gu, Gwangju, 61005, South Korea

Dr. Vito Di Maio
Institute of Applied Science and Intelligent Systems (ISASI) of CNR, Via Campi Flegrei 34, 80078 Pozzuoli (NA), Italy

Dr. Artur Luczak
Canadian Centre for Behavioural Neuroscience, Department of Neuroscience, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada

Manuscript Topics
Information processing in the brain is the most important brain function and it is given by the simultaneous activity of billions of neurons each connected by synaptic contacts with thousands of other neurons. The activity of synapses on a single neuron contribute to the neural code formation (spike sequences) which contribute to the activity of neural networks. Since from the most elementary structures which transfer information in the brain, the synapses, the complexity of the mechanisms supporting their functionality represents a limitation to the complete understanding by using only experimental approaches. The transfer, integration and elaboration of the informationamong these structures require the synergy of experimental, modeling, computational and theoretical approaches, with different levels of complexity, to be fully understood.

The aim of this section is then to publish original papers, reports, reviews and collections of articles reporting results of biologically inspired theoretical and computational models ranging from synaptic transmission to neural network activity. Papers which compare experimental and theoretical results will be particularly welcome. Articles welcome will be (but not restricted to) in the following fields:

• Synaptic modeling
• Synaptic integration and neuronal modeling
• Dendritic synaptic computing
• Single neuron modeling
• Neuronal integration
• Neuronal coding
• Neuronal networks
• Biologically inspired Neuronal modeling in Artificial Intelligence
• Biologically inspired Neuronal networks in Artificial Intelligence
• Theoretical neurosciences

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Zeynep Kaya, Mohammadreza Soltanipour, Alessandro Treves
AIMS Neuroscience, 2020, 7(3): 275-298. doi: 10.3934/Neuroscience.2020015
+ Abstract     + HTML     + PDF(2351 KB)
Evangelos Almpanis, Constantinos Siettos
+ Abstract     + HTML     + PDF(1208 KB)
Tiziana M. Florio
AIMS Neuroscience, 2020, 7(2): 136-152. doi: 10.3934/Neuroscience.2020010
+ Abstract     + HTML     + PDF(339 KB)
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