AI News, I485/H400: Biologically Inspired Computing

I485/H400: Biologically Inspired Computing

Description:Biological organisms cope with the demands of their environments using solutions quite unlike the traditional human-engineered approaches to problem solving.

It aims to provide an understanding of the distributed architectures of natural complex systems, and how those can be used to produce informatics tools with enhanced robustness, scalability, flexibility and which can interface more effectively with humans.

I485/H400: Biologically Inspired Computing

Description:Biological organisms cope with the demands of their environments using solutions quite unlike the traditional human-engineered approaches to problem solving.

It aims to provide an understanding of the distributed architectures of natural complex systems, and how those can be used to produce informatics tools with enhanced robustness, scalability, flexibility and which can interface more effectively with humans.

Bio-inspired computing

Bio-inspired computing, short for biologically inspired computing, is a field of study that loosely knits together subfields related to the topics of connectionism, social behaviour and emergence.

Some areas of study encompassed under the canon of biologically inspired computing, and their biological counterparts: The way in which bio-inspired computing differs from the traditional artificial intelligence (AI) is in how it takes a more evolutionary approach to learning, as opposed to what could be described as 'creationist' methods used in traditional AI.

For this reason, in neural network models, it is necessary to accurately model an in vivo network, by live collection of 'noise' coefficients that can be used to refine statistical inference and extrapolation as system complexity increases.[2] Natural evolution is a good analogy to this method–the rules of evolution (selection, recombination/reproduction, mutation and more recently transposition) are in principle simple rules, yet over millions of years have produced remarkably complex organisms.

It is a highly interdisciplinary conference, spanning classical nonlinear systems such as hydrodynamics, electronics, optical and electronic chaotic and pattern forming systems, complex networks, but also including biological, medical and sometimes even social science topics.

We encourage the participation of the nonlinear and complex systems community wishing to share their recent experimental findings about nonlinear dynamics in: Brain and neurons, optics and lasers, electronic networks, social science and transportation systems, data science, machine learning and cyber security, granular media and active matter, climate and earth, fluids &

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