AIMC Topic: Biological Evolution

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Hierarchical growth in neural networks structure: Organizing inputs by Order of Hierarchical Complexity.

PloS one
Several studies demonstrate that the structure of the brain increases in hierarchical complexity throughout development. We tested if the structure of artificial neural networks also increases in hierarchical complexity while learning a developing ta...

Technology Trends for Massive MIMO towards 6G.

Sensors (Basel, Switzerland)
At the dawn of the next-generation wireless systems and networks, massive multiple-input multiple-output (MIMO) in combination with leading-edge technologies, methodologies, and architectures are poised to be a cornerstone technology. Capitalizing on...

The morphological paradigm in robotics.

Studies in history and philosophy of science
In the paper, we are going to show how robotics is undergoing a shift in a bionic direction after a period of emphasis on artificial intelligence and increasing computational efficiency, which included isolation and extreme specialization. We assembl...

SVcnn: an accurate deep learning-based method for detecting structural variation based on long-read data.

BMC bioinformatics
BACKGROUND: Structural variations (SVs) refer to variations in an organism's chromosome structure that exceed a length of 50 base pairs. They play a significant role in genetic diseases and evolutionary mechanisms. While long-read sequencing technolo...

A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data.

BMC bioinformatics
BACKGROUND: There is an increasing interest in the use of Deep Learning (DL) based methods as a supporting analytical framework in oncology. However, most direct applications of DL will deliver models with limited transparency and explainability, whi...

Jumping over fences: why field- and laboratory-based biomechanical studies can and should learn from each other.

The Journal of experimental biology
Locomotor biomechanics faces a core trade-off between laboratory-based and field-based studies. Laboratory conditions offer control over confounding factors, repeatability, and reduced technological challenges, but limit the diversity of animals and ...

Complex computation from developmental priors.

Nature communications
Machine learning (ML) models have long overlooked innateness: how strong pressures for survival lead to the encoding of complex behaviors in the nascent wiring of a brain. Here, we derive a neurodevelopmental encoding of artificial neural networks th...

Major evolutionary transitions in individuality between humans and AI.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
That humans might undergo future evolutionary transitions in individuality (ETIs) seems fanciful. However, drawing upon recent thinking concerning the origins of properties that underpin ETIs, I argue that certain ETIs are imminently realizable. Cent...

A stochastic numerical approach for a class of singular singularly perturbed system.

PloS one
In the present study, a neuro-evolutionary scheme is presented for solving a class of singular singularly perturbed boundary value problems (SSP-BVPs) by manipulating the strength of feed-forward artificial neural networks (ANNs), global search parti...

Understanding Human Object Vision: A Picture Is Worth a Thousand Representations.

Annual review of psychology
Objects are the core meaningful elements in our visual environment. Classic theories of object vision focus upon object recognition and are elegant and simple. Some of their proposals still stand, yet the simplicity is gone. Recent evolutions in beha...