Volume 3 Issue 9, September 2021

The regulatory landscape for artificial intelligence (AI) is shaping up on both sides of the Atlantic, urgently awaited by the scientific and industrial community. Commonalities and differences start to crystallize in the approaches to AI in medicine.

Functional subsystems of the macroscale human brain connectome are mapped onto a recurrent neural network and found to perform optimally in a critical regime at the edge of chaos.

Neuromorphic chips that use spikes to encode information could provide fast and energy-efficient computing for ubiquitous embedded systems. A bio-plausible spike-timing solution for training spiking neural networks that makes the most of sparsity is implemented on the BrainScaleS-2 hardware platform.

The ethical use of publicly available datasets with human data for which consent has not been explicitly given needs urgent attention from researchers, funders, research institutes and publishers. A specific challenging case is research involving hacked data and this Perspective discusses whether and under what conditions it is morally and ethically justified to conduct such research.

When the training data for machine learning are highly personal or sensitive, collaborative approaches can help a collective of stakeholders to train a model together without having to share any data. But there are still risks to the privacy of the data. This Perspective provides an overview of potential attacks on collaborative machine learning and how these threats could be addressed.

Deep learning-based methods to generate new molecules can require huge amounts of data to train. Skinnider et al. show that models developed for natural language processing work well for generating molecules from small amounts of training data, and identify robust metrics to evaluate the quality of generated molecules.

The relationship between brain organization, connectivity and computation is not well understood. The authors construct neuromorphic artificial neural networks endowed with biological connection patterns derived from diffusion-weighted imaging. The neuromorphic networks are trained to perform a memory task, revealing an interaction between network structure and dynamics.

Radiomics has been used to discover imaging signatures that predict therapy response and outcomes, but clinical translation has been slow. Using machine learning methods, the authors report tumour subtypes that are applicable across major imaging modalities and three cancer types. The tumour subtypes have distinct radiological and molecular features, as well as survival outcomes after conventional therapies.

Deep learning has revolutionized image analysis, but applying it to voluminous and multimodal images can be challenging. The authors propose a neural network approach with a modality sampling strategy and an attention module for segmentation in fluorescence microscopy images.

Incorporating prior knowledge in deep learning models can overcome the difficulties of supervised learning, including the need for large amounts of annotated data. An approach in this area called deep reasoning networks is applied to the complex task of mapping crystal structures from X-ray diffraction data for multi-element oxide structures, and identified 13 phases from 307 X-ray diffraction patterns in the previously unsolved Bi-Cu-V oxide system.

Spiking neural networks promise fast and energy-efficient information processing. The ‘time-to-first-spike’ coding scheme, where the time elapsed before a neuron’s first spike is utilized as the main variable, is a particularly efficient approach and Göltz and Kriener et al. demonstrate that error backpropagation, an essential ingredient for learning in neural networks, can be implemented in this scheme.