The first complete activity map of a mammalian brain has revealed unprecedented insights into how decisions are made – and may even hint at the roots of that mysterious feeling we call intuition. For decades, neuroscientists have wanted to capture activity across the whole brain at the level of individual neurons – but there is a limit to how many neurons an electrode can record from, how many electrodes can be implanted in a single brain and how many animals a single lab can test. To overcome this, researchers across 12 labs have joined forces, each running the same experiment but recording activity from different areas of the brain, with some overlap to ensure the data they collected was consistent. The combined data from more than 650,000 neurons has now produced the first brain-wide activity map of a complex behaviour. "This work demonstrates a completely new way of tackling complex questions in modern neuroscience," says Benedetto De Martino at University College London, who wasn't involved in the work.

Abstract: Recent strides in the field of neural computation has seen the adoption of Winner-Take-All (WTA) circuits to facilitate the unification of hierarchical Bayesian inference and spiking neural networks as a neurobiologically plausible model of information processing. However, researchers have not yet reached consensus about how best to translate the stochastic responses from these networks into discrete decisions, a process known as population decoding. Despite being an often underexamined part of SNNs, in this work we show that population decoding has a significanct impact on the classification performance of WTA networks. For this purpose, we apply a WTA network to the problem of cancer subtype diagnosis from multi-omic data, using datasets from The Cancer Genome Atlas (TCGA). In doing so we utilise a novel implementation of gene similarity networks, a feature encoding technique based on Kohoen's self-organising map algorithm. We further show that the impact of selecting certain population decoding methods is amplified when facing imbalanced datasets. Multi-omics data integration in cancer diagnosis Alternatively, some research focuses on the timedependent refers to the integration of information from various relationship of spiking neurons, for biological "omics" e.g., genomics, transcriptomics, instance by weighting neuron responses more highly metabolomics, to provide a more comprehensive based on how quickly they fire (Grün & Rotter, 2010; understanding of the molecular landscape of cancer. In order to extract information from SNNs, we neurobiologically inspired method of information examine the spikes generated by a population of processing which aim to solve tasks using plausible neurons in response to a stimulus.

The world of gaming is being rocked by an AI controversy that could upend the multi-billion dollar industry. Researchers have created an artificial intelligence tool that is capable of tracing wines back to their origins, using a chemical analysis that can warn consumers of potential fraudsters. "There's a lot of wine fraud around with people making up some crap in their garage, printing off labels, and selling it for thousands of dollars," University of Geneva Professor Alexandre Pouget, one of the researchers behind the project, said, according to a report in the Guardian. "We show for the first time that we have enough sensitivity with our chemical techniques to tell the difference." Fake wines are a widespread problem, the report noted, with fraudsters typically whipping up a batch as close to a well-known brand as possible and slapping a fake label on the bottle.

Wines really are given a distinct identity by the place where their grapes are grown and the wine is made, according to an analysis of red Bordeaux wines. Alexandre Pouget at the University of Geneva, Switzerland, and his colleagues used machine learning to analyse the chemical composition of 80 red wines from 12 years between 1990 and 2007. All the wines came from seven wine estates in the Bordeaux region of France. "We were interested in finding out whether there is a chemical signature that is specific to each of those chateaux that's independent of vintage," says Pouget, meaning one estate's wines would have a very similar chemical profile, and therefore taste, year after year. To do this, Pouget and his colleagues used a machine to vaporise each wine and separate it into its chemical components.

Fraudsters who pass off ropey plonk as a high-end tipple may soon have artificial intelligence on their case; scientists have trained an algorithm to trace wines to their origins based on routine chemical analyses. Researchers used machine learning to distinguish wines based on subtle differences in the concentrations of scores of compounds, allowing them to track the wines back not only to a particular vine-growing region, but to the estate where the wine was made. "There's a lot of wine fraud around with people making up some crap in their garage, printing off labels, and selling it for thousands of dollars," said Prof Alexandre Pouget at the University of Geneva in Switzerland. "We show for the first time that we have enough sensitivity with our chemical techniques to tell the difference." To train the program, the scientists turned to gas chromatography, which had been used to analyse 80 wines harvested over 12 years from seven different estates in the Bordeaux region of France.