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The full lethal impact of massive cuts to international food aid
The role and function validation of P2RX4 as a novel cancer biomarker in pan-cancer analysis
Global modules robustly emerge from local interactions and smooth gradients
Childhood maltreatment influences coping in youths with major depression and bipolar depression through resilience and impulsivity
Mathematician who reshaped theory of symmetry wins Abel Prize
Comparing large Language models and human annotators in latent content analysis of sentiment, political leaning, emotional intensity and sarcasm
Pregnancy’s true toll on the body: huge birth study paints most detailed picture yet
The sixth finger illusion induced by palm outside stroking shows stable ownership and independence
Efficient 22 nm GNRFET PTLA using low power trimode technique for high speed processor
Robust and reproducible human intestinal organoid-derived monolayer model for analyzing drug absorption
Evaluation of the efficacy of P11-4 and CCP-ACPF in the prevention and treatment of white spot lesions: a multi-technique approach
Assignment of hybrid laser and microwave inter-satellite links for navigation satellite systems
Evaluation of correctness and reliability of GPT, Bard, and Bing chatbots’ responses in basic life support scenarios
De novo design of transmembrane fluorescence-activating proteins
Neural activation in a septal area is related to intrinsic motivation for non-courtship singing in adult zebra finches
Synaptic and neural behaviours in a standard silicon transistor
Abstract Hardware implementations of artificial neural networks (ANNs)—the most advanced of which are made of millions of electronic neurons interconnected by hundreds of millions of electronic synapses—have achieved higher energy efficiency than classical computers in some small-scale data-intensive computing tasks1. State-of-the-art neuromorphic computers, such as Intel’s Loihi2 or IBM’s NorthPole3, implement ANNs using bio-inspired neuron- and synapse-mimicking circuits made of complementary metal–oxide–semiconductor (CMOS) transistors, at least 18 per neuron and six per synapse. Simplifying the structure and size of these two building blocks would enable the construction of more sophisticated, larger and more energy-efficient ANNs. Here we show that a single CMOS transistor can exhibit neural and synaptic behaviours if biased in a specific (unconventional) manner. By connecting one additional CMOS transistor in series, we build a versatile 2-transistor-cell that exhibits adjustable neuro-synaptic response (which we named neuro-synaptic random access memory cell, or NS-RAM cell). This electronic performance comes with a yield of 100% and an ultra-low device-to-device variability, owing to the maturity of the silicon CMOS platform used—no materials or devices alien to the CMOS process are required. These results represent a short-term solution for the implementation of efficient ANNs and an opportunity in terms of CMOS circuit design and optimization for artificial intelligence applications.
Molecular hydrogen as a potential mediator of the antitumor effect of inulin consumption
Choice history biases in dyadic decision making
Abstract How do we interact with our environment and make decisions about the world around us? Empirical research using psychophysical tasks has demonstrated that our perceptual decisions are influenced by past choices, a phenomenon known as the “choice history bias” effect. This decision-making process suggests that the brain adapts to environmental uncertainties based on history. However, single-subject experiment task design is prevalent across the work on choice history bias, thus limiting the implications of the empirical evidence to individual decisions. Here, we explore the choice history bias effect using a dual-participant approach, where dyads perform a shared perceptual decision-making task. We first propose two competing hypotheses: the participants equally weigh their own and their partner’s decision history, or the participants do not weigh equally their own and their partner’s decision history. We then use a statistical modeling approach to fit generalized linear models to the choice data in a series of steps and arrive at a model that best fits the observed data. Our results indicated that the own and partner’s trial history cannot be treated independently. The findings suggest an interaction of actor and decision at 1-back, leading to a choice alternation bias after a partner’s decision in contrast to a choice repetition bias after an own decision. A similar effect is observed at 2-back, in addition to an additive choice repetition bias of similar size. The effects of actor and decision at 2-back do not depend on the properties of the 1-back trial. Together, these findings support the idea that the participants do not ignore their partner’s decisions but treat these qualitatively differently from their own.