13 papers · ranked by Valyu relevance
William Dorrell, Peter E. Latham, Timothy E. J. Behrens, James C. R. Whittington
The efficient coding hypothesis presents a compelling success story for theoretical and systems neuroscience. It marshals a unifying idea, that neural codes can be understood as efficient encodings of natural stimuli, to explain phenomena from across sensory systems, sometimes with exquisite precision. However, similar…
Kazunori D Yamada, M. Samy Baladram, Fangzhou Lin
Attention mechanisms are one of the most frequently used architectures in the development of artificial intelligence because they can process contextual information efficiently. Various artificial intelligence mechanisms such as transformer for processing natural language, image data, etc. include the attention…
Willem A.M. Wybo, Leander Ewert, Charl Linssen, Pooja Babu + 3 more
While the implementation of learning and memory in the brain is governed in large part by subcellular mechanims in the dendrites of neurons, large-scale network simulations featuring such processes remain challenging to achieve. This can be attributed to a lack of appropriate software tools, as neuroscientific…
Hongwei Cai, Zheng Ao, Chunhui Tian, Zhuhao Wu + 5 more
Brain-inspired hardware emulates the structure and working principles of a biological brain and may address the hardware bottleneck for fast-growing artificial intelligence (AI). Current brain-inspired silicon chips are promising but still limit their power to fully mimic brain function for AI computing. Here, we…
Eugene Christo V R, Christoph Robert Meinecke, Bert Nitzsche, Roman Lyttleton + 5 more
Network-based biocomputing (NBC) presents an energy-efficient, parallel computing approach for solving nondeterministic polynomial time (NP) complete problems by leveraging motor-driven cytoskeletal filaments that explore all possible solutions through nanofabricated networks in a massively parallel fashion. However…
Jan Kubanek
Understanding how humans and animals can make effective decisions given natural constraints would have a profound impact on economics, psychology, ecology, and related fields. Neoclassical economics provides a formalism for optimal decisions, but the apparatus requires a large number of evaluations of the decision…
Anamika Agrawal, Michael A. Buice
The simple linear threshold units used in many artificial neural networks have a limited computational capacity. Famously, a single unit cannot handle non-linearly separable problems like XOR. In contrast, real neurons exhibit complex morphologies as well as active dendritic integration, suggesting that their…
Tim Schröder, Julian Bauer, Patrick Schüler, Jonas Zähringer + 6 more
Silicon-based computing faces limitations in energy consumption, parallelism and the possibility to interact with living systems, prompting interest in biomolecular alternatives such as DNA computing. Here we present Brownian DNA computing that uses coupled molecular balances to form a molecular processing unit (MPU)…
Lewis Grozinger, Jesús Miró-Bueno, Ángel Goñi-Moreño
The programming of computations in living cells can be done by manipulating information flows within genetic networks. Typically, a single bit of information is encoded by a single gene’s steady state expression. Expression is discretized into high and low levels that correspond to 0 and 1 logic values, analogous to…
Thomas D. Schneider
Unlike the Carnot heat engine efficiency published in 1824, an isothermal efficiency derived from thermodynamics and information theory can be applied to biological systems. The original approach by Pierce and Cutler in 1959 to derive the isothermal efficiency equation came from Shannon’s channel capacity of 1949 and…
Bastian Wiederhold, Martin Stemmler, Andreas V.M. Herz
While our senses transmit information at rates exceeding 10^6^ bit/s, high-level cognitive processing is thought to be much slower, on the order of 10 bit/s regardless of the task^1^. It is unclear, though, whether this limit holds when the human mind is challenged. To test how fast one can process abstract…
Veronika Koren, Simone Blanco Malerba, Tilo Schwalger, Stefano Panzeri
The principle of efficient coding posits that sensory cortical networks are designed to encode maximal sensory information with minimal metabolic cost. Despite the major influence of efficient coding in neuroscience, it has remained unclear whether fundamental empirical properties of neural network activity can be…
Michał J. Wójcik, Amy Li, Dante Wasmuht, Jake P. Stroud + 3 more
Working memory has been traditionally studied as a passive storage for information. However, recent advances have suggested that working memory is prospective rather than retrospective, meaning that its content undergoes transformations that will support future behaviour. One perspective that underscores this notion…