The study of the visual system of the brain has attracted the attention and interest of many neuro-scientists, that derived computational models of some types of neuron that compose it. These findings inspired researchers in image processing and computer vision to deploy such models to solve ...
1 define a new hierarchy that formalizes the requirements of algorithms and their implementation on a range of neuromorphic systems, thereby laying the foundations for a structured approach to research in which algorithms and hardware for brain-inspired computers can be designed separately....
With growing interest in using novel materials for neuromorphic computing, the search for suitable materials and characterizing their properties has gained attention in materials science. Lu et al. [110] have studied the use of machine learning algorithms instead of traditional computational strategies to...
Brain-Inspired Algorithms for Processing of Visual DataNicola Strisciuglio; Nicolai Petkov2021 An Hybrid Attention-Based System for the Prediction of Facial AttributesSouad Khellat-Kihel; Zhenan Sun; Massimo Tistarelli2021 The Statistical Physics of Learning Revisited: Typical Learning Curves in Model...
M. Composing neural algorithms with Fugu. In Proc. Int. Conf. Neuromorphic Systems 1–8 (ACM, 2019). Lagorce, X. & Benosman, R. Stick: spike time interval computational kernel, a framework for general purpose computation using neurons, precise timing, delays, and synchrony. Neural Comput....
The International Semiconductor Association has recognized brain-inspired computing as one of the two most promising disruptive computing technologies in the post-Moore's Law era. As an interdisciplinary field involving chips, software, algorithms, models, and more, the concept and research paradigm of...
Our brain-inspired medical inference framework outperforms commonly used deep learning algorithms, with an AUC score of 0.963 (95\% confidential interval (CI)=0.923–1.000) for thyroid US image diagnosis. Results indicate that our framework improves diagnostic objectivity and interpretability while ...
Memory and learning tasks are achieved using simple algorithms that respond to changes in electronic resistance at junctions where the nanowires overlap. Known as ‘resistive memory switching’, this function is created when electrical inputs encounter changes in conductivity, similar to what happens wit...
It can be seen that ELSM surpasses other algorithms in both performance and stability with low complexity, owing to the advantages of its brain-inspired static and dynamic topology internally. Discussion A long time ago, when the Liquid State Machine (LSM) was first proposed as a tool for ...
Clustering algorithmsParallel architecturesNeuroscienceNanotechnologyProgrammingAs mobile computing becomes increasingly pervasive, so do our expectations of the devices we use and interact with in our everyday lives. We want these devices to be smarter, anticipate our needs, and share our perception of ...