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Found 10 papers, 3 papers with code
Bio-inspired computational memory model of the Hippocampus: an approach to a neuromorphic spike-based Content-Addressable Memory
This work presents the first hardware implementation of a fully-functional bio-inspired spiking hippocampal content-addressable memory model, paving the way for the development of future more complex neuromorphic systems.
Bio-inspired spike-based Hippocampus and Posterior Parietal Cortex models for robot navigation and environment pseudo-mapping
The brain has a great capacity for computation and efficient resolution of complex problems, far surpassing modern computers.
A bio-inspired implementation of a sparse-learning spike-based hippocampus memory model
The nervous system, more specifically, the brain, is capable of solving complex problems simply and efficiently, far surpassing modern computers.
Construction of a spike-based memory using neural-like logic gates based on Spiking Neural Networks on SpiNNaker
Neuromorphic engineering concentrates the efforts of a large number of researchers due to its great potential as a field of research, in a search for the exploitation of the advantages of the biological nervous system and the brain as a whole for the design of more efficient and real-time capable applications.
Spike-based computational models of bio-inspired memories in the hippocampal CA3 region on SpiNNaker
The human brain is the most powerful and efficient machine in existence today, surpassing in many ways the capabilities of modern computers.
Spike-based building blocks for performing logic operations using Spiking Neural Networks on SpiNNaker
The functionality of traditional logic gates and the proposed blocks is studied, and the feasibility of the presented approach is discussed.
Event Based Time-Vectors for auditory features extraction: a neuromorphic approach for low power audio recognition
In recent years tremendous efforts have been done to advance the state of the art for Natural Language Processing (NLP) and audio recognition.
Wide & Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems
Prostate cancer (PCa) is one of the most commonly diagnosed cancer and one of the leading causes of death among men, with almost 1. 41 million new cases and around 375, 000 deaths in 2020.
Real-time detection of uncalibrated sensors using Neural Networks
Nowadays, sensors play a major role in several contexts like science, industry and daily life which benefit of their use.
Neuromorphic adaptive spiking CPG towards bio-inspired locomotion of legged robots
The Spiking Central Pattern Generator consists of a network of five populations of Leaky Integrate-and-Fire neurons designed with a specific topology in such a way that the rhythmic patterns can be generated and driven by the aforementioned external stimulus.
