[HTML][HTML] Where is the error? Hierarchical predictive coding through dendritic error computation
Top-down feedback in cortex is critical for guiding sensory processing, which has
prominently been formalized in the theory of hierarchical predictive coding (hPC). However …
prominently been formalized in the theory of hierarchical predictive coding (hPC). However …
Deep learning in spiking neural networks
In recent years, deep learning has revolutionized the field of machine learning, for computer
vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is …
vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is …
Ion-tunable antiambipolarity in mixed ion–electron conducting polymers enables biorealistic organic electrochemical neurons
Biointegrated neuromorphic hardware holds promise for new protocols to record/regulate
signalling in biological systems. Making such artificial neural circuits successful requires …
signalling in biological systems. Making such artificial neural circuits successful requires …
Advancing neuromorphic computing with loihi: A survey of results and outlook
Deep artificial neural networks apply principles of the brain's information processing that led
to breakthroughs in machine learning spanning many problem domains. Neuromorphic …
to breakthroughs in machine learning spanning many problem domains. Neuromorphic …
Loihi: A neuromorphic manycore processor with on-chip learning
Loihi is a 60-mm2 chip fabricated in Intels 14-nm process that advances the state-of-the-art
modeling of spiking neural networks in silicon. It integrates a wide range of novel features for …
modeling of spiking neural networks in silicon. It integrates a wide range of novel features for …
Building machines that learn and think like people
Recent progress in artificial intelligence has renewed interest in building systems that learn
and think like people. Many advances have come from using deep neural networks trained …
and think like people. Many advances have come from using deep neural networks trained …
Supervised learning in spiking neural networks: A review of algorithms and evaluations
X Wang, X Lin, X Dang - Neural Networks, 2020 - Elsevier
As a new brain-inspired computational model of the artificial neural network, a spiking
neural network encodes and processes neural information through precisely timed spike …
neural network encodes and processes neural information through precisely timed spike …
Probabilistic neural computing with stochastic devices
The brain has effectively proven a powerful inspiration for the development of computing
architectures in which processing is tightly integrated with memory, communication is event …
architectures in which processing is tightly integrated with memory, communication is event …
Cognitive computational neuroscience
To learn how cognition is implemented in the brain, we must build computational models
that can perform cognitive tasks, and test such models with brain and behavioral …
that can perform cognitive tasks, and test such models with brain and behavioral …
[HTML][HTML] Toward an integration of deep learning and neuroscience
Neuroscience has focused on the detailed implementation of computation, studying neural
codes, dynamics and circuits. In machine learning, however, artificial neural networks tend …
codes, dynamics and circuits. In machine learning, however, artificial neural networks tend …