Memory and learning in the brain are realized by a collection of mechanisms that interact, resulting in learning and subsequent recognition of input patterns. While these mechanisms are complex and span different areas of the brain, we hypothesize that we can construct electronic circuits that mimic these mechanisms, demonstrating some aspects of pattern recognition that mimic the brains ability to learn and recognize patterns
David Marr famously proposed three levels of analysis (implementational, algorithmic, and computatio...
Neuromorphic engineering attempts to understand the computational properties of neural processing sy...
An artificial worlds model of the brain has been developed that integrates memory, intraneuronal dyn...
This book presents neuromorphic cognitive systems from a learning and memory-centered perspective. I...
We demonstrate learning in a neuromorphic recurrent attractor network distributed onto two VLSI chip...
SummaryThe ability to associate some stimuli while differentiating between others is an essential ch...
The ability to associate some stimuli while differentiating between others is an essential character...
Human memory and learning represent the most complex and miraculous of human abilities and also the ...
Human memory and learning represent the most complex and miraculous of human abilities and also the ...
This paper sketches several aspects of a hypothetical cortical architecture for visual object recogn...
Neuromorphic plasticity is the basic platform for learning in biological systems and is considered a...
Synapses and neural connectivity are plastic and shaped by experience. But to what extent does conne...
Indiveri G, Chicca E, Douglas RJ. Artificial cognitive systems: From VLSI networks of spiking neuron...
The success of deep networks and recent industry involvement in brain-inspired computing is igniting...
Abstract Several unique properties of biological systems, such as adaptation to nat-ural environment...
David Marr famously proposed three levels of analysis (implementational, algorithmic, and computatio...
Neuromorphic engineering attempts to understand the computational properties of neural processing sy...
An artificial worlds model of the brain has been developed that integrates memory, intraneuronal dyn...
This book presents neuromorphic cognitive systems from a learning and memory-centered perspective. I...
We demonstrate learning in a neuromorphic recurrent attractor network distributed onto two VLSI chip...
SummaryThe ability to associate some stimuli while differentiating between others is an essential ch...
The ability to associate some stimuli while differentiating between others is an essential character...
Human memory and learning represent the most complex and miraculous of human abilities and also the ...
Human memory and learning represent the most complex and miraculous of human abilities and also the ...
This paper sketches several aspects of a hypothetical cortical architecture for visual object recogn...
Neuromorphic plasticity is the basic platform for learning in biological systems and is considered a...
Synapses and neural connectivity are plastic and shaped by experience. But to what extent does conne...
Indiveri G, Chicca E, Douglas RJ. Artificial cognitive systems: From VLSI networks of spiking neuron...
The success of deep networks and recent industry involvement in brain-inspired computing is igniting...
Abstract Several unique properties of biological systems, such as adaptation to nat-ural environment...
David Marr famously proposed three levels of analysis (implementational, algorithmic, and computatio...
Neuromorphic engineering attempts to understand the computational properties of neural processing sy...
An artificial worlds model of the brain has been developed that integrates memory, intraneuronal dyn...