Think like a brain!
Neuromorphic means brain-inspired; this is a growing field that aims to learn lessons from the efficient processing and computation of biological brains. This group aims to facilitate collaboration between those who study brains, algorithms, and engineering by providing a semi-formal environment to share new research articles and ideas.
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Abstract: Despite their seemingly impressive performance at image recognition and other perceptual tasks, deep convolutional neural networks are prone to be easily…
In Search of Invariance in Brains and Machines
From Luis El Srouji
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Abstract: Flexible function is essential for the brain to cope with varying environments, changing quality of sensory information as well as context dependency. This…
Functional self-reconfiguration processes in…
From Luis El Srouji
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Abstract: As Moore’s Law comes to a close, new innovative approaches to microelectronics research are important to achieve much needed capabilities improvements…
Dr. Aimone: A Probabilistic Future for…
From Luis El Srouji
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Prof. Cawenberghs presents neuromorphic cognitive computing systems-on-chip implemented in custom silicon compute-in-memory neural and memristive synaptic crossbar array…
Prof. Cauwenberghs: Reverse Engineering the…
From Luis El Srouji
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Discussion of analog CMOS computation and parallels to biological neurons. Prof. Hasler points out that the exponential voltage controls found in MOS devices can be used…
Prof. Hasler: Physical Neuromorphic Computing
From Luis El Srouji
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Luis El Srouji leads a discussion on Vector Symbolic Architectures (VSA). VSAs are a mathematical framework in which basis vectors encode some attribute (color, weight,…
Vector Symbolic Architectures
From Luis El Srouji
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Professor Olshausen discusses the neural circuits thought to underly signal compression in the retina, and theories of attractor dynamics downstream in cortex that may…
Professor Olshausen: Neural computations in…
From Luis El Srouji
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Professor Chris Eliasmith gave a guest presentation on some mechanisms of encoding information within spiking neural networks. Spatial Semantic Pointers are an extension…
Meeting 6-1-21: Spatial Semantic Pointers and…
From Luis El Srouji
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Algorithmic Frameworks for Neuromorphic Computing: Neural Engineering Framework (NEF) Luis El Srouji gives an overview and explanation of the Neural Engineering…
Meeting 5-18-21: Neural Engineering Framework
From Luis El Srouji
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Meeting 5-4-21: Backpropagation and the Brain 10 of 15
Meeting 5-4-21: Backpropagation and the Brain
Mehmet Berkay leads a discussion on the potential biological basis for backpropagation and a justification for its uses in spiking neural networks.Meeting 5-4-21: Backpropagation and the Brain
From Luis El Srouji
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Dr. Farhad Shokraneh goes over his recent work on an optical processor for neural networks, and describes the features which make this architecture more fault- and…
Meeting 4-20-21: Diamond Mesh MZI-based Optical…
From Luis El Srouji
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Quick video introducing our group's use of Microsoft Teams and how to join.
Group Coordination & Teams tutorial
From Luis El Srouji
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Professor Randy O'Reilly presented on Predictive Error-Driven Learning in the Brain; this method approximates backpropagation in rate-approximated neural networks…
Meeting 4-1-21: Predictive Error-Driven Learning
From Luis El Srouji
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A review of basic neurobiology followed by a discussion on the capabilities of IBM's TrueNorth and Intel's Loihi neuromorphic chips.
Meeting-3-11-21: From biology to silicon
From Luis El Srouji
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First club meeting; a brief overview of neuromorphic computing in comparison to both biological brains and Von Neumann computers. A short discussion of ABR's Nengo…
First Meeting: Intro to Neuromorphic computing
From Luis El Srouji
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