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SUMMARY:AI Workshop: Spiking Neural Networks
DESCRIPTION:Welcome to our immersive AI technology workshop series. During these sessions you will be introduced to new and established AI tools that will help you create and manipulate content in new and powerful ways. Each session is led by an industry expert who will guide you through the material and share its real-world implications. \nLearning Outcomes \nAt the conclusion of the workshop\, you should be able to \n\nDescribe and discuss the fundamental principles of Spiking Neural Networks (SNNs)\, including spike-based data representation\, key neuron models (e.g.\, Leaky Integrate-and-Fire)\, synaptic operations\, the current state-of-the-art in neuromorphic hardware\, and the realistic short-term and long-term potential of SNNs in the broader AI landscape.\nExplain the differences between Spiking Neural Networks (SNNs) and traditional Artificial Neural Networks (ANNs)\, articulating the motivational factors for SNN adoption such as energy efficiency and event-driven computation.\nDemonstrate an ability to properly and effectively implement simple SNN applications (e.g.\, the XOR problem and a more complex use case) using tools like Nengo\, understanding how information is encoded and processed through spike trains\, and applying different training approaches for SNNs while contrasting them with traditional backpropagation and evaluating the challenges and opportunities in SNN learning.\n\nTopics Include \n\nIntroduction to Spiking Neural Networks: What are SNNs? Visualizing spike-based computation.\nWhy SNNs Matter: Motivations (energy efficiency\, neuromorphic hardware)\, real-world applications (robotics\, IoT)\, and a realistic look at when they are most beneficial.\nCore Concepts of SNNs: Spike-based representation (events\, timing\, frequency)\, simplified neuron models (LIF)\, synaptic function\, and encoding strategies (rate vs. temporal).\nHands-On with SNNs (Nengo Demo): Interactive exploration of spike generation\, parameter tuning\, and visualizing network behavior.\nProblem Solving with SNNs:\nThe XOR problem: Understanding challenges with discrete logic.\nApplying SNNs to continuous\, real-world analog-like problems.\nTraining Spiking Neural Networks: Exploring alternatives to backpropagation (e.g.\, evolutionary computation\, PSO) and understanding the associated challenges.\nThe Neuromorphic Landscape: Introduction to key neuromorphic hardware (e.g.\, Intel Loihi\, BrainChip Akida)\, their architectures\, and real-world case studies.\nCurrent Limitations and Future Outlook: Discussing speed vs. efficiency\, challenges in tooling and frameworks\, and the short-term and long-term vision for SNN adoption.\n\nStudents are required to bring laptops for class exercises. \nThis event is co-sponsored by the UCSC Silicon Valley Professional Education Artificial Intelligence program.
URL:https://events.ucsc.edu/event/ai-workshop-spiking-neural-networks/
LOCATION:Silicon Valley Campus\, 3175 Bowers Avenue\, Santa Clara\, CA\, 95054\, United States
CATEGORIES:Lectures & Presentations,Meetings & Conferences,Training
GEO:37.3796975;-121.9765484
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