CMSC 191: Introduction to Neural Computing
Reflections and Future Directions
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Exploring Beyond Deep Learning
Exploring Beyond Deep Learning
In this final topic, we’ll take a step into the future of neural computation, looking beyond today’s deep learning to explore the next exciting frontiers. We’ll begin with Spiking Neural Networks (SNNs) and neuromorphic systems, which push the boundaries of what’s possible by blurring the line between biology and hardware. These systems mimic the way neurons fire in the brain, bringing us closer to creating machines that think and learn in ways even more similar to us.
We’ll also dive into the merging of AI with fields like physics and neuroscience, exploring how these powerful intersections are opening up new possibilities for the future of computing. This convergence is pushing us toward even more adaptive, efficient, and biologically-inspired systems.
As we explore these cutting-edge technologies, we’ll pause to reflect on the role of curiosity as the true driver of progress. Innovation often starts with asking bold questions and daring to imagine possibilities beyond the models we know today.
Finally, we’ll turn inward—to you, the learner. The future of neural computing is not just about mastering existing knowledge, but about questioning assumptions and challenging the status quo. It’s about blending rigor with wonder, analysis with imagination. The path forward belongs to those who dare to innovate by thinking outside the box and imagining what’s next.
By the end of this topic, you’ll be inspired not only to understand the latest advancements in neural computation, but to contribute to them—to question, explore, and dream beyond the current limits of AI. The future is open, and it's waiting for the next generation of thinkers and innovators to shape it.
Learning Outcomes
Learning Outcomes
Describe the principles of Spiking Neural Networks (SNNs) and neuromorphic computing.
Explain how interdisciplinary approaches, such as optical or analog computing, contribute to next-generation AI systems.
Discuss how neuroscience and AI inform and accelerate each other.
Recognize curiosity and intrinsic motivation as key drivers of innovation in both AI agents and human researchers.
Reflect on their own potential contributions to the continuing evolution of neural computing.
Guide Questions
Guide Questions
How do Spiking Neural Networks differ fundamentally from traditional artificial neurons?
Why is neuromorphic computing considered a critical direction for future AI hardware?
What can physics and biology teach us about building more efficient or intelligent systems?
How does curiosity—both in humans and machines—drive genuine discovery?
What questions should we continue to ask to shape the future of neural computing?
Topic Outline
Topic Outline
Reflections and Future Directions* (topic handout)
Beyond the Horizon
Emerging Trends in Neural Computation
Beyond the Black Box: Spiking and Neuromorphic Systems
Where Worlds Collide: Interdisciplinary Frontiers
The Role of Curiosity and Research in AI
The Engine of Discovery: Curiosity in AI
Your Place in the Future: Questioning and Contributing
The Next Frontier is You
The semester at a glance:
Reflections and Future . . .
Neural computing evolves because we remain curious enough to ask what the next neuron should dream of.
Neural computing evolves because we remain curious enough to ask what the next neuron should dream of.
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