Cognitive AI & Spiking Neural Networks
Next-gen bio-inspired architectures — neuromorphic computing for your team.
2–3 days Research engineers, ML engineers, innovation leads
Overview
Transformer-based LLMs dominate today, but the next wave of AI draws from neuroscience. This masterclass introduces your team to bio-inspired computing: spiking neural networks (SNNs), neuromorphic hardware, and cognitive architectures that offer orders-of-magnitude improvements in energy efficiency and real-time processing.
What you’ll learn
- How biological neurons differ from artificial neurons — and why it matters
- Practical SNN implementation and training
- When neuromorphic approaches beat traditional deep learning
- How to evaluate whether cognitive AI fits your roadmap
Curriculum
Day 1 — From Deep Learning to Cognitive AI
- Limitations of current architectures: energy, latency, continual learning
- Biological neural computation: spikes, timing, plasticity
- Spiking Neural Networks: leaky integrate-and-fire, Izhikevich models
- Encoding schemes: rate coding, temporal coding, population coding
- Hands-on: building your first SNN with snnTorch / Norse
Day 2 — Training & Applications
- Surrogate gradient methods for training SNNs
- Converting trained ANNs to SNNs
- Neuromorphic hardware landscape: Intel Loihi, BrainChip Akida, SpiNNaker
- Applications: edge inference, event-driven vision, robotics, anomaly detection
- Energy benchmarks: SNN vs transformer for comparable tasks
- Hands-on: event-driven vision pipeline with DVS camera data
Day 3 — Cognitive Architectures & Your Roadmap
- Cognitive architectures: ACT-R, SOAR, and modern hybrid approaches
- Memory systems: episodic, semantic, procedural — beyond key-value stores
- Combining LLMs with neuromorphic components
- Evaluating neuromorphic fit for your domain and constraints
- Building an internal research roadmap
- Workshop: architecture design session for your use case
Prerequisites
- Strong Python skills
- Familiarity with PyTorch or TensorFlow
- Basic understanding of neural network fundamentals
Outcomes
Your team leaves with working SNN prototypes, a clear understanding of when neuromorphic approaches add value, and a research roadmap tailored to your organisation.
Interested in this masterclass?
Tell me about your team and I'll tailor the programme to your needs.
Book this masterclass