Computation in today’s AI systems is typically carried out by silicon-based hardware performing arithmetic operations on simplified artificial neurons, often at gigahertz clock rates.
This differs markedly from the brain, where neurons communicate using brief electrical spikes on millisecond timescales and where each neuron has rich internal dynamics.
Could AI learn from the brain?
The guest’s group develops brain-inspired computing systems using neuromorphic hardware, both to emulate neural dynamics efficiently and to investigate how networks of spiking neurons perform computations.
Links:
- Wunderlich et al: “Demonstrating Advantages of Neuromorphic Computation: A Pilot Study”, Frontiers in Neuroscience (2019)
- Billaudelle et al: “Versatile Emulation of Spiking Neural Networks on an Accelerated Neuromorphic Substrate”, IEEE XPlore (2020)
- Göltz et al: “Fast and energy-efficient neuromorphic deep learning with first-spike times”, Nature Machine Intelligence (2021)
- Zenke et al: “Visualizing a joint future of neuroscience and neuromorphic engineering”, Neuron (2021)
- Ellenberger et al: “Backpropagation through space, time and the brain”, Nature Communications (2025)
- Boikov et al: “Ultrafast neural sampling with spiking nanolasers”, Nature Communications (2025)
- Homepage of Mihai Petrovici
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