Neuromorphic hardware

Gaute Einevoll and Mihai Petrovici discuss how neuromorphic hardware can support artificial intelligence and efficient brain simulation. They contrast high-frequency arithmetic on conventional silicon with the rich dynamics and millisecond spikes of biological neurons, and explore brain-inspired systems for emulating neural dynamics and studying computation in spiking networks. Petrovici is a senior researcher at the University of Bern. The conversation was recorded on 18 June 2026 and released on 23 September 2026; the publisher’s audio runs 1 hour, 41 minutes and 46 seconds.

Understanding the computational principles of the brain and replicating them on neuromorphic hardware and modern deep learning architectures is crucial for advancing neuro-inspired AI (NeuroAI). Here, we develop an experimentally-constrained biophysical network model of neocortical circuit motifs, focusing on layers 2-3 of the primary visual cortex (V1). We investigate the role of four major cortical interneuron classes in a competitive-cooperative computational primitive and validate these circuit motifs implemented soft winner-take-all (sWTA) computation for gain modulation, signal restorati

Brains can process sensory information from different modalities at astonishing speed; this is surprising as already the integration of inputs through the membrane causes a delayed response. Experiments reveal a possible explanation for the fast processing, showing that neurons can advance their output firing rate with respect to their input current, a concept which we refer to as prospective coding. The underlying mechanisms of prospective coding, however, are not completely understood. We propose a mechanistic explanation for neurons advancing their output on the level of single action poten

Spiking neural networks (SNNs) inherently rely on the timing of signals for representing and processing information. Transmission delays play an important role in shaping these temporal characteristics. Recent work [1, 2] has demonstrated the substantial advantages of learning these delays along with synaptic weights, both in terms of accuracy and memory efficiency. However, these approaches suffer from drawbacks in terms of precision and learning efficiency, as they operate in discrete time and with approximate gradients, while also requiring membrane potential recordings for calculating para

Invasive cortical brain-machine interfaces (BMIs) can significantly improve life quality for motor-impaired patients. However, externally mounted BMIs carry the risk of wound infection, necessitating fully implanted systems with integrated signal processing. Such systems must ensure reliable and accurate data processing while meeting tight latency and energy constraints. To meet these processing requirements, spiking neural networks (SNNs) seem an attractive solution due to their minimal preprocessing requirements of cortical spike trains (CSTs) and their suitability for ultra-low-power, low-

…enable continual learning on future neuromorphic hardware. One such neuroscience-inspired method is metaplasticity, which refers to a network's ability to change the learning capacity of its synapses based on its cu

…Despite its potential, neuromorphic event-based computing, which promises to fully harness the benefits of spiking networks, remains largely confined to research settings, with commercialization hindered by the lack of user-friendly software tools for developers. While initiatives like Intel's Lava for Loihi and PySpiNNaker2 for deploying SNNs on the SpiNNaker 2 neuromorphic chip are promis

The use of electromyography (EMG) for translating muscle activity into precise movements has become a pivotal technique to control myoelectric prosthetic devices, significantly improving the quality of life for amputees. Traditional EMG control of prosthetics relies on techniques that decode forearm muscle activity into discrete and simple gestures. Although robust and reliable, these methods are far from the dexterity of a real hand, and can hinder the users' acceptance. Recent advancements in neural network-based control offer a more intuitive approach, enabling amputees to achieve more natu

Memory is a key component of biological neural systems that enables the retention of information over a huge range of temporal scales, ranging from hundreds of milliseconds up to years. While Hebbian plasticity is believed to play a pivotal role in biological memory, it has so far been analyzed mostly in the context of pattern completion and unsupervised learning. Here, we propose that Hebbian plasticity is fundamental for computations in biological neural systems. We introduce a novel spiking neural network (SNN) architecture that is enriched by Hebbian synaptic plasticity. We experimentally

…energy efficiency when implemented on neuromorphic hardware. However, many of the biological features are at best irrelevant and at worst counterproductive when evaluated in the context of task performance and suitability for neuromorphic hardware. In this talk, I will present an alternative paradigm to design deep learning architectures with good task performance in real-world benchmarks while maintaining all the advantages of SNNs. We do this by focusing on two main feature

…training spiking RNNs on dedicated neuromorphic hardware is still an open challenge. This is due mainly to the lack of local, hardware-friendly learning mechanisms that can solve the temporal credit assignment problem and ensure stable network dynamics, even when the weight resolution is limited. These challenges are further accentuated, if one resorts to using memristive devices for in-memory computing

Recent advances in neuromorphic hardware and Surrogate Gradient (SG) learning highlight the potential of Spiking Neural Networks (SNNs) for energy-efficient signal processing and learning. Like in Artificial Neural Networks (ANNs), training performance in SNNs strongly depends on the initialization of synaptic and neuronal parameters. While there are established methods of initializing deep ANNs for high performance, effective strategies for optimal SNN initialization are lacking. Here, we address this gap and propose flexible data-dependent initialization strategies for SNNs.

…to build dedicated computers, such as neuromorphic hardware that emulate the biological processes of the brain. While the hardware technologies already exist, their application to real-world tasks is hindered by the lack of suitable programming methods. Advances at the interface of neural computation and machine learning showed that key aspects of deep learning models and tools can be transferred to biologically plausible neural circuits. Building on these advances, I will show that differ