How does a brain wire itself, starting from a single cell, using only the information encoded in a genome? We pose this as an engineering problem.

The genomic mechanisms that efficiently encode the initial architecture and synaptic connectivity of neural circuits remain poorly understood. We hypothesise that two primary mechanisms — spatial enco...

Long-serving prime minister beaten by opposition after early results showed clear lead

A flexible framework for structural plasticity in GPU-accelerated sparse spiking neural networks, Knight, James C, Senk, Johanna, Nowotny, Thomas

Spiking Neural Networks As Universal Function Approximators

Olfactory sensing begins with the transduction of odors into receptor currents on the dendrites of olfactory receptor neurons (ORNs). In insects and many other arthropods, ORNs are grouped stereotypically in hair-like sensilla on the surface of olfactory organs, enabling mutual inhibition through non-synaptic 'ephaptic' interactions (NSIs). Given the electrical, and therefore virtually instantaneous, nature of NSIs, it has been hypothesized that they contribute to processing fast temporal elements of mixed odor plumes. Here, we present single sensillum recordings and computational modeling that characterize NSIs during short offset dual-odor stimulations in the olfactory sensilla of adult female Drosophila melanogaster. We find in the experiments that the magnitude of inhibition between co-housed ORNs cannot be predicted by their instantaneous activity (firing rate) alone. It is adaptation-dependent, with strong effects only occurring when the inhibited ORN is adapted. This limits the usefulness of NSIs for fast odor processing when ORNs lack time to adapt. We reproduced the observed phenomena in a computational model and use this model to explain how the adaptation-dependence of NSI-mediated inhibition arises from nonlinearities in neural responses. We conclude that NSIs are unlikely to support the encoding of fast temporal dynamics in mixed odor stimuli, instead contributing to slower peripheral processing, supporting roles such as novelty detection. More broadly, we demonstrate how the nonlinear interactions of fairly simple electrical components lead to non-intuitive results, offering insight into the longstanding debate around ephaptic interactions in other systems, such as the mammalian CNS. ### Competing Interest Statement The authors have declared no competing interest. Leverhulme Trust, https://ror.org/012mzw131, RPG-2019-232 Engineering and Physical Sciences Research Council, https://ror.org/0439y7842, EP/P006094/1, EP/S030964/1

Inspired by how neurons in the brain communicate, Spiking Neural Networks are gaining attention as efficient models for solving spatiotemporal AI tasks. The authors introduce a training method for syn...

Inspired by how neurons in the brain communicate, Spiking Neural Networks are gaining attention as efficient models for solving spatiotemporal AI tasks. The authors introduce a training method for syn...

Spiking neural networks as universal function approximators (SNUFA) online workshop 2025. For more see http://snufa.net/2025/

Author summary Many human sports, including tennis, football, and basketball, rely on the ability to visually detect and respond to the motion of a small, rapidly moving object. Indeed, some sports st...

Spiking Neural networks as Universal Function Approximators

Spiking Neural networks as Universal Function Approximators

Computational sciences offer an opportunity to increase global access to, and participation in, neuroscience. Neuromatch’s inclusive, scalable model for community building shows how to realize this…

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This form collects valuable information from the Neuromorphic Community as part of a project led by Matteo Saponati, Laura Kriener, Sebastian Billaudelle, Filippo Moro, and Melika Payvand. The goal is...

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The extent to which structural modularity in neural networks ensures functional specialization remains unclear. Here the authors show that specialization can emerge in neural modules placed under reso...

Spiking Neural Networks (SNNs) are attracting increased attention as a more energy-efficient alternative to traditional Artificial Neural Networks. Spiking neurons are stateful and intrinsically recur...

Loss shaping enhances exact gradient learning with Eventprop in spiking neural networks, Nowotny, Thomas, Turner, James P, Knight, James C