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Brain-Inspired Computing

Bridging neuroscience and computer engineering through spiking networks, neuromorphic hardware and biologically plausible learning rules.

Open for submissionsSubmission deadline: 15 November 2026Hosted by Computational Neuroscience Review
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Articles in this collection

Original Research

Short-term synaptic plasticity stabilises working memory in sparse spiking networks

Daniel Osei, Lucas Ferreira

Persistent activity has long been proposed as the substrate of working memory, but sustaining it in sparse cortical networks is energetically costly. We show that short-term synaptic facilitation allows information to be held in latent synaptic states, reducing the need for continuous firing. Simulations reproduce the activity-silent dynamics observed in primate prefrontal recordings and predict a characteristic reactivation signature after brief non-specific input.

Original Research

Energy-efficient event-based vision with neuromorphic recurrent architectures

Daniel Osei, Lucas Ferreira

Event cameras generate sparse, asynchronous data streams well suited to neuromorphic processing. We propose a recurrent spiking architecture that achieves competitive accuracy on gesture and optical-flow benchmarks while consuming an order of magnitude less energy than conventional convolutional baselines on equivalent hardware.

Original Research

Graph neural networks predict individual cognitive scores from structural connectomes

Daniel Osei

Structural connectomes encode rich information about individual brain organisation. We train graph neural networks on diffusion-MRI-derived connectomes from 9,000 participants and show they outperform kernel baselines in predicting fluid intelligence, with attributions concentrated in fronto-parietal hubs.