SNUFA 2026 Abstracts

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Invited talks (TBD)

Talk

• Synaptic plasticity selects transmission delays for resonant communication in spiking networks (Ali Ghadiri, Saeed Taghavi, Hedyeh Rezaei, Ad Aertsen, Arvind Kumar, Alireza Valizadeh)
• Where forgetting lives in a spiking continual-learning memory (Anindya Ghosh, Alejandro Rodriguez-Garcia, Srikanth Ramaswamy)
• Time-reversed event-based simulations enable memory-efficient exact gradient descent in spiking neural networks (Christian Klos)
• Firing Rate Homeostasis Improves Learning Through Irregular Spiking (Huihong Li, Daniel Levenstein)
• A General Theory of Exact Gradients in Spiking Neural Networks & The Potential of Biorealistic Neuron Models (Lennart P. L. Landsmeer, Amirreza Movahedin, Said Hamdioui, Christos Strydis)
• Energy-Efficient Neural Coding under Uniform and Heavy-Tailed Input Distributions (Robin Duvoisin, Jean-Pascal Pfister, Pau Vilimelis Aceituno)
• Spike timing neural manifolds for learning, memory and computation in the brain (Simon R. Schultz)

Flash talk

• Bio-plausible statistical regularizer for spiking JEPA (Alexis Gleyo, Laurent Perrinet)
• Learning part-whole relationships through polychronization in a hierarchical spiking network (Brian Gardner, Patrick McCarthy, Joseph Chrol-Cannon, Dan Goodman, Simon Schultz, Giovanni Lo Iacono, Simon Stringer)
• Energy-Efficient Spike-driven Transformer for Satellite-Streak Segmentation in Space Surveillance (Iulia Maria Filimon, Radu Danescu)
• Audio Event Classification with Spiking Neural Networks and e-prop (Lucía Torrescusa-Rubio, Xavier Otazu)
• Learning to play with spikes: characterizing, predicting, and engineering unsupervised plasticity rules for spiking reservoir computing (Maciej Kania, Basile Confavreux, Tim P. Vogels)
• Neural heterogeneity promotes robust learning by increasing representational separability (Priyam Rai, Hardik Rajpal, Marcus Ghosh)
• Short-term synaptic plasticity reshapes credit assignment through eligibility traces in recurrent E/I spiking networks (Mauricio A. Diaz, Janina Hesse)
• Motiforge: Flexible Motif-Constrained Network Generation (Noah Ostendorf, Barna Zajzon, Abigail Morrison)
• Is Poisson-like spiking essential to general-purpose neural information processing? (Rich Pang)
• Learning with delays: white-matter plasticity and timing in spiking networks (Stephen Coombes, Rüdiger Thul)
• Building and interpreting spiking network dynamics via sliding-mode control (William F. Podlaski, Christian K. Machens)

Poster

• Creating functional engrams by swapping synaptic weights in spiking neural networks (Aaradhya Vaze, Maayan Levy, Tim Vogels)
• Spike-Coding Networks Are Exactly Solvable: Periodic Firing Patterns as Eigenvectors (William Podlaski, Ildefonso Ferreira Pica)
• A minimal model of working memory in neural systems and neuromorphic circuits (Damien Depannemaecker, Adrien d’Hollande, Gabriele Casagrande, Jiaming Wu, Marcelo Rozenberg)
• Learning sensorimotor representations through active event-based foveated sensing (Alejandro Rodriguez-Garcia, Anindya Ghosh, Arthur Aubret, Srikanth Ramaswamy, Giulia D’Angelo)
• The Role of Dopamine in Resolving the Speed-Accuracy Trade-Off (Alex Gwyn O’Hare, Jonathan E. Rubin, Timothy Verstynen, Catalina Vich)
• Kairos: A Novel Neuromorphic Architecture for Efficient Simulation of SNNs with Long-range Synaptic Delays (Amirreza Movahedin, Lennart P. L. Landsmeer, Said Hamdioui, Christos Strydis)
• Bifurcation Structure Underlying Tonic-Clonic Seizure Transitions (Ana Mayora-Cebollero (Universidad de Zaragoza), Oliver Cattell (University of Nottingham), Reuben O’Dea (University of Nottingham), Roberto Barrio (Universidad de Zaragoza), Stephen Coombes (University of Nottingham))
• A deformable attractor manifold organizes human resting-state brain dynamics (Anastasios P. Athanasiadis, Marmaduke M. Woodman, Amirhossein Esmaeili, Meysam Hashemi, Spase Petkoski, Viktor K. Jirsa)
• The inNuCE Research Infrastructure for Developing Neuromorphic AIoT Applications (Andrea Pignata, Gianvito Urgese)
• Emergence of biological responses tuned to predictive codes in Drosophila larvae (Antony W. N’dri, Frédéric Guyard)
• No computation without representation: Abstraction/Representation Theory applied to neuromorphic and neural wetware computers (Aslan SAtary Dizaji)
• Bounds for Universal Representation with Spiking Neural Networks (Aston Wan, Evan Zhao, Nathan Yan)
• TSKI 5: A Bio-Inspired Discrete Temporal Neural Network Model without Gradient Descent or a Global Error Function. (Atorin Andrii )
• Deep Learning to Analyze Chaotic Dynamics in Computational Neuroscience (Carmen Mayora-Cebollero (Universidad de Zaragoza), Roberto Barrio (Universidad de Zaragoza), Álvaro Lozano (Universidad de Zaragoza), Ana Mayora-Cebollero (Universidad de Zaragoza))
• Ion-Channel State and Neuronal Firing: A Quantum Signal Processing Case Study (Chitaranjan Mahapatra)
• DeltaQ: Value-Guided Hebbian Learning in Spiking Neuronal Networks for Multi-Goal Navigation (Christopher Earl, Gozde Unal, Hananel Hazan, Samuel Neymotin)
• Studying Minimal Complexity: From Neural Oscillators to Spiking Neural Networks for Robust Graph Reconstruction (Fernando Vera Buschmann, Horacio Rotstein, Vincent Oria)
• Insect Neural Mechanisms of Navigation Through Robotics (Gabriel Gattaux)
• Chaos as Exploration: Dopamine-Modulated Spiking Attractors That Discover How to Walk on a Real Robot (Gabriel Torre, Juan Giribet, Sergio Lew)
• Multi-Delay-Driven Hopfield Networks: High-Capacity Sequence Memory via Global Remapping (Julius Rosenberg, Lilli Kiessling, Pau Vilimelis Aceituno, Matthew Cook, Georg Reich)
• Image generation with e-prop: Application to musical symbols. (Gerard Asbert, Alicia Fornés, Xavier Otazu)
• Breaking Point, Engineered: Agents That Design, Prove, and Certify Fault-Tolerant Spiking Networks (Breaking Point, Engineered: Agents That Design, Prove, and Certify Fault-Tolerant Spiking Networks)
• Degeneracy and unidentifiability in spiking and oscillatory neural networks: Two faces of the same coin (Horacio G. Rotstein)
• Limits of Local Credit Assignment in Recurrent Neural Networks (Jesús Andrés Espinoza-Valverde, Gustavo Ramirez-Hidalgo)
• Role of axo-axonic synapses in Drosophila sensory processing (Jiaqi Chen, Daniel Han, Albert Cardona)
• Factorization and spatial encoding: how the genomic code may wire up a brain (Karim G. Habashy, Benjamin D. Evans, Dan F. M. Goodman, Jeffrey S. Bowers)
• Analyzing Surrogate Gradient Training Stability in Spiking Neural Networks Using Dynamical Systems Theory (Kevin Karim)
• Chained Neural Delays for Extended Temporal Pattern Tracking (Laurent Perrinet)
• A Structured Neuron-Space for Analysing Functional Organization in Spiking Networks (Mehran Jaberinezhad)
• One-sided saturation in spiking neurons: the inhibitory dynamic range equals the operating point (Nada Fath, Feng Wang, Daoshu Luo)
• Blood and Modularity: A Brain-Inspired Approach to Functional Disentangled Neural Representations (Nicolas Dundov, Dan Goodman)
• Learning quadruped and hexapod gaits through predictive spiking control of neuromodulated CPGs (Oleg Nikitin, Olivia Lukyanova, Alex Kunin)
• When a correlation metric cannot tell learning from non-learning in a spiking sensorimotor network (Pan haimeng)
• Clustered heterogeneity, spiking history and efficient silencing maximize mutual information in adaptive burst coding of time (Raphael Lafond-Mercier, Leonard Maler, Andre Longtin)
• Unimodal versus bimodal learning and processing of audio-visual stimuli in spiking neural networks (Raphaël Bergoin,Alessandro Torcini,Gustavo Deco,Mathias Quoy,Mario Senden,Gorka Zamora-López)
• Computational principles of dual contribution of dopamine in reinforcement learning and action selection (Roberto Sautto, Nicolas Cuperlier, Thanos Manos, Marwen Belkaid)
• Spiking of locally connected parvalbumin-positive fast-spiking inhibitory interneurons encodes spatial network topography and maps excitatory drive in layer ⅔ of the medial entorhinal cortex. (Roksolana Demchynska, Serhii Zavhorodnii, Bohdana Ruzhytska, Sergii Romanenko, Liubov Sadova, Yelyzaveta Razumikhina, Veronika Kisliakova, Yaroslav Subin,Ruben Tikidji-Hamburyan)
• Deriving, learning, and understanding heterogeneous spike coding networks (Stefan Iacob, Sander Keemink, Fleur Zeldenrust)
• Brain Age Prediction from EEG Features Using Spiking Neural Networks (William Klein, Jessica Fleck)
• Axo-axonic inhibition suppresses Kenyon cell output in the Drosophila mushroom body (Yudi Wu, Albert Cardona)