SNUFA 2023 Abstracts
Click here for the full programme
Invited talks
- Neuromorphic learning: a control perspective (Rodolphe Sepulchre)
- Plasticity, activity, and computation in neuronal networks
- Analog substrates for temporal and local event-based computation (Melika Payvand)
- CARLSim: An Open-Source Community Resource for Large-Scale, Detailed Spiking Neural Network Research and Development (Jeff Krichmar)
Short talk
- Dendrites support formation and reactivation of sequential memories through Hebbian plasticity (Alessio Quaresima, Hartmut Fitz, Renato Duarte, Peter Hagoort, Karl Magnus Petersson)
- Combining various types of spike timing-dependent plasticity to learn efficient neural codes (Antony W. N’dri, Céline Teulière and Jochen Triesch)
- Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacings (Ilyass Hammouamri, Ismail Khalfaoui Hassani, Timothée Masquelier)
- Training Spiking Neural Networks for Continuous Control with Surrogate Gradients (Justus Huebotter, Pablo Lanillos, Serge Thill, Marcel van Gerven)
- Capacity of a spiking network with preserved weight distribution: a game-theory-inspired study (Maayan Levy & Tim P. Vogels)
- Training fast spiking networks through STDP and control feedback (Alexander Efremov, Martino Sorbaro, Pau Vilimelis Aceituno, Benjamin Grewe)
- Efficient computing of high-dimensional neural representations with biologically plausible E-I spiking networks (Veronika Koren)
Flash talk
- Library of dynamics: linking parameters and behaviour of spiking networks with Simulation-Based Inference (Basile Confavreux, Aaradhya Vaze, Poornima Ramesh, Pedro Gonçalves, Jakob H. Macke and Tim P. Vogels)
- Are training trajectories of deep single-spike and deep ReLU network equivalent? (Ana Stanojevic, Stanislaw Wozniak, Guillaume Bellec, Giovanni Cherubini, Angeliki Pantazi, Wulfram Gerstner)
- Exploring the Potential of Heterogeneous Spiking Neural Networks for Neuro-AI Advancements (Biswadeep Chakraborty)
- Distributing task-related neural activity across a cortical network through task-independent connections (Christopher Kim)
- Neuromorphic Intermediate Representation (Jens Egholm Pedersen, Steven Abreu, Matthias Jobst, Vittorio Fra, and Sadique Sheik)
- Comparing surrogate gradients and likelihood-based training for spiking neural networks (Julia Gygax, Friedemann Zenke)
- Retina optimised for prediction across animal species (Luke Taylor, Andrew J King, Nicol Spencer Harper)
- Training SNNs with Local Synaptic Plasticity and Online Error Signal (Mike Stuck and Richard Naud)
- How do receptor secretion and transport properties lead to the regularization of synaptic plasticity and benefit learning? (Oleg Nikitin, Olga Lukyanova, Alex Kunin)
- Noise cancellation and visual processing using Resonate-and-Fire neurons (Reem Al Fata, Jules Lecomte)
- First-spike coding promotes accurate and efficient spiking neural networks for discrete events with rich temporal structures (Siying Liu)
Poster
- Linking Rate-Based And Spiking Models: A Quest Towards Biologically Relevant Neural Systems (Aiswarya P S, Indian Institute of Science Education and Research, Thiruvananthapuram, India)
- Smooth Exact Gradient Descent Learning in Spiking Neural Networks (Christian Klos, Raoul-Martin Memmesheimer)
- Training spiking trisynaptic circuit models to perform spatial-memory dependent navigation using reinforcement learning (Christopher Earl)
- Laminar Organization and Information Integration in Spiking Recurrent Neural Networks (Aidai Kazybekova, Fatemeh Hadaeghi, Michael Winklhofer, Claus C. Hilgetag)
- A computational model of mammalian brainstem to solve sound localization (Francesco De Santis, Alberto Antonietti)
- Input regularization mechanisms of the olfactory bulb glomerular layer (J. Forest, K. R. Mama, R. Moyal, M. Einhorn, A. Borthakur, T. A. Cleland)
- Enhancing the Cleo experiment simulation testbed to support all-optical control, multi-channel optogenetics, and easier integration into data analysis pipelines (Kyle A. Johnsen, Nathanael A. Cruzado, Adam S. Charles, Christopher J. Rozell)
- Accurate detection of precise spiking motifs in neurobiological data (Laurent Perrinet)
- The neural tangent kernel in networks with step-like activation functions (Luke Eilers, Sven Goedeke)
- On the role of heterogeneity in multimodal networks (Marcus Ghosh, Gabriel Béna, Volker Bormuth, Dan F. M. Goodman )
- Real-Time Gesture Recognition with Event Camera (Muhammad Aitsam)
- The dual nature of synaptic homeostasis: Interaction between fast and slow processes (Petros Evgenios Vlachos)
- The Effect of Neuron Behavior on Local Field Potentials (Rahmi Elibol)
- MotorSRNN: A Brain-Inspired Spiking Recurrent Neural Network for Accuracy-Energy-Balanced Cortical Spike Train Decoding (Tengjun Liu)
- SNN Model Training Evaluated Through Loss Landscapes (Thomas Summe, Siddarth Joshi)
- Efficient Uncertainty Estimation in Spiking Neural Networks via MC-dropout (Tao Sun)
- Nanoimaging of Correlated Oxide Memristive Devices (Yohannes Abate)