Paper List
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GOPHER: Optimization-based Phenotype Randomization for Genome-Wide Association Studies with Differential Privacy
This paper addresses the core challenge of balancing rigorous privacy protection with data utility when releasing full GWAS summary statistics, overco...
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Real-time Cricket Sorting By Sex A low-cost embedded solution using YOLOv8 and Raspberry Pi
This paper addresses the critical bottleneck in industrial insect farming: the lack of automated, real-time sex sorting systems for Acheta domesticus ...
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Training Dynamics of Learning 3D-Rotational Equivariance
This work addresses the core dilemma of whether to use computationally expensive equivariant architectures or faster symmetry-agnostic models with dat...
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Fast and Accurate Node-Age Estimation Under Fossil Calibration Uncertainty Using the Adjusted Pairwise Likelihood
This paper addresses the dual challenge of computational inefficiency and sensitivity to fossil calibration errors in Bayesian divergence time estimat...
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Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training
This paper addresses the core challenge of accurately predicting protein fitness with only a handful of experimental observations, where data collecti...
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scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing
This paper addresses the critical gap of fragmented and non-standardized benchmarking in single-cell RNA-seq clustering, which hinders objective compa...
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Simulation and inference methods for non-Markovian stochastic biochemical reaction networks
This paper addresses the computational bottleneck of simulating and performing Bayesian inference for non-Markovian biochemical systems with history-d...
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Assessment of Simulation-based Inference Methods for Stochastic Compartmental Models
This paper addresses the core challenge of performing accurate Bayesian parameter inference for stochastic epidemic models when the likelihood functio...
Equivalence of approximation by networks of single- and multi-spike neurons
Faculty of Mathematics and Research Network DataScience @ Uni Vienna, University of Vienna
30秒速读
IN SHORT: This paper resolves the fundamental question of whether single-spike spiking neural networks (SNNs) are inherently less expressive than multi-spike SNNs, proving their theoretical equivalence in approximation capabilities.
核心创新
- Theory Established a formal transference principle (Theorem 1) proving that approximation bounds for multi-spike SNNs directly translate to single-spike SNNs with at most N_s·n neurons, and vice versa.
- Methodology Developed constructive proofs showing how to replace any multi-spike neuron with N_s single-spike neurons (by threshold adjustment) and any single-spike neuron with αN_s multi-spike neurons (via spike cancellation).
- Theory Extended the equivalence to include lower bounds (Corollary 1) and common input encoders (Corollary 2), making existing theoretical results for one paradigm immediately applicable to the other.
主要结论
- Single-spike and multi-spike SNNs are theoretically equivalent in approximation capabilities for a large class of neuron models including LIF with subtractive reset.
- Any approximation bound for multi-spike SNNs with n neurons translates to single-spike SNNs with at most N_s·n neurons (linear scaling in maximum spike count).
- The reverse direction holds with prefactor α ≤ min(1, 6/π² + 1/√N_s) for N_s ≥ 1, and α < 6/π² + 1/(2√N_s) for N_s ≥ 8.
摘要: In a spiking neural network, is it enough for each neuron to spike at most once? In recent work, approximation bounds for spiking neural networks have been derived, quantifying how well they can fit target functions. However, these results are only valid for neurons that spike at most once, which is commonly thought to be a strong limitation. Here, we show that the opposite is true for a large class of spiking neuron models, including the commonly used leaky integrate-and-fire model with subtractive reset: for every approximation bound that is valid for a set of multi-spike neural networks, there is an equivalent set of single-spike neural networks with only linearly more neurons (in the maximum number of spikes) for which the bound holds. The same is true for the reverse direction too, showing that regarding their approximation capabilities in general machine learning tasks, single-spike and multi-spike neural networks are equivalent. Consequently, many approximation results in the literature for single-spike neural networks also hold for the multi-spike case.