Paper List
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Evolutionarily Stable Stackelberg Equilibrium
通过要求追随者策略对突变入侵具有鲁棒性，弥合了斯塔克尔伯格领导力模型与演化稳定性之间的鸿沟。
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Recovering Sparse Neural Connectivity from Partial Measurements: A Covariance-Based Approach with Granger-Causality Refinement
通过跨多个实验会话累积协方差统计，实现从部分记录到完整神经连接性的重建。
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Atomic Trajectory Modeling with State Space Models for Biomolecular Dynamics
ATMOS通过提供一个基于SSM的高效框架，用于生物分子的原子级轨迹生成，弥合了计算昂贵的MD模拟与时间受限的深度生成模型之间的差距。
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Slow evolution towards generalism in a model of variable dietary range
通过证明是种群统计噪声（而非确定性动力学）驱动了模式形成和泛化食性的演化，解决了间接竞争下物种形成的悖论。
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Grounded Multimodal Retrieval-Augmented Drafting of Radiology Impressions Using Case-Based Similarity Search
通过将印象草稿基于检索到的历史病例，并采用明确引用和基于置信度的拒绝机制，解决放射学报告生成中的幻觉问题。
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Unified Policy–Value Decomposition for Rapid Adaptation
通过双线性分解在策略和价值函数之间共享低维目标嵌入，实现对新颖任务的零样本适应。
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Mathematical Modeling of Cancer–Bacterial Therapy: Analysis and Numerical Simulation via Physics-Informed Neural Networks
提供了一个严格的、无网格的PINN框架，用于模拟和分析细菌癌症疗法中复杂的、空间异质的相互作用。
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Sample-Efficient Adaptation of Drug-Response Models to Patient Tumors under Strong Biological Domain Shift
通过从无标记分子谱中学习可迁移表征，利用最少的临床数据实现患者药物反应的有效预测。
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.