Hayden McAlister,Anthony Robins,Lech Szymanski
Hayden McAlister
Sequential learning involves learning tasks in a sequence and proves challenging for most neural networks. Biological neural networks regularly succeed at the sequential learning challenge and are even capable of transferring knowledge both...
Measuring Stimulus Information Transfer Between Neural Populations through the Communication Subspace [0.03%]
通过通信子空间测量神经元群体间的刺激信息传递
Oren Weiss,Ruben Coen-Cagli
Oren Weiss
Sensory processing arises from the communication between neural populations across multiple brain areas. While the widespread presence of neural response variability shared throughout a neural population limits the amount of stimulus-relate...
Aishwarya Balwani,Suhee Cho,Hannah Choi
Aishwarya Balwani
The cortex plays a crucial role in various perceptual and cognitive functions, driven by its basic unit, the canonical cortical microcircuit. Yet, we remain short of a framework that definitively explains the structure-function relationship...
Crystal-LSBO: Automated Design of De Novo Crystals with Latent Space Bayesian Optimization [0.03%]
基于潜变量空间贝叶斯优化的从头设计晶体研究
Onur Boyar,Yanheng Gu,Yuji Tanaka et al.
Onur Boyar et al.
Generative modeling of crystal structures is significantly challenged by the complexity of input data, which constrains the ability of these models to explore and discover novel crystals. This complexity often confines de novo design method...
Nonlinear Neural Dynamics and Classification Accuracy in Reservoir Computing [0.03%]
非线性神经动力学与 reservoir computing 中的分类精度
Claus Metzner,Achim Schilling,Andreas Maier et al.
Claus Metzner et al.
Reservoir computing information processing based on untrained recurrent neural networks with random connections is expected to depend on the nonlinear properties of the neurons and the resulting oscillatory, chaotic, or fixed-point dynamics...
Continuous-Time Neural Networks Can Stably Memorize Random Spike Trains [0.03%]
连续时间神经网络可以稳定地记忆随机脉冲列
Hugo Aguettaz,Hans-Andrea Loeliger
Hugo Aguettaz
This letter explores the capability of continuous-time recurrent neural networks to store and recall precisely timed scores of spike trains. We show (by numerical experiments) that this is indeed possible: within some range of parameters, a...
A Categorical Framework for Quantifying Emergent Effects in Network Topology [0.03%]
网络拓扑中量化涌现效应的分类框架
Johnny Jingze Li,Sebastian Pardo Guerra,Kalyan Basu et al.
Johnny Jingze Li et al.
Emergent effect is crucial to understanding the properties of complex systems that do not appear in their basic units, but there has been a lack of theories to measure and understand its mechanisms. In this letter, we consider emergence as ...
Predictive Coding Model Detects Novelty on Different Levels of Representation Hierarchy [0.03%]
预测编码模型在层级表征的不同层次检测新颖性
T Ed Li,Mufeng Tang,Rafal Bogacz
T Ed Li
Novelty detection, also known as familiarity discrimination or recognition memory, refers to the ability to distinguish whether a stimulus has been seen before. It has been hypothesized that novelty detection can naturally arise within netw...
Aleksandra Świetlicka
Aleksandra Świetlicka
Artificial neural networks (ANNs) have shown great potential in enhancing human-robot interaction (HRI). ANNs are computational models inspired by the structure and function of biological neural networks in the brain, which can learn from e...
Closed-Loop Multistep Planning [0.03%]
闭环多步规划
Giulia Lafratta,Bernd Porr,Christopher Chandler et al.
Giulia Lafratta et al.
Living organisms interact with their surroundings in a closed-loop fashion, where sensory inputs dictate the initiation and termination of behaviors. Even simple animals are able to develop and execute complex plans, which has not yet been ...