A neural network model combining the successor representation and actor-critic methods reveals effective biological use of the representation [0.03%]
结合后继者表示法和策略评估方法的神经网络模型揭示了有效利用该表征的生物机制
Takayuki Tsurumi,Kenji Morita
Takayuki Tsurumi
In learning goal-directed behavior, state representation is important for adapting to the environment and achieving goals. A predictive state representation called successive representation (SR) has recently attracted attention as a candida...
Claudius Gros
Claudius Gros
The big strides seen in generative AI are not based on somewhat obscure algorithms, but due to clearly defined generative principles. The resulting concrete implementations have proven themselves in large numbers of applications. We suggest...
Exploring internal representations of self-supervised networks: few-shot learning abilities and comparison with human semantics and recognition of objects [0.03%]
探究自监督网络的内部表征：少量样本学习能力以及与人类语义和物体识别的比较
Asaki Kataoka,Yoshihiro Nagano,Masafumi Oizumi
Asaki Kataoka
Recent advances in self-supervised learning have attracted significant attention from both machine learning and neuroscience. This is primarily because self-supervised methods do not require annotated supervisory information, making them ap...
A hierarchical Bayesian inference model for volatile multivariate exponentially distributed signals [0.03%]
多变的多元指数分布信号的分层贝叶斯推断模型
Changbo Zhu,Ke Zhou,Fengzhen Tang et al.
Changbo Zhu et al.
Brain activities often follow an exponential family of distributions. The exponential distribution is the maximum entropy distribution of continuous random variables in the presence of a mean. The memoryless and peakless properties of an ex...
Common characteristics of variants linked to autism spectrum disorder in the WAVE regulatory complex [0.03%]
与谱系障碍相关的WAVE调控复合体变异的共同特征
Song Xie,Ke Zuo,Silvia De Rubeis et al.
Song Xie et al.
Six variants associated with autism spectrum disorder (ASD) abnormally activate the WASP-family Verprolin-homologous protein (WAVE) regulatory complex (WRC), a critical regulator of actin dynamics. This abnormal activation may contribute to...
Mojtaba Madadi Asl
Mojtaba Madadi Asl
Triboelectric nanogenerators for neural data interpretation: bridging multi-sensing interfaces with neuromorphic and deep learning paradigms [0.03%]
用于神经数据解释的摩擦纳米发电机：多传感接口与神经形态和深度学习范式的桥梁技术
Lingli Gan,Shuqin Yuan,Min Guo et al.
Lingli Gan et al.
The rapid growth of computational neuroscience and brain-computer interface (BCI) technologies require efficient, scalable, and biologically compatible approaches for neural data acquisition and interpretation. Traditional sensors and signa...
Neural heterogeneity as a unifying mechanism for efficient learning in spiking neural networks [0.03%]
神经异质性作为尖峰神经网络高效学习的统一机制
Fudong Zhang,Jingjing Cui
Fudong Zhang
The brain is a highly diverse and heterogeneous network, yet the functional role of this neural heterogeneity remains largely unclear. Despite growing interest in neural heterogeneity, a comprehensive understanding of how it influences comp...
Interleaving cortex-analog mixing improves deep non-negative matrix factorization networks [0.03%]
类脑交织混合改善深度非负矩阵分解网络
Mahbod Nouri,David Rotermund,Alberto Garcia-Ortiz et al.
Mahbod Nouri et al.
Considering biological constraints in artificial neural networks has led to dramatic improvements in performance. Nevertheless, to date, the positivity of long-range signals in the cortex has not been shown to yield improvements. While Non-...
Universal differential equations as a unifying modeling language for neuroscience [0.03%]
万能微分方程作为神经科学统一建模语言的优势
Ahmed El-Gazzar,Marcel van Gerven
Ahmed El-Gazzar
The rapid growth of large-scale neuroscience datasets has spurred diverse modeling strategies, ranging from mechanistic models grounded in biophysics, to phenomenological descriptions of neural dynamics, to data-driven deep neural networks ...