Shubham Choudhary,Paul Masset,Demba Ba
Shubham Choudhary
Understanding how the brain encodes stimuli has been a fundamental problem in computational neuroscience. Insights into this problem have led to the design and development of artificial neural networks that learn representations by incorpor...
Signal-Dependent Planning Noise Reduces Task Interference by Assisting in the Formation of Stable Motor Primitives in a Neural Network Model of Muscle Coordination Learning [0.03%]
信号依赖性规划噪声通过协助在肌肉协调学习的神经网络模型中形成稳定的运动原理由而减少任务干扰
Daniel W Feng,David J Reinkensmeyer,Juan C Perez-Ibarra
Daniel W Feng
In human motor coordination, learning to coactivate multiple muscles at once to achieve distinct target combinations of forces or tasks remains a fundamental area of study. Task interference, where training on one task degrades performance ...
Dmitri Rachkovskij,Evgeny Osipov,Olexander Volkov et al.
Dmitri Rachkovskij et al.
This article introduces a family of multiclass linear perceptron classifiers with a multiplicative margin mechanism (MMPerc), as an alternative to standard margin-free and additive margin perceptrons. The multiplicative formulation enforces...
Takashi Kanamaru,Kazuyuki Aihara
Takashi Kanamaru
Force learning is a learning method for generating various types of complex dynamics in recurrent neural networks (RNNs), which is related to the reservoir computing (RC). RC uses an RNN called reservoir whose synaptic weights are randomly ...
The Cooperative Network Architecture: Learning Structured Networks as Representation of Sensory Patterns [0.03%]
合作的网络架构：学习结构化网络作为感觉模式的表示方法
Pascal J Sager,Jan M Deriu,Benjamin F Grewe et al.
Pascal J Sager et al.
We introduce the cooperative network architecture (CNA), a model that represents sensory signals using structured, recurrently connected networks of neurons, termed "nets." Nets are dynamically assembled from overlapping net fragments, whic...
Richard W Prager,Richard Apps
Richard W Prager
This article explores how simple reinforcement learning algorithms might be implemented by the anatomy of the cerebellum. In doing this, we highlight which anatomical and physiological details are most important for assessing algorithmic fi...
Peter Neri
Peter Neri
Sensory operators are classically modeled using small circuits involving canonical computations, such as energy extraction and gain control. Notwithstanding their utility, circuit models do not provide a unified framework encompassing the v...
Inhibitory Feedback Enables Predictive Learning of Multiple Sequences in Neural Networks [0.03%]
抑制性反馈使神经网络能够预测学习多个序列
Matteo Saponati,Martin Vinck
Matteo Saponati
Anticipating future events is a key computational task for neuronal networks. Experimental evidence suggests that reliable temporal sequences in neural activity play a functional role in the association and anticipation of events in time. H...
ReBaCCA-ss: Relevance-Balanced Continuum Correlation Analysis With Smoothing and Surrogating for Quantifying Similarity Between Population Spiking Activities [0.03%]
基于平滑和替代的平衡相关连续性分析（ReBaCCA-ss）量化人群神经元放电活动相似性的方法
Xiang Zhang,Chenlin Xu,Zhouxiao Lu et al.
Xiang Zhang et al.
Quantifying similarity between population spike patterns is essential for understanding how neural dynamics encode information. Traditional approaches, which combine kernel smoothing, principal component analysis, and canonical correlation ...
Attractor-Based Models for Sequences and Pattern Generation in Neural Circuits [0.03%]
基于一体吸引子的序列和模式生成模型
Juliana Londono Alvarez,Katherine Morrison,Carina Curto
Juliana Londono Alvarez
Neural circuits in the brain perform a variety of essential functions, including input classification, pattern completion, and the generation of rhythms and oscillations that support functions such as breathing and locomotion. There is also...