Distributed Synaptic Connection Strength Changes Dynamics in a Population Firing Rate Model in Response to Continuous External Stimuli [0.03%]
连续外部刺激下群体 firing rate 模型中分布式突触连接强度变化动力学特性研究
Masato Sugino,Mai Tanaka,Kenta Shimba et al.
Masato Sugino et al.
Neural network complexity allows for diverse neuronal population dynamics and realizes higherorder brain functions such as cognition and memory. Complexity is enhanced through chemical synapses with exponentially decaying conductance and gr...
Elucidating the Theoretical Underpinnings of Surrogate Gradient Learning in Spiking Neural Networks [0.03%]
揭示突触神经网络中替代梯度学习的理论基础
Julia Gygax,Friedemann Zenke
Julia Gygax
Training spiking neural networks to approximate universal functions is essential for studying information processing in the brain and for neuromorphic computing. Yet the binary nature of spikes poses a challenge for direct gradient-based tr...
Jose Miguel Ramos,Luis Sa-Couto,Andreas Wichert
Jose Miguel Ramos
A vast majority of the current research in the field of machine learning is done using algorithms with strong arguments pointing to their biological implausibility such as backpropagation, deviating the field's focus from understanding its ...
The Leaky Integrate-and-Fire Neuron Is a Change-Point Detector for Compound Poisson Processes [0.03%]
渗漏积分和火神经元是复合泊松过程的变更点检测器
Shivaram Mani,Paul Hurley,André van Schaik et al.
Shivaram Mani et al.
Animal nervous systems can detect changes in their environments within hundredths of a second. They do so by discerning abrupt shifts in sensory neural activity. Many neuroscience studies have employed change-point detection (CPD) algorithm...
Jhunlyn Lorenzo,Juan-Antonio Rico-Gallego,Stéphane Binczak et al.
Jhunlyn Lorenzo et al.
From biological and artificial network perspectives, researchers have started acknowledging astrocytes as computational units mediating neural processes. Here, we propose a novel biologically inspired neuron-astrocyte network model for imag...
Hanna Pankka,Jaakko Lehtinen,Risto J Ilmoniemi et al.
Hanna Pankka et al.
Forecasting electroencephalography (EEG) signals, that is, estimating future values of the time series based on the past ones, is essential in many real-time EEG-based applications, such as brain-computer interfaces and closed-loop brain st...
Context-Sensitive Processing in a Model Neocortical Pyramidal Cell With Two Sites of Input Integration [0.03%]
具有两个输入整合位点的模型新大脑皮层锥体细胞中的上下文相关处理
Bruce P Graham,Jim W Kay,William A Phillips
Bruce P Graham
Neocortical layer 5 thick-tufted pyramidal cells are prone to exhibiting burst firing on receipt of coincident basal and apical dendritic inputs. These inputs carry different information, with basal inputs coming from feedforward sensory pa...
Nearly Optimal Learning Using Sparse Deep ReLU Networks in Regularized Empirical Risk Minimization With Lipschitz Loss [0.03%]
具有Lipschitz损失的正则化经验风险最小化中使用稀疏深度ReLU网络进行近似最优学习
Ke Huang,Mingming Liu,Shujie Ma
Ke Huang
We propose a sparse deep ReLU network (SDRN) estimator of the regression function obtained from regularized empirical risk minimization with a Lipschitz loss function. Our framework can be applied to a variety of regression and classificati...
Raffaele Marino,Lorenzo Buffoni,Lorenzo Chicchi et al.
Raffaele Marino et al.
The Wilson-Cowan model for metapopulation, a neural mass network model, treats different subcortical regions of the brain as connected nodes, with connections representing various types of structural, functional, or effective neuronal conne...
Xi Zhang,Akshay Aravamudan,Georgios C Anagnostopoulos
Xi Zhang
Temporal point processes are essential for modeling event dynamics in fields such as neuroscience and social media. The time rescaling theorem is commonly used to assess model fit by transforming a point process into a homogeneous Poisson p...