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CogNet

MIT Press CogNet is the essential research tool for scholars in the brain and cognitive sciences. Visit the CogNet information page for more information.

The Leaky Integrate-and-Fire Neuron Is a Change-Point Detector for Compound Poisson Processes

Shivaram Mani; Paul Hurley; André van Schaik; Travis Monk

https://doi.org/10.1162/neco_a_01750

A Generalized Time Rescaling Theorem for Temporal Point Processes

Xi Zhang; Akshay Aravamudan; Georgios C. Anagnostopoulos

https://doi.org/10.1162/neco_a_01745

Distributed Synaptic Connection Strength Changes Dynamics in a Population Firing Rate Model in Response to Continuous External Stimuli

Masato Sugino; Mai Tanaka; Kenta Shimba; Kiyoshi Kotani; Yasuhiko Jimbo

https://doi.org/10.1162/neco_a_01749

Adding Space to Random Networks of Spiking Neurons: A Method Based on Scaling the Network Size

Cecilia Romaro; Jose Roberto Castilho Piqueira; A. C. Roque

https://doi.org/10.1162/neco_a_01747

Multilevel Data Representation for Training Deep Helmholtz Machines

Jose Miguel Ramos; Luis Sa-Couto; Andreas Wichert

https://doi.org/10.1162/neco_a_01748

Reformulation of RBM to Unify Linear and Nonlinear Dimensionality Reduction

Jiangsheng You; Chun-Yen Liu

https://doi.org/10.1162/neco_a_01751

Elucidating the Theoretical Underpinnings of Surrogate Gradient Learning in Spiking Neural Networks

Julia Gygax; Friedemann Zenke

https://doi.org/10.1162/neco_a_01752

Neural Code Translation With LIF Neuron Microcircuits

Ville Karlsson; Joni Kämäräinen

https://doi.org/10.1162/neco_a_01754

Dynamics of Continuous Attractor Neural Networks With Spike Frequency Adaptation

Yujun Li; Tianhao Chu; Si Wu

https://doi.org/10.1162/neco_a_01757

Dynamics and Bifurcation Structure of a Mean-Field Model of Adaptive Exponential Integrate-and-Fire Networks

Lionel Kusch; Damien Depannemaecker; Alain Destexhe; Viktor Jirsa

https://doi.org/10.1162/neco_a_01758

Memory States From Almost Nothing: Representing and Computing in a Nonassociative Algebra

Stefan Reimann

https://doi.org/10.1162/neco_a_01755

Low-Rank, High-Order Tensor Completion via t - Product-Induced Tucker (tTucker) Decomposition

Yaodong Li; Jun Tan; Peilin Yang; Guoxu Zhou; Qibin Zhao

https://doi.org/10.1162/neco_a_01756

Context-Sensitive Processing in a Model Neocortical Pyramidal Cell With Two Sites of Input Integration

Bruce P. Graham; Jim W. Kay; William A. Phillips

https://doi.org/10.1162/neco_a_01739

Nearly Optimal Learning Using Sparse Deep ReLU Networks in Regularized Empirical Risk Minimization With Lipschitz Loss

Ke Huang; Mingming Liu; Shujie Ma

https://doi.org/10.1162/neco_a_01742

Spiking Neuron-Astrocyte Networks for Image Recognition

Jhunlyn Lorenzo; Juan-Antonio Rico-Gallego; Stéphane Binczak; Sabir Jacquir

https://doi.org/10.1162/neco_a_01740

Learning in Wilson-Cowan Model for Metapopulation

Raffaele Marino; Lorenzo Buffoni; Lorenzo Chicchi; Francesca Di Patti; Diego Febbe; Lorenzo Giambagli; Duccio Fanelli

https://doi.org/10.1162/neco_a_01744

Knowledge as a Breaking of Ergodicity

Yang He; Vassiliy Lubchenko

https://doi.org/10.1162/neco_a_01741

Active Inference and Intentional Behavior

Karl J. Friston; Tommaso Salvatori; Takuya Isomura; Alexander Tschantz; Alex Kiefer; Tim Verbelen; Magnus Koudahl; Aswin Paul; Thomas Parr; Adeel Razi; Brett J. Kagan; Christopher L. Buckley; Maxwell J. D. Ramstead

https://doi.org/10.1162/neco_a_01738

Enhanced EEG Forecasting: A Probabilistic Deep Learning Approach

Hanna Pankka; Jaakko Lehtinen; Risto J. Ilmoniemi; Timo Roine

https://doi.org/10.1162/neco_a_01743

Gradual Domain Adaptation via Normalizing Flows

Shogo Sagawa; Hideitsu Hino

https://doi.org/10.1162/neco_a_01734