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- Brown UniversityUS17 papers
- Allen InstituteUS16 papers
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- Institut du CerveauFR8 papers
- Sorbonne UniversitéFR7 papers
- Université Fédérale de Toulouse Midi-PyrénéesFR7 papers
- Chongqing Normal UniversityCN6 papers
- East China Normal UniversityCN6 papers
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6 papers · 1 filter
Stability of stochastic finite-size spiking-neuron networks: Comparing mean-field, 1-loop correction and quasi-renewal approximations
Dmitrii Todorov, Wilson Truccolo
We examine the stability and qualitative dynamics of stochastic neuronal networks specified as multivariate nonlinear Hawkes processes and related point-process generalized linear…
A coupled autoencoder approach for multi-modal analysis of cell types
Rohan Gala, Nathan Gouwens, Zizhen Yao +6
Recent developments in high throughput profiling of individual neurons have spurred data driven exploration of the idea that there exist natural groupings of neurons referred to as…
Library network, a possible path to explainable neural networks
Jung Hoon Lee
Deep neural networks (DNNs) may outperform human brains in complex tasks, but the lack of transparency in their decision-making processes makes us question whether we could fully t…
Simulation of virtual cohorts increases predictive accuracy of cognitive decline in MCI subjects
Igor Koval, Stéphanie Allassonnière, Stanley Durrleman
The ability to predict the progression of biomarkers, notably in NDD, is limited by the size of the longitudinal data sets, in terms of number of patients, number of visits per pat…
Complementary reinforcement learning towards explainable agents
Jung Hoon Lee
Reinforcement learning (RL) algorithms allow agents to learn skills and strategies to perform complex tasks without detailed instructions or expensive labelled training examples. T…
Interpretable machine learning: definitions, methods, and applications
W. James Murdoch, Chandan Singh, Karl Kumbier +2
Machine-learning models have demonstrated great success in learning complex patterns that enable them to make predictions about unobserved data. In addition to using models for pre…