Eray Erturk
University of Southern California
H-index: 2
North America-United States
About Eray Erturk
Eray Erturk, With an exceptional h-index of 2 and a recent h-index of 2 (since 2020), a distinguished researcher at University of Southern California, specializes in the field of Neuroscience, Neural Engineering, Signal Processing, Statistical Signal Processing, Machine Learning.
His recent articles reflect a diverse array of research interests and contributions to the field:
Dynamical flexible inference of nonlinear latent factors and structures in neural population activity
Dynamical flexible inference of nonlinear latent structures in neural population activity
Power allocation and temporal fair user group scheduling for downlink NOMA
Eray Erturk Information
University | University of Southern California |
---|---|
Position | Ph.D. student at NSEIP Lab |
Citations(all) | 10 |
Citations(since 2020) | 10 |
Cited By | 0 |
hIndex(all) | 2 |
hIndex(since 2020) | 2 |
i10Index(all) | 0 |
i10Index(since 2020) | 0 |
University Profile Page | University of Southern California |
Eray Erturk Skills & Research Interests
Neuroscience
Neural Engineering
Signal Processing
Statistical Signal Processing
Machine Learning
Top articles of Eray Erturk
Dynamical flexible inference of nonlinear latent factors and structures in neural population activity
Authors
Hamidreza Abbaspourazad,Eray Erturk,Bijan Pesaran,Maryam M Shanechi
Journal
Nature Biomedical Engineering
Published Date
2024/1
Modelling the spatiotemporal dynamics in the activity of neural populations while also enabling their flexible inference is hindered by the complexity and noisiness of neural observations. Here we show that the lower-dimensional nonlinear latent factors and latent structures can be computationally modelled in a manner that allows for flexible inference causally, non-causally and in the presence of missing neural observations. To enable flexible inference, we developed a neural network that separates the model into jointly trained manifold and dynamic latent factors such that nonlinearity is captured through the manifold factors and the dynamics can be modelled in tractable linear form on this nonlinear manifold. We show that the model, which we named ‘DFINE’ (for ‘dynamical flexible inference for nonlinear embeddings’) achieves flexible inference in simulations of nonlinear dynamics and across neural datasets …
Dynamical flexible inference of nonlinear latent structures in neural population activity
Authors
Hamidreza Abbaspourazad,Eray Erturk,Bijan Pesaran,Maryam M Shanechi
Journal
bioRxiv
Published Date
2023
Inferring complex spatiotemporal dynamics in neural population activity is critical for investigating neural mechanisms and developing neurotechnology. These activity patterns are noisy observations of lower-dimensional latent factors and their nonlinear dynamical structure. A major unaddressed challenge is to model this nonlinear structure, but in a manner that allows for flexible inference, whether causally, non-causally, or in the presence of missing neural observations. We address this challenge by developing DFINE, a new neural network that separates the model into dynamic and manifold latent factors, such that the dynamics can be modeled in tractable form. We show that DFINE achieves flexible nonlinear inference across diverse behaviors and brain regions. Further, despite enabling flexible inference unlike prior neural network models of population activity, DFINE also better predicts the behavior and neural activity, and better captures the latent neural manifold structure. DFINE can both enhance future neurotechnology and facilitate investigations across diverse domains of neuroscience.
Power allocation and temporal fair user group scheduling for downlink NOMA
Authors
Eray Erturk,Ozlem Yildiz,Shahram Shahsavari,Nail Akar
Journal
Telecommunication Systems
Published Date
2021/8
Non-Orthogonal Multiple Access (NOMA) has been proposed as a new radio access technique for cellular networks as an alternative to OMA (Orthogonal Multiple Access) in which the users of a group (pairs or triples of users in a group are considered in this paper) are allowed to use the wireless channel simultaneously. In this paper, for downlink single-input single-output SISO-NOMA, a heuristic power allocation algorithm within a group is first proposed which attempts to ensure that the users of a group benefit from simultaneous transmission equally in terms of achievable throughput. Moreover, a user group scheduling algorithm is proposed for downlink NOMA systems by which a user group is to be dynamically selected for transmission while satisfying long term temporal fairness among the individual contending users. The effectiveness of the proposed power allocation method along with the temporal …
Eray Erturk FAQs
What is Eray Erturk's h-index at University of Southern California?
The h-index of Eray Erturk has been 2 since 2020 and 2 in total.
What are Eray Erturk's top articles?
The articles with the titles of
Dynamical flexible inference of nonlinear latent factors and structures in neural population activity
Dynamical flexible inference of nonlinear latent structures in neural population activity
Power allocation and temporal fair user group scheduling for downlink NOMA
are the top articles of Eray Erturk at University of Southern California.
What are Eray Erturk's research interests?
The research interests of Eray Erturk are: Neuroscience, Neural Engineering, Signal Processing, Statistical Signal Processing, Machine Learning
What is Eray Erturk's total number of citations?
Eray Erturk has 10 citations in total.