Feng Zhang

PhD student · Tianjin University

School of Electrical and Information Engineering

Research interests

  • Computational neuroscience
  • Neural dynamics
  • Multiscale modeling
  • EEG analysis
  • Mechanistic model parameter identification
Portrait of Feng Zhang

To experience as much of the world’s richness and diversity as I can in my lifetime.

Education

2023.09 — Present
Tianjin UniversitySchool of Electrical and Information Engineering
PhD student in Control Science and Engineering
Research: Computational neuroscience
2020.09 — 2023.06
Tianjin UniversitySchool of Mathematics
MSc in Mathematics
2016.09 — 2020.06
Shanxi UniversitySchool of Mathematical Sciences
BSc in Mathematics and Applied Mathematics

Research

Understanding complex brain rhythms through multiscale neuronal dynamics.

My research uses mathematical modeling and dynamical systems analysis to connect microscopic, mesoscopic and macroscopic scales: from single-neuron dynamics, through neural network propagation, to EEG modeling. I study multi-timescale oscillations in pyramidal neurons and ephaptic coupling in neural networks, and explore how neural mass models relate to epileptiform EEG activity. Parameter identification is an ongoing exploratory direction for connecting biophysical mechanisms with observed brain signals.

News

  1. Submitted

    “Multi-timescale interactions underlying the emergence and evolution of seizure dynamics” was submitted to Chinese Physics B on 2026-08-16. It is currently under first-round review.

  2. Submitted

    “Computational modeling of the fast-to-slow EEG rhythm transition across seizure evolution and postictal recovery” was submitted to Journal of Neural Engineering on 2026-07-28. It is currently under second-round review.

  3. Publication year

    Our paper on epileptiform fast–slow compound waves is available in Neurocomputing. Assigned to volume 697, article 134260. Read paper

  4. Publication year

    Our dual-component epileptic EEG model paper was published in EMBC 2026, pp. 8134–8140. Indexing is pending.

  5. Proceedings publication

    Our paper on electric-field-driven dendritic–somatic interactions appears in the IEEE NER 2025 proceedings. Read paper

  6. Proceedings publication

    Our paper on planar epileptic waves under ephaptic coupling for closed-loop stimulation appears in the IEEE NER 2025 proceedings. Read paper

  7. Published online

    Our three-compartment model study of multi-timescale compound oscillations was published online in Nonlinear Dynamics, following acceptance on 1 September 2024. It appears in the January 2025 issue. Read paper

  8. Proceedings publication

    Our paper on a time–frequency multi-frame network for epilepsy prediction appears in the CCC 2024 proceedings. Read paper

  9. Journal issue

    Our review of neural-network methods for seizure prediction was published in Journal of Frontiers of Computer Science & Technology, 17(11), 2543–2556. Read paper

  10. Published

    Our multi-frame seizure-prediction paper was published in Frontiers in Computational Neuroscience, following acceptance on 20 October 2022. Liangfu Lu and Feng Zhang share first authorship. Read paper

Publications

Journal articles

2026
JOURNAL

Epileptiform fast-slow compound waves in neural networks: Mechanisms of dendritic oscillations and non-synaptic recruitment

Feng Zhang, Meili Lu, Xile Wei

Neurocomputing 697, 134260 (2026)

Developed a biophysical hippocampal network model linking dendritic oscillations and ephaptic coupling to fast–slow epileptiform waves and non-synaptic recruitment.

Read paperCode
2025
JOURNAL

Multi-timescale compound oscillations in pyramidal neurons: insights from a three-compartment model

Feng Zhang, Meili Lu, Xile Wei

Nonlinear Dynamics 113(2), 1685–1712 (2025)

Developed a three-compartment pyramidal-neuron model and used multi-timescale and bifurcation analysis to explain how dendritic Ca²⁺ and NMDA dynamics shape compound oscillations.

Read paperCode
2023
JOURNAL

Review of Application of Neural Networks in Epileptic Seizure Prediction

Honghong Huang, Feng Zhang, Liangfu Lu, Xiaopeng Si

Journal of Frontiers of Computer Science & Technology 17(11), 2543–2556 (2023)

Read paper
2022
JOURNAL

A multi-frame network model for predicting seizure based on sEEG and iEEG data

Liangfu Lu, Feng Zhang, Yubo Wu, Songnan Ma, Xin Zhang, Guangjian Ni

Frontiers in Computational Neuroscience 16, 1059565 (2022)

Joint first authors: Liangfu Lu and Feng Zhang.

Read paper

Conference papers

2026
CONFERENCE

A Dual-Component Epileptic EEG Model Coupling Fast Ictal Activity and Spike–Wave Discharges

Feng Zhang, Meili Lu, Xile Wei

EMBC 2026, 8134–8140 (2026)

Published; indexing pending.

2025
CONFERENCE

Computational Modeling of Dendritic-Somatic Interactions Driven by Electric Field Polarization in Pyramidal Neurons

Feng Zhang, Meili Lu, Xile Wei

2025 12th International IEEE/EMBS Conference on Neural Engineering (NER) 228–233 (2025)

Read paper
2025
CONFERENCE

Computational Modeling of Planar Epileptic Waves Under Ephaptic Coupling for Closed-Loop Stimulation

Feng Zhang, Meili Lu, Xile Wei

2025 12th International IEEE/EMBS Conference on Neural Engineering (NER) 393–398 (2025)

Read paper
2024
CONFERENCE

A multi-frame network model based on time-frequency analysis for epilepsy prediction

Feng Zhang, Xile Wei, Liangfu Lu

2024 43rd Chinese Control Conference (CCC) 8262–8267 (2024)

Read paper

Manuscripts under review

Under review (first round)

Multi-timescale interactions underlying the emergence and evolution of seizure dynamics

Feng Zhang, Meili Lu, Xile Wei

Chinese Physics B · Submitted 2026-08-16

Under review (second round)

Computational modeling of the fast-to-slow EEG rhythm transition across seizure evolution and postictal recovery

Feng Zhang, Meili Lu, Xile Wei

Journal of Neural Engineering · Submitted 2026-07-28

A dual-component dynamical model qualitatively reproduces the progression from fast seizure activity to slower postictal EEG rhythms. It connects recurrent seizure cycles with rhythm evolution and explores how network recruitment shapes recovery dynamics.

Technical skills

  • MATLAB — numerical simulation of dynamical models
  • Python — numerical simulation and deep-learning training
  • XPPAUT — bifurcation analysis
  • LaTeX — scientific writing and equation typesetting
  • Codex / Claude Code — assisted coding and debugging
  • EEG / EDF data processing

Visitor statistics

Statistics are available after setup.