paper.bib

% ============================================================================
% CANNs Library Bibliography
% ============================================================================
% This file contains references for the CANNs documentation.
% Format: BibTeX
% Citation style: numeric ([1], [2], [3])
% Shared by both English and Chinese documentation
%
% File Structure:
% 1. Foundational CANN Theory
% 2. Neural Cell Types and Spatial Coding
% 3. Theta Rhythms and Sequences
% 4. Learning Rules and Plasticity
% 5. Path Integration and Navigation
% 6. Computational Frameworks and Tools
% 7. This Library
%
% Citation key format: authorYEARkeyword
% Examples: wu2008dynamics, amari1977dynamics
% ============================================================================


% ============================================================================
% 1. Foundational CANN Theory
% ============================================================================
% These papers establish the theoretical foundation of Continuous Attractor Neural Networks

% Amari (1977) - Foundational work on CANN theory
% Introduced dynamics theory for lateral-inhibition type neural fields
% Document locations: Core concepts, design philosophy, multiple tutorials
% Citation format: :cite:`amari1977dynamics`
@article{amari1977dynamics,
  title={Dynamics of pattern formation in lateral-inhibition type neural fields},
  author={Amari, Shun-ichi},
  journal={Biological cybernetics},
  volume={27},
  number={2},
  pages={77--87},
  year={1977},
  publisher={Springer},
  doi={10.1007/BF00337259}
}

% Wu et al. (2008) - Dynamics and computation of continuous attractors
% Systematic analysis of CANN dynamics and computational capabilities
% One of the most influential papers on continuous attractor networks
% Document locations: 0_why_canns, core concepts, Tutorial 1-7
% Citation format: :cite:`wu2008dynamics`
@article{wu2008dynamics,
  title={Dynamics and computation of continuous attractors},
  author={Wu, Si and Hamaguchi, Kosuke and Amari, Shun-ichi},
  journal={Neural computation},
  volume={20},
  number={4},
  pages={994--1025},
  year={2008},
  publisher={MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~…},
  doi={10.1162/neco.2008.10-06-378}
}

% Fung et al. (2010) - Moving bump dynamics in continuous manifolds
% Comprehensive study of tracking dynamics in continuous attractor neural networks
% Analyzes how activity bumps move and track external inputs
% Document locations: Core concepts, modeling tutorials
% Citation format: :cite:`fung2010moving`
@article{fung2010moving,
  title={A moving bump in a continuous manifold: a comprehensive study of the tracking dynamics of continuous attractor neural networks},
  author={Fung, CC Alan and Wong, KY Michael and Wu, Si},
  journal={Neural Computation},
  volume={22},
  number={3},
  pages={752--792},
  year={2010},
  publisher={MIT Press},
  doi={10.1162/neco.2009.07-08-824}
}


% Wu et al. (2016) - CANN review paper
% Comprehensive review of CANN as a canonical model for neural information representation
% Excellent overview paper for understanding CANNs' role in neuroscience
% Document locations: 0_why_canns, design philosophy
% Citation format: :cite:`wu2016continuous`
@article{wu2016continuous,
  title={Continuous attractor neural networks: candidate of a canonical model for neural information representation},
  author={Wu, Si and Wong, KY Michael and Fung, CC Alan and Mi, Yuanyuan and Zhang, Wenhao},
  journal={F1000Research},
  volume={5},
  pages={F1000--Faculty},
  year={2016},
  doi={10.12688/f1000research.7387.1}
}


% ============================================================================
% 2. Neural Cell Types and Spatial Coding
% ============================================================================
% These groundbreaking works discovered spatial coding cells in the brain

% O'Keefe & Dostrovsky (1971) - Discovery of place cells
% First recording of location-specific neurons in freely moving rat hippocampus
% Foundational work that led to the Nobel Prize in Physiology or Medicine 2014
% Document locations: Tutorial 5, 6, 7; core concepts
% Citation format: :cite:`o1971hippocampus`
@article{o1971hippocampus,
  title={The hippocampus as a spatial map: preliminary evidence from unit activity in the freely-moving rat.},
  author={O'Keefe, John and Dostrovsky, Jonathan},
  journal={Brain research},
  year={1971},
  publisher={Elsevier Science},
  doi={10.1016/0006-8993(71)90358-1}
}

% Taube et al. (1990) - Discovery of head direction cells
% Discovery of direction-encoding neurons in the postsubiculum
% Essential for understanding directional navigation in the brain
% Document locations: Tutorial 5, 6; model collections
% Citation format: :cite:`taube1990head`
@article{taube1990head,
  title={Head-direction cells recorded from the postsubiculum in freely moving rats. I. Description and quantitative analysis},
  author={Taube, Jeffrey S and Muller, Robert U and Ranck, James B},
  journal={Journal of Neuroscience},
  volume={10},
  number={2},
  pages={420--435},
  year={1990},
  publisher={Society for Neuroscience},
  doi={10.1523/JNEUROSCI.10-02-00420.1990}
}

% Hafting et al. (2005) - Discovery of grid cells (Nobel Prize work)
% Discovery of grid cells with periodic hexagonal spatial responses in entorhinal cortex
% Led to the 2014 Nobel Prize in Physiology or Medicine (shared with O'Keefe)
% Document locations: Tutorial 5, 6, 7; core concepts; design philosophy
% Citation format: :cite:`hafting2005microstructure`
@article{hafting2005microstructure,
  title={Microstructure of a spatial map in the entorhinal cortex},
  author={Hafting, Torkel and Fyhn, Marianne and Molden, Sturla and Moser, May-Britt and Moser, Edvard I},
  journal={Nature},
  volume={436},
  number={7052},
  pages={801--806},
  year={2005},
  publisher={Nature Publishing Group UK London},
  doi={10.1038/nature03721}
}


% ============================================================================
% 3. Theta Rhythms and Sequences
% ============================================================================
% These papers study the modulation of theta rhythms on neural coding and sequence generation

% Wang et al. (2015) - Theta sequences in hippocampal firing fields
% Demonstrated that theta sequences are essential for internally generated hippocampal firing fields
% Important experimental evidence for theta-modulated neural dynamics
% Document locations: Tutorial 6, 7
% Citation format: :cite:`wang2015theta`
@article{wang2015theta,
  title={Theta sequences are essential for internally generated hippocampal firing fields},
  author={Wang, Yingxue and Romani, Sandro and Lustig, Brian and Leonardo, Anthony and Pastalkova, Eva},
  journal={Nature neuroscience},
  volume={18},
  number={2},
  pages={282--288},
  year={2015},
  publisher={Nature Publishing Group US New York},
  doi={10.1038/nn.3904}
}

% Ji et al. (2025) - Phase precession in theta-modulated head direction cells
% Studies the relationship between phase precession and turning angle
% Shows how theta modulation affects head direction cell firing
% Document location: Tutorial 6 (06_theta_sweep_hd_grid.ipynb, Lines 66-67)
% Citation format: :cite:`ji2025phase`
@article{ji2025phase,
  title={Phase Precession Relative to Turning Angle in Theta-Modulated Head Direction Cells},
  author={Ji, Zilong and Lomi, Eleonora and Jeffery, Kate and Mitchell, Anna S and Burgess, Neil},
  journal={Hippocampus},
  volume={35},
  number={2},
  pages={e70008},
  year={2025},
  publisher={Wiley Online Library},
  doi={10.1002/hipo.70008}
}


% Ji et al. (2025) - Systems model of alternating theta sweeps via firing rate adaptation
% Proposes a computational model for alternating theta sweeps using SFA
% Demonstrates how firing rate adaptation generates theta sweep patterns
% Document location: Tutorial 6 (06_theta_sweep_hd_grid.ipynb)
% Citation format: :cite:`ji2025systems`
@article{ji2025systems,
  title={A systems model of alternating theta sweeps via firing rate adaptation},
  author={Ji, Zilong and Chu, Tianhao and Wu, Si and Burgess, Neil},
  journal={Current Biology},
  volume={35},
  number={4},
  pages={709--722},
  year={2025},
  publisher={Elsevier},
  doi={10.1016/j.cub.2024.08.059}
}


% Chu et al. (2024) - Firing rate adaptation in place cells
% Shows how firing rate adaptation enables theta sweeps, phase precession, and procession
% Key paper connecting SFA to hippocampal spatial coding phenomena
% Document location: Tutorial 7 (07_theta_sweep_place_cell.ipynb)
% Citation format: :cite:`chu2024firing`
@article{chu2024firing,
  title={Firing rate adaptation affords place cell theta sweeps, phase precession, and procession},
  author={Chu, Tianhao and Ji, Zilong and Zuo, Junfeng and Mi, Yuanyuan and Zhang, Wen-hao and Huang, Tiejun and Bush, Daniel and Burgess, Neil and Wu, Si},
  journal={Elife},
  volume={12},
  pages={RP87055},
  year={2024},
  publisher={eLife Sciences Publications Limited},
  doi={10.7554/eLife.87055.4}
}



% ============================================================================
% 4. Learning Rules and Plasticity
% ============================================================================
% These classic papers define fundamental rules for neural network learning

% Hebb (1949/2005) - Original theory of Hebbian learning
% Proposed the famous "Neurons that fire together, wire together" principle
% This is the 2005 reprint of the 1949 classic work
% Document locations: Quick start 5, core concepts 5, Tutorial 3 (brain_inspired)
% Citation format: :cite:`hebb2005organization`
@book{hebb2005organization,
  title={The organization of behavior: A neuropsychological theory},
  author={Hebb, Donald Olding},
  year={2005},
  publisher={Psychology press},
  doi={10.1001/jama.1950.02910470083028}
}

% Amari (1977) - Neural theory of association and concept formation
% Foundational work on how neural networks form associations and concepts
% Important for understanding memory and learning in neural networks
% Document location: Hopfield model implementation (hopfield.py)
% Citation format: :cite:`amari1977neural`
@article{amari1977neural,
  title={Neural theory of association and concept-formation},
  author={Amari, S-I},
  journal={Biological cybernetics},
  volume={26},
  number={3},
  pages={175--185},
  year={1977},
  publisher={Springer},
  doi={10.1007/BF00365229}
}

% Hopfield (1982) - Hopfield networks and associative memory
% Pioneering application of collective computational abilities of physical systems to neural networks
% Foundation for understanding attractor networks and associative memory
% Document location: Tutorial 3 (pattern_storage_recall.ipynb)
% Documentation mentions "Amari-Hopfield networks"
% Citation format: :cite:`hopfield1982neural`
@article{hopfield1982neural,
  title={Neural networks and physical systems with emergent collective computational abilities.},
  author={Hopfield, John J},
  journal={Proceedings of the national academy of sciences},
  volume={79},
  number={8},
  pages={2554--2558},
  year={1982},
  doi={10.1073/pnas.79.8.2554}
}

% Bi & Poo (1998) - Experimental validation of STDP (Spike-Timing Dependent Plasticity)
% Demonstrated that synaptic modifications depend on precise spike timing
% Key experimental evidence for temporal coding in synaptic plasticity
% Document location: Core concepts 5 (brain_inspired_training.rst)
% Citation format: :cite:`bi1998synaptic`
@article{bi1998synaptic,
  title={Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type},
  author={Bi, Guo-qiang and Poo, Mu-ming},
  journal={Journal of neuroscience},
  volume={18},
  number={24},
  pages={10464--10472},
  year={1998},
  publisher={Society for Neuroscience},
  doi={10.1523/JNEUROSCI.18-24-10464.1998}
}

% Bienenstock et al. (1982) - BCM learning rule
% Proposed theory for development of neuron selectivity in visual cortex
% Explains how neurons develop orientation selectivity through experience
% Document location: Core concepts 5 (brain_inspired_training.rst)
% Citation format: :cite:`bienenstock1982theory`
@article{bienenstock1982theory,
  title={Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex},
  author={Bienenstock, Elie L and Cooper, Leon N and Munro, Paul W},
  journal={Journal of Neuroscience},
  volume={2},
  number={1},
  pages={32--48},
  year={1982},
  publisher={Society for Neuroscience},
  doi={10.1523/JNEUROSCI.02-01-00032.1982}
}

% ---- Spike Frequency Adaptation (SFA) References ----
% These papers study how SFA enables anticipative tracking and theta dynamics

% Mi et al. (2014) - SFA implements anticipative tracking in CANNs
% First paper showing how spike frequency adaptation enables prediction in CANNs
% Demonstrates that SFA allows the network to anticipate moving inputs
% Document location: A-CANN model in Tutorial 6, 7
% Citation format: :cite:`mi2014spike`
@article{mi2014spike,
  title={Spike frequency adaptation implements anticipative tracking in continuous attractor neural networks},
  author={Mi, Yuanyuan and Fung, CC and Wong, KY and Wu, Si},
  journal={Advances in neural information processing systems},
  volume={27},
  year={2014},
}

% Li et al. (2025) - Dynamics of CANNs with SFA
% Recent comprehensive analysis of how SFA affects CANN dynamics
% Provides theoretical framework for understanding SFA in continuous attractors
% Document location: A-CANN model in Tutorial 6, 7
% Citation format: :cite:`li2025dynamics`
@article{li2025dynamics,
  title={Dynamics of Continuous Attractor Neural Networks With Spike Frequency Adaptation},
  author={Li, Yujun and Chu, Tianhao and Wu, Si},
  journal={Neural Computation},
  volume={37},
  number={6},
  pages={1057--1101},
  year={2025},
  publisher={MIT Press 255 Main Street, 9th Floor, Cambridge, Massachusetts 02142, USA~…},
  doi={10.1162/neco_a_01757}
}


% Oja (1982) - Oja's learning rule for principal component analysis
% Introduces simplified neuron model that extracts the first principal component
% Foundation for unsupervised learning in neural networks
% Document locations: Referenced in trainer implementations
% Citation format: :cite:`oja1982simplified`
@article{oja1982simplified,
  title={Simplified neuron model as a principal component analyzer},
  author={Oja, Erkki},
  journal={Journal of mathematical biology},
  volume={15},
  number={3},
  pages={267--273},
  year={1982},
  publisher={Springer},
  doi={10.1007/BF00275687}
}

% Sanger (1989) - Sanger's learning rule for multiple principal components
% Extends Oja's rule to extract multiple principal components sequentially
% Important for hierarchical unsupervised learning in neural networks
% Document locations: Referenced in trainer implementations
% Citation format: :cite:`network1989optimal`
@article{sanger1989optimal,
  title={Optimal unsupervised learning in a single-layer linear feedforward neural network},
  author={Sanger, Terence D},
  journal={Neural networks},
  volume={2},
  number={6},
  pages={459--473},
  year={1989},
  publisher={Elsevier},
  doi={10.1016/0893-6080(89)90044-0}
}


% ============================================================================
% 5. Path Integration and Navigation
% ============================================================================
% These papers study how the brain achieves spatial navigation through path integration

% McNaughton et al. (2006) - Path integration and neural basis of the 'cognitive map'
% Comprehensive review of path integration mechanisms in the brain
% Connects grid cells, place cells, and head direction cells to navigation
% Document location: Tutorial 5 (hierarchical_network.ipynb)
% Citation format: :cite:`mcnaughton2006path`
@article{mcnaughton2006path,
  title={Path integration and the neural basis of the'cognitive map'},
  author={McNaughton, Bruce L and Battaglia, Francesco P and Jensen, Ole and Moser, Edvard I and Moser, May-Britt},
  journal={Nature Reviews Neuroscience},
  volume={7},
  number={8},
  pages={663--678},
  year={2006},
  publisher={Nature Publishing Group UK London},
  doi={10.1038/nrn1932}
}

% Samsonovich & McNaughton (1997) - CANN model for path integration and cognitive mapping
% Early influential computational model of path integration using continuous attractors
% Demonstrates how grid-like representations can emerge from path integration dynamics
% Document locations: Tutorial 5, 6, 7 (hierarchical_network.ipynb, theta sweep tutorials)
% Citation format: :cite:`samsonovich1997path`
@article{samsonovich1997path,
  title={Path integration and cognitive mapping in a continuous attractor neural network model},
  author={Samsonovich, Alexei and McNaughton, Bruce L},
  journal={Journal of Neuroscience},
  volume={17},
  number={15},
  pages={5900--5920},
  year={1997},
  publisher={Society for Neuroscience},
  doi={10.1523/JNEUROSCI.17-15-05900.1997}
}

% Etienne & Jeffery (2004) - Path integration in mammals review
% Comprehensive review of path integration mechanisms across mammalian species
% Discusses behavioral evidence and neural substrates for self-motion integration
% Document locations: Background reference for navigation tasks and path integration concepts
% Citation format: :cite:`etienne2004path`
@article{etienne2004path,
  title={Path integration in mammals},
  author={Etienne, Ariane S and Jeffery, Kathryn J},
  journal={Hippocampus},
  volume={14},
  number={2},
  pages={180--192},
  year={2004},
  publisher={Wiley Online Library},
  doi={10.1002/hipo.10173}
}


% ============================================================================
% 6. Computational Frameworks and Tools
% ============================================================================
% Computational frameworks and tools used by this library

% ---- BrainPy Framework ----
% Wang et al. (2023) - BrainPy framework paper
% Official publication describing BrainPy as a flexible brain dynamics programming framework
% Core dependency of this library, provides JAX-based high-performance neural simulation
% Document location: Mentioned frequently throughout documentation
% Citation format: :cite:`wang2023brainpy`

@article {wang2023brainpy,
    article_type = {journal},
    title = {BrainPy, a flexible, integrative, efficient, and extensible framework for general-purpose brain dynamics programming},
    author = {Wang, Chaoming and Zhang, Tianqiu and Chen, Xiaoyu and He, Sichao and Li, Shangyang and Wu, Si},
    editor = {Stimberg, Marcel},
    volume = 12,
    year = 2023,
    month = {dec},
    pub_date = {2023-12-22},
    pages = {e86365},
    citation = {eLife 2023;12:e86365},
    doi = {10.7554/eLife.86365},
    url = {https://doi.org/10.7554/eLife.86365},
    abstract = {Elucidating the intricate neural mechanisms underlying brain functions requires integrative brain dynamics modeling. To facilitate this process, it is crucial to develop a general-purpose programming framework that allows users to freely define neural models across multiple scales, efficiently simulate, train, and analyze model dynamics, and conveniently incorporate new modeling approaches. In response to this need, we present BrainPy. BrainPy leverages the advanced just-in-time (JIT) compilation capabilities of JAX and XLA to provide a powerful infrastructure tailored for brain dynamics programming. It offers an integrated platform for building, simulating, training, and analyzing brain dynamics models. Models defined in BrainPy can be JIT compiled into binary instructions for various devices, including Central Processing Unit (CPU), Graphics Processing Unit (GPU), and Tensor Processing Unit (TPU), which ensures high running performance comparable to native C or CUDA. Additionally, BrainPy features an extensible architecture that allows for easy expansion of new infrastructure, utilities, and machine-learning approaches. This flexibility enables researchers to incorporate cutting-edge techniques and adapt the framework to their specific needs},
    journal = {eLife},
    issn = {2050-084X},
    publisher = {eLife Sciences Publications, Ltd},
}

@article{wang2025integrating,
  title={Integrating physical units into high-performance AI-driven scientific computing},
  author={Wang, Chaoming and He, Sichao and Luo, Shouwei and Huan, Yuxiang and Wu, Si},
  journal={Nature Communications},
  volume={16},
  number={1},
  pages={3609},
  year={2025},
  publisher={Nature Publishing Group UK London}
}

% JAX - Google's composable transformations framework
% Provides high-performance automatic differentiation and JIT compilation
% Enables GPU/TPU acceleration for neural network simulations
% Document location: Mentioned throughout documentation for JAX compilation and GPU/TPU support
% Citation format: :cite:`jax2018github`
@software{jax2018github,
  author = {James Bradbury and Roy Frostig and Peter Hawkins and Matthew James Johnson and Chris Leary and Dougal Maclaurin and George Necula and Adam Paszke and Jake Vander{P}las and Skye Wanderman-{M}ilne and Qiao Zhang},
  title = {{JAX}: composable transformations of {P}ython+{N}um{P}y programs},
  url = {http://github.com/jax-ml/jax},
  version = {0.3.13},
  year = {2018},
}

% ---- Topological Data Analysis (TDA) ----
% These papers provide foundations for topological analysis of neural activity

% Carlsson (2009) - Topology and data foundations
% Seminal paper introducing topological data analysis to a broad audience
% Provides accessible introduction to persistent homology and its applications
% Document location: Core concepts 4 (analysis_methods.rst, Section 4: TDA)
% Citation format: :cite:`carlsson2009topology`
@article{carlsson2009topology,
  title={Topology and data},
  author={Carlsson, Gunnar},
  journal={Bulletin of the American Mathematical Society},
  volume={46},
  number={2},
  pages={255--308},
  year={2009},
  doi={10.1090/S0273-0979-09-01249-X}
}

% Edelsbrunner & Harer (2010) - Computational topology textbook
% Comprehensive technical reference for computational topology and persistent homology
% Standard textbook for understanding the algorithmic foundations of TDA
% Document location: Core concepts 4 (analysis_methods.rst, Section 4: TDA)
% Citation format: :cite:`edelsbrunner2010computational`
@book{edelsbrunner2010computational,
  title={Computational topology: an introduction},
  author={Edelsbrunner, Herbert and Harer, John},
  year={2010},
  publisher={American Mathematical Soc.}
}

% ---- RNN Dynamics Analysis ----
% These papers establish methods for analyzing recurrent neural network dynamics

% Sussillo & Barak (2013) - Low-dimensional dynamics in RNNs
% Pioneering work on fixed point analysis in recurrent neural networks
% Established methodology for understanding RNN computations through dynamical systems theory
% Document location: Core concepts 4 (analysis_methods.rst, Section 3: RNN Dynamics)
% Citation format: :cite:`sussillo2013opening`
@article{sussillo2013opening,
  title={Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks},
  author={Sussillo, David and Barak, Omri},
  journal={Neural computation},
  volume={25},
  number={3},
  pages={626--649},
  year={2013},
  publisher={MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~…},
  doi={10.1162/NECO_a_00409}
}

% Golub & Sussillo (2018) - FixedPointFinder tool
% Practical TensorFlow implementation for identifying fixed points in RNNs
% Widely used tool in computational neuroscience for analyzing network dynamics
% Document location: Core concepts 4 (analysis_methods.rst, Section 3: RNN Dynamics)
% Citation format: :cite:`golub2018fixedpointfinder`
@article{golub2018fixedpointfinder,
  title={FixedPointFinder: A Tensorflow toolbox for identifying and characterizing fixed points in recurrent neural networks},
  author={Golub, Matthew D and Sussillo, David},
  journal={Journal of open source software},
  volume={3},
  number={31},
  pages={1003},
  year={2018},
  doi={10.21105/joss.01003}
}


% ============================================================================
% 7. This Library
% ============================================================================
% Citation for the CANNs toolkit itself

% He et al. (2026) - CANNs toolkit
% arXiv preprint describing the CANNs library
% Primary citation for the CANNs toolkit in academic publications
% Citation format: :cite:`he2026canns`
@misc{he2026canns,
  author       = {He, Sichao and
                  Tuerhong, Aiersi and
                  She, Shangjun and
                  Chu, Tianhao and
                  Wu, Yuling and
                  Zuo, Junfeng and
                  Wu, Si},
  title        = {{CANNs}: A Toolkit for Research on Continuous Attractor Neural Networks},
  year         = {2026},
  eprint       = {2606.27783},
  archivePrefix = {arXiv},
  primaryClass = {q-bio.NC},
  doi          = {10.48550/arXiv.2606.27783},
  url          = {https://arxiv.org/abs/2606.27783}
}

% He et al. (2026) - CANNs toolkit
% Zenodo version-specific archive for v1.0.0
% Cite alongside the arXiv preprint when a specific version pin is required
% Citation format: :cite:`he_2026_canns`
@software{he_2026_canns,
  author       = {He, Sichao and
                  Tuerhong, Aiersi and
                  She, Shangjun and
                  Chu, Tianhao and
                  Wu, Yuling and
                  Zuo, Junfeng and
                  Wu, Si},
  title        = {{CANNs}: Continuous Attractor Neural Networks Toolkit},
  month        = feb,
  year         = {2026},
  publisher    = {Zenodo},
  version      = {v1.0.0},
  doi          = {10.5281/zenodo.18453893},
  url          = {https://doi.org/10.5281/zenodo.18453893}
}


% ============================================================================
% 8. Optional Enhancement Citations
% ============================================================================
% These citations would further enrich the documentation but are not critical

% O'Keefe & Recce (1993) - Theta phase precession discovery
% First systematic study of phase relationship between place cell firing and theta rhythm
% Foundational discovery for understanding temporal coding in hippocampus
% Document location: Could enhance theta sweep tutorials (Tutorial 6, 7)
% Citation format: :cite:`o1993phase`
@article{o1993phase,
  title={Phase relationship between hippocampal place units and the EEG theta rhythm},
  author={O'Keefe, John and Recce, Michael L},
  journal={Hippocampus},
  volume={3},
  number={3},
  pages={317--330},
  year={1993},
  publisher={Wiley Online Library},
  doi={10.1002/hipo.450030307}
}


% ============================================================================
% Guide for Adding New References
% ============================================================================
% 1. Use Google Scholar or DOI to find BibTeX format
% 2. Ensure citation key follows authorYEARkeyword format
% 3. Add appropriate category comments explaining the paper's contribution
% 4. In document location comments, specify which files use this reference
% 5. Provide citation format example: :cite:`citationkey`
% 6. For key papers, add context about their importance to the field
%
% Useful Resources:
% - Google Scholar: https://scholar.google.com/
% - DOI to BibTeX: https://doi2bib.org/
% - PubMed: https://pubmed.ncbi.nlm.nih.gov/
% ============================================================================
@article{chu2025localized,
  title={Localized Space Coding and Phase Coding Complement Each Other to Achieve Robust and Efficient Spatial Representation},
  author={Chu, Tianhao and Wu, Yuling and Qiu, Wentao and Jiang, Zihao and Burgess, Neil and Hong, Bo and Wu, Si},
  journal={bioRxiv},
  pages={2025--09},
  year={2025},
  publisher={Cold Spring Harbor Laboratory},
  doi={10.1101/2025.09.07.674775}
}

@article{burak2009accurate,
  title={Accurate path integration in continuous attractor network models of grid cells},
  author={Burak, Yoram and Fiete, Ila R},
  journal={PLoS computational biology},
  volume={5},
  number={2},
  pages={e1000291},
  year={2009},
  publisher={Public Library of Science San Francisco, USA},
  doi={10.1371/journal.pcbi.1000291}
}

% Related Works
@ARTICLE{Gewaltig:NEST,
  author  = {Marc-Oliver Gewaltig and Markus Diesmann},
  title   = {NEST (NEural Simulation Tool)},
  journal = {Scholarpedia},
  year    = {2007},
  volume  = {2},
  pages   = {1430},
  number  = {4},
  doi     = {10.4249/scholarpedia.1430}
}

@article{stimberg2019brian,
  title={Brian 2, an intuitive and efficient neural simulator},
  author={Stimberg, Marcel and Brette, Romain and Goodman, Dan FM},
  journal={elife},
  volume={8},
  pages={e47314},
  year={2019},
  publisher={eLife Sciences Publications, Ltd}
}

@article{hines1997neuron,
  title={The NEURON simulation environment},
  author={Hines, Michael L and Carnevale, Nicholas T},
  journal={Neural computation},
  volume={9},
  number={6},
  pages={1179--1209},
  year={1997},
  publisher={MIT Press}
}

@inproceedings{wolf-etal-2020-transformers,
    title = "Transformers: State-of-the-Art Natural Language Processing",
    author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
    pages = "38--45"
}

@inproceedings{Ansel_PyTorch_2_Faster_2024,
author = {Ansel, Jason and Yang, Edward and He, Horace and Gimelshein, Natalia and Jain, Animesh and Voznesensky, Michael and Bao, Bin and Bell, Peter and Berard, David and Burovski, Evgeni and Chauhan, Geeta and Chourdia, Anjali and Constable, Will and Desmaison, Alban and DeVito, Zachary and Ellison, Elias and Feng, Will and Gong, Jiong and Gschwind, Michael and Hirsh, Brian and Huang, Sherlock and Kalambarkar, Kshiteej and Kirsch, Laurent and Lazos, Michael and Lezcano, Mario and Liang, Yanbo and Liang, Jason and Lu, Yinghai and Luk, CK and Maher, Bert and Pan, Yunjie and Puhrsch, Christian and Reso, Matthias and Saroufim, Mark and Siraichi, Marcos Yukio and Suk, Helen and Suo, Michael and Tillet, Phil and Wang, Eikan and Wang, Xiaodong and Wen, William and Zhang, Shunting and Zhao, Xu and Zhou, Keren and Zou, Richard and Mathews, Ajit and Chanan, Gregory and Wu, Peng and Chintala, Soumith},
booktitle = {29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2 (ASPLOS '24)},
doi = {10.1145/3620665.3640366},
month = apr,
publisher = {ACM},
title = {{PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation}},
url = {https://docs.pytorch.org/assets/pytorch2-2.pdf},
year = {2024}
}

@software{Abadi_TensorFlow_Large-scale_machine_2015,
author = {Abadi, Martín and Agarwal, Ashish and Barham, Paul and Brevdo, Eugene and Chen, Zhifeng and Citro, Craig and Corrado, Greg S. and Davis, Andy and Dean, Jeffrey and Devin, Matthieu and Ghemawat, Sanjay and Goodfellow, Ian and Harp, Andrew and Irving, Geoffrey and Isard, Michael and Jozefowicz, Rafal and Jia, Yangqing and Kaiser, Lukasz and Kudlur, Manjunath and Levenberg, Josh and Mané, Dan and Schuster, Mike and Monga, Rajat and Moore, Sherry and Murray, Derek and Olah, Chris and Shlens, Jonathon and Steiner, Benoit and Sutskever, Ilya and Talwar, Kunal and Tucker, Paul and Vanhoucke, Vincent and Vasudevan, Vijay and Viégas, Fernanda and Vinyals, Oriol and Warden, Pete and Wattenberg, Martin and Wicke, Martin and Yu, Yuan and Zheng, Xiaoqiang},
doi = {10.5281/zenodo.4724125},
license = {Apache-2.0},
month = nov,
title = {{TensorFlow, Large-scale machine learning on heterogeneous systems}},
year = {2015}
}

@software{torchvision2016,
  title = {TorchVision: PyTorch's Computer Vision library},
  author = {TorchVision maintainers and contributors},
  year = {2016},
  journal = {GitHub repository},
  publisher = {GitHub},
  howpublished = {\url{https://github.com/pytorch/vision}}
}