面向生物视觉的神经编码模型研究：进展与挑战

贾杉杉; 余肇飞; 刘健; 黄铁军

doi:10.11999/JEIT221368

面向生物视觉的神经编码模型研究：进展与挑战

doi: 10.11999/JEIT221368

贾杉杉^{1, 2},
余肇飞^{1, 2, ,},
刘健³,
黄铁军^{1, 2}

1.
北京大学计算机学院北京 100871
2.
北京大学人工智能研究院北京 100871
3.
利兹大学计算学院利兹 LS2 9JT

基金项目: 国家自然科学基金(62176003)

详细信息

作者简介:
贾杉杉：女，博士生，研究方向为类脑计算、神经计算

余肇飞：男，博士，助理教授，研究方向为类脑计算、神经网络

刘健：男，博士，教授，研究方向为类脑计算、神经计算

黄铁军：男，博士，教授，研究方向为类脑计算、人工智能

通讯作者:
余肇飞　yuzf12@pku.edu.cn

中图分类号: TN919.31; TP183
计量
- 文章访问数: 915
- HTML全文浏览量: 784
- PDF下载量: 269
- 被引次数: 0
出版历程
- 收稿日期: 2022-11-01
- 修回日期: 2023-03-16
- 网络出版日期: 2023-03-21
- 刊出日期: 2023-08-21

Research on Neural Encoding Models for Biological Vision: Progress and Challenges

JIA Shanshan^{1, 2},
YU Zhaofei^{1, 2
, ,},
LIU Jian³,
HUANG Tiejun^{1, 2}

1.
School of Computer Science, Peking University, Beijing 100871, China
2.
Institute for Artificial Intelligence, Peking University, Beijing 100871, China
3.
School of Computing, University of Leeds, Leeds LS2 9JT, UK

Funds: The National Natural Science Foundation of China (62176003)

摘要

摘要: 视觉系统通过神经元将丰富且密集的动态视觉刺激编码成时变的神经响应。探寻视觉刺激与神经响应之间函数关系是理解神经编码机理的一种常见手段。该文首先介绍了视觉系统的神经编码模型，归纳为两类：生物物理编码模型和人工神经网络编码模型。然后介绍了各种模型的参数估计方法。通过对比各种模型的特性，总结了各自的优势、应用场景及所存在问题。最后，对视觉编码研究的现状以及未来面对的挑战进行了展望。
- 类脑视觉 /
- 生物视觉系统 /
- 生物物理编码模型 /
- 人工神经网络编码模型 /
- 系统辨识
Abstract: The visual system encodes rich and dense dynamic visual stimuli into time-varying neural responses through neurons. Exploring the functional relationship between visual stimuli and neural responses is a common approach to understanding neural encoding mechanisms. Neural encoding models of the visual system are presented throughout this paper, which can be grouped into two categories: biophysical encoding models and artificial neural network encoding models. Then parameter estimation methods for various models are introduced. By comparing the characteristics of various models, the respective advantages, application scenarios and existing problems are summarized. Finally, the current situation and future challenges of visual encoding research are summarized and forecasted．
- Brain-like vision /
- Biological visual system /
- Biophysical encoding model /
- Artificial neural network encoding model /
- System identification

HTML全文

图 1 线性模型

下载: 全尺寸图片幻灯片

图 2 非线性输入模型

下载: 全尺寸图片幻灯片

参考文献(64)

[1]	COLLINGER J L, WODLINGER B, DOWNEY J E, et al. High-performance neuroprosthetic control by an individual with tetraplegia[J]. The Lancet, 2013, 381(9866): 557–564. doi: 10.1016/S0140-6736(12)61816-9
[2]	SHANECHI M M, ORSBORN A L, MOORMAN H G, et al. Rapid control and feedback rates enhance neuroprosthetic control[J]. Nature Communications, 2017, 8: 13825. doi: 10.1038/ncomms13825
[3]	SEEBER B U and BRUCE I C. The history and future of neural modeling for cochlear implants[J]. Network: Computation in Neural Systems, 2016, 27(2/3): 53–66. doi: 10.1080/0954898X.2016.1223365
[4]	JOHNSON L A, DELLA SANTINA C C, and WANG Xiaoqin. Representations of time-varying cochlear implant stimulation in auditory cortex of awake marmosets (Callithrix jacchus)[J]. Journal of Neuroscience, 2017, 37(29): 7008–7022. doi: 10.1523/JNEUROSCI.0093-17.2017
[5]	GHEZZI D. Retinal prostheses: Progress toward the next generation implants[J]. Frontiers in Neuroscience, 2015, 9: 290. doi: 10.3389/fnins.2015.00290
[6]	TANG Jing, QIN Nan, CHONG Yan, et al. Nanowire arrays restore vision in blind mice[J]. Nature Communications, 2018, 9(1): 786. doi: 10.1038/s41467-018-03212-0
[7]	HUANG Tiejun, ZHENG Yajing, YU Zhaofei, et al. 1000× faster camera and machine vision with ordinary devices[J]. Engineering, To be published.
[8]	ZHU Lin, DONG Siwei, LI Jianing, et al. Retina-like visual image reconstruction via spiking neural model[C]. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA, 2020: 1438–1446.
[9]	ZHENG Yajing, ZHENG Lingxiao, YU Zhaofei, et al. High-speed image reconstruction through short-term plasticity for spiking cameras[C]. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, USA, 2021: 6354–6363.
[10]	ZHAO Jing, XIONG Ruiqin, XIE Jiyu, et al. Reconstructing clear image for high-speed motion scene with a retina-inspired spike camera[J]. IEEE Transactions on Computational Imaging, 2022, 8: 12–27. doi: 10.1109/TCI.2021.3136446
[11]	ZHAO Junwei, YU Zhaofei, MA Lei, et al. Modeling the detection capability of high-speed spiking cameras[C]. 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore, 2022: 4653–4657.
[12]	DING Ziluo, ZHAO Rui, ZHANG Jiyuan, et al. Spatio-temporal recurrent networks for event-based optical flow estimation[C]. The 36th AAAI Conference on Artificial Intelligence, Palo Alto, USA, 2022: 525–533.
[13]	HU Liwen, ZHAO Rui, DING Ziluo, et al. Optical flow estimation for spiking camera[C]. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, USA, 2022: 17844–17853.
[14]	LINDEN J F, LIU R C, SAHANI M, et al. Spectrotemporal structure of receptive fields in areas AI and AAF of mouse auditory cortex[J]. Journal of Neurophysiology, 2003, 90(4): 2660–2675. doi: 10.1152/jn.00751.2002
[15]	MACHENS C K, WEHR M S, and ZADOR A M. Linearity of cortical receptive fields measured with natural sounds[J]. Journal of Neuroscience, 2004, 24(5): 1089–1100. doi: 10.1523/JNEUROSCI.4445-03.2004
[16]	SAHANI M and LINDEN J F. How linear are auditory cortical responses?[C]. The 15th International Conference on Neural Information Processing Systems, Vancouver, Canada, 2002: 125–132.
[17]	SHARPEE T, RUST N C, and BIALEK W. Analyzing neural responses to natural signals: Maximally informative dimensions[J]. Neural Computation, 2004, 16(2): 223–250. doi: 10.1162/089976604322742010
[18]	CHICHILNISKY E J. A simple white noise analysis of neuronal light responses[J]. Network, 2001, 12(2): 199–213. doi: 10.1080/713663221
[19]	PANINSKI L. Maximum likelihood estimation of cascade point-process neural encoding models[J]. Network: Computation in Neural Systems, 2004, 15(4): 243–262. doi: 10.1088/0954-898X_15_4_002
[20]	RABINOWITZ N C, WILLMORE B D B, SCHNUPP J W H, et al. Contrast gain control in auditory cortex[J]. Neuron, 2011, 70(6): 1178–1191. doi: 10.1016/j.neuron.2011.04.030
[21]	VINJE W E and GALLANT J L. Natural stimulation of the nonclassical receptive field increases information transmission efficiency in V1[J]. Journal of Neuroscience, 2002, 22(7): 2904–2915. doi: 10.1523/JNEUROSCI.22-07-02904.2002
[22]	LIU J K, SCHREYER H M, ONKEN A, et al. Inference of neuronal functional circuitry with spike-triggered non-negative matrix factorization[J]. Nature Communications, 2017, 8(1): 149. doi: 10.1038/s41467-017-00156-9
[23]	SHAH N P, BRACKBILL N, RHOADES C, et al. Inference of nonlinear receptive field subunits with spike-triggered clustering[J]. eLife, 2020, 9: e45743. doi: 10.7554/eLife.45743
[24]	KARAMANLIS D and GOLLISCH T. Nonlinear spatial integration underlies the diversity of retinal ganglion cell responses to natural images[J]. Journal of Neuroscience, 2021, 41(15): 3479–3498. doi: 10.1523/JNEUROSCI.3075-20.2021
[25]	LIU J K, KARAMANLIS D, and GOLLISCH T. Simple model for encoding natural images by retinal ganglion cells with nonlinear spatial integration[J]. PLoS Computational Biology, 2022, 18(3): e1009925. doi: 10.1371/journal.pcbi.1009925
[26]	MCFARLAND J M, CUI Yuwei, and BUTTS D A. Inferring nonlinear neuronal computation based on physiologically plausible inputs[J]. PLoS Computational Biology, 2013, 9(7): e1003143. doi: 10.1371/journal.pcbi.1003143
[27]	DORRN A L, YUAN Kexin, BARKER A J, et al. Developmental sensory experience balances cortical excitation and inhibition[J]. Nature, 2010, 465(7300): 932–936. doi: 10.1038/nature09119
[28]	MARMARELIS V. Analysis of Physiological Systems: The White-Noise Approach[M]. Springer, 2012.
[29]	PARK I M, ARCHER E, PRIEBE N, et al. Spectral methods for neural characterization using generalized quadratic models[C]. The 26th International Conference on Neural Information Processing Systems, Lake Tahoe USA, 2013: 2454–2462.
[30]	PARK I M and PILLOW J W. Bayesian spike-triggered covariance analysis[C]. The 24th International Conference on Neural Information Processing Systems, Granada, Spain, 2011: 1692–1700.
[31]	JIA Shanshan, XING Dajun, YU Zhaofei, et al. Dissecting cascade computational components in spiking neural networks[J]. PLoS Computational Biology, 2021, 17(11): e1009640. doi: 10.1371/journal.pcbi.1009640
[32]	LECUN Y, BENGIO Y, and HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436–444. doi: 10.1038/nature14539
[33]	YAMINS D L K, HONG Ha, CADIEU C F, et al. Performance-optimized hierarchical models predict neural responses in higher visual cortex[J]. Proceedings of the National Academy of Sciences of the United States of America, 2014, 111(23): 8619–8624. doi: 10.1073/pnas.1403112111
[34]	KHALIGH-RAZAVI S M and KRIEGESKORTE N. Deep supervised, but not unsupervised, models may explain IT cortical representation[J]. PLoS Computational Biology, 2014, 10(11): e1003915. doi: 10.1371/journal.pcbi.1003915
[35]	KRIEGESKORTE N. Deep neural networks: A new framework for modeling biological vision and brain information processing[J]. Annual Review of Vision Science, 2015, 1: 417–446. doi: 10.1146/annurev-vision-082114-035447
[36]	YAMINS D L K and DICARLO J J. Using goal-driven deep learning models to understand sensory cortex[J]. Nature Neuroscience, 2016, 19(3): 356–365. doi: 10.1038/nn.4244
[37]	ROWEKAMP R J and SHARPEE T O. Cross-orientation suppression in visual area V2[J]. Nature Communications, 2017, 8: 15739. doi: 10.1038/ncomms15739
[38]	CADENA S A, DENFIELD G H, WALKER E Y, et al. Deep convolutional models improve predictions of macaque V1 responses to natural images[J]. PLoS Computational Biology, 2019, 15(4): e1006897. doi: 10.1371/journal.pcbi.1006897
[39]	YAN Qi, ZHENG Yajing, JIA Shanshan, et al. Revealing fine structures of the retinal receptive field by deep-learning networks[J]. IEEE Transactions on Cybernetics, 2022, 52(1): 39–50. doi: 10.1109/TCYB.2020.2972983
[40]	VANCE P J, DAS G P, KERR D, et al. Bioinspired approach to modeling retinal ganglion cells using system identification techniques[J]. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(5): 1796–1808. doi: 10.1109/TNNLS.2017.2690139
[41]	MCINTOSH L T, MAHESWARANATHAN N, NAYEBI A, et al. Deep learning models of the retinal response to natural scenes[C]. The 30th International Conference on Neural Information Processing Systems, Barcelona, Spain, 2016: 1369–1377.
[42]	KAR K, KUBILIUS J, SCHMIDT K, et al. Evidence that recurrent circuits are critical to the ventral stream’ s execution of core object recognition behavior[J]. Nature Neuroscience, 2019, 22(6): 974–983. doi: 10.1038/s41593-019-0392-5
[43]	KIETZMANN T C, SPOERER C J, SÖRENSEN L K A, et al. Recurrence is required to capture the representational dynamics of the human visual system[J]. Proceedings of the National Academy of Sciences of the United States of America, 2019, 116(43): 21854–21863. doi: 10.1073/pnas.1905544116
[44]	RAJAEI K, MOHSENZADEH Y, EBRAHIMPOUR R, et al. Beyond core object recognition: Recurrent processes account for object recognition under occlusion[J]. PLoS Computational Biology, 2019, 15(5): e1007001. doi: 10.1371/journal.pcbi.1007001
[45]	LINSLEY D, KIM J, VEERABADRAN V, et al. Learning long-range spatial dependencies with horizontal gated recurrent units[C]. The 32nd International Conference on Neural Information Processing Systems, Montréal, Canada, 2018: 152–164.
[46]	O’BRIEN J and BLOOMFIELD S A. Plasticity of retinal gap junctions: Roles in synaptic physiology and disease[J]. Annual Review of Vision Science, 2018, 4: 79–100. doi: 10.1146/annurev-vision-091517-034133
[47]	RIVLIN-ETZION M, GRIMES W N, and RIEKE F. Flexible neural hardware supports dynamic computations in retina[J]. Trends in Neurosciences, 2018, 41(4): 224–237. doi: 10.1016/j.tins.2018.01.009
[48]	TRENHOLM S, SCHWAB D J, BALASUBRAMANIAN V, et al. Lag normalization in an electrically coupled neural network[J]. Nature Neuroscience, 2013, 16(2): 154–156. doi: 10.1038/nn.3308
[49]	YU Zhaofei, LIU J K, JIA Shanshan, et al. Toward the next generation of retinal neuroprosthesis: Visual computation with spikes[J]. Engineering, 2020, 6(4): 449–461. doi: 10.1016/j.eng.2020.02.004
[50]	ZHENG Yajing, JIA Shanshan, YU Zhaofei, et al. Unraveling neural coding of dynamic natural visual scenes via convolutional recurrent neural networks[J]. Patterns, 2021, 2(10): 100350. doi: 10.1016/j.patter.2021.100350
[51]	PANINSKI L. Convergence properties of some spike-triggered analysis techniques[C]. The 15th International Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, 2002: 189–196.
[52]	JIA Shanshan, YU Zhaofei, ONKEN A, et al. Neural system identification with spike-triggered non-negative matrix factorization[J]. IEEE Transactions on Cybernetics, 2022, 52(6): 4772–4783. doi: 10.1109/TCYB.2020.3042513
[53]	ONKEN A, LIU J K, KARUNASEKARA P P C R, et al. Using matrix and tensor factorizations for the single-trial analysis of population spike trains[J]. PLoS Computational Biology, 2016, 12(11): e1005189. doi: 10.1371/journal.pcbi.1005189
[54]	WILLIAMS A H, KIM T H, WANG F, et al. Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor component analysis[J]. Neuron, 2018, 98(6): 1099–1115.e8. doi: 10.1016/j.neuron.2018.05.015
[55]	ZHUANG Chengxu, YAN Siming, NAYEBI A, et al. Unsupervised neural network models of the ventral visual stream[J]. Proceedings of the National Academy of Sciences of the United States of America, 2021, 118(3): e2014196118. doi: 10.1073/pnas.2014196118
[56]	BRENNER N, STRONG S P, KOBERLE R, et al. Synergy in a neural code[J]. Neural Computation, 2000, 12(7): 1531–1552. doi: 10.1162/089976600300015259
[57]	SHARPEE T O, MILLER K D, and STRYKER M P. On the importance of static nonlinearity in estimating spatiotemporal neural filters with natural stimuli[J]. Journal of Neurophysiology, 2008, 99(5): 2496–2509. doi: 10.1152/jn.01397.2007
[58]	MEYER A F, DIEPENBROCK J P, HAPPEL M F K, et al. Discriminative learning of receptive fields from responses to non-Gaussian stimulus ensembles[J]. PLoS One, 2014, 9(4): e93062. doi: 10.1371/journal.pone.0093062
[59]	MEYER A F, DIEPENBROCK J P, OHL F W, et al. Quantifying neural coding noise in linear threshold models[C]. The 6th International IEEE/EMBS Conference on Neural Engineering, San Diego, USA, 2013: 1127–1130.
[60]	ZAPP S J, NITSCHE S, and GOLLISCH T. Retinal receptive-field substructure: Scaffolding for coding and computation[J]. Trends in Neurosciences, 2022, 45(6): 430–445. doi: 10.1016/J.TINS.2022.03.005
[61]	KARAMANLIS D, SCHREYER H M, and GOLLISCH T. Retinal encoding of natural scenes[J]. Annual Review of Vision Science, 2022, 8: 171–193. doi: 10.1146/annurev-vision-100820-114239
[62]	SALAHIAN N, TAB F A, SEYEDI S A, et al. Deep autoencoder-like NMF with contrastive regularization and feature relationship preservation[J]. Expert Systems with Applications, 2023, 214: 119051. doi: 10.1016/J.ESWA.2022.119051
[63]	CHEN Wensheng, ZENG Qianwen, and PAN Binbin. A survey of deep nonnegative matrix factorization[J]. Neurocomputing, 2022, 491: 305–320. doi: 10.1016/j.neucom.2021.08.152
[64]	XU Qi, LI Yaxin, SHEN Jiangrong, et al. Hierarchical spiking-based model for efficient image classification with enhanced feature extraction and encoding[J]. IEEE Transactions on Neural Networks and Learning Systems, To be published.