Skip to main content
SpringerLink
Log in

Motion Selectivity of the Local Filed Potentials in the Primary Visual Cortex of Rats: A Machine Learning Approach

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

Using rodents as a model of physiological vision studies requires adequate information about their visual cortex. Although the primary visual cortex of rats has different sub-regions, there are few studies on the different response patterns of these sub-regions. In this study, we recorded the local field potentials (LFPs) from sub-regions of the primary visual cortex (V1) of anesthetized rats. We used random dots patterns as moving stimuli presented in random sequences. Then we used machine learning methods to decode the direction and speed of the stimuli from the recorded signals. Our results revealed that there are different patterns of responses to motion stimuli across sub-regions. Although the decoding results using LFPs were not high, they were enhanced by moving to the lateral sub-regions of the V1. Our results suggested that the location of the recording areas impact reaction time, the pattern of the responses in time- and frequency- domains, and encoding the motion stimuli.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data Availability

The datasets generated for this study are available upon a reasonable request to the corresponding author.

Abbreviations

LFP:

Local Field Potential

V1:

Primary Visual Cortex

V1M:

Monocular V1

V1BM:

Binocular- monocular V1

V1B:

Binocular V1

NIH:

National Institute of Health

LCD:

Liquid Crystal Display

EMVP:

Envelopes' Maximum Value Points

USART:

Universal asynchronous receiver-transmitter

MI:

Mutual Information

KNN:

K-Nearest Neighbor

References

  1. Vinken K, Van den Bergh G, Vermaercke B, Op de Beeck HP. Neural Representations of Natural and Scrambled Movies Progressively Change from Rat Striate to Temporal Cortex. Cereb Cortex. 2016;26(7):3310–22.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Zoccolan D. Invariant visual object recognition and shape processing in rats. Behav Brain Res. 2015;285:10–33.

    Article  PubMed  PubMed Central  Google Scholar 

  3. De Keyser R, Bossens C, Kubilius J, Op de Beeck HP. Cue-invariant shape recognition in rats as tested with secondorder contours. J Vis. 2015;15(15):14–14.

    Article  PubMed  Google Scholar 

  4. Djurdjevic V, Ansuini A, Bertolini D, Macke JH, Zoccolan D. Accuracy of Rats in Discriminating Visual Objects Is Explained by the Complexity of Their Perceptual Strategy. Curr Biol. 2018;28(7):1005-1015.e5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Tafazoli S, et al. Emergence of transformation-tolerant representations of visual objects in rat lateral extrastriate cortex. Elife. 2017;6:1–39.

    Article  Google Scholar 

  6. Samonds JM, Lieberman S, Priebe NJ. Motion discrimination and the motion aftereffect in mouse vision. eNeuro. 2018;5(6).

  7. Caramellino R, Piasini E, Buccellato A, Carboncino A, Balasubramanian V, Zoccolan D. Rat sensitivity to multipoint statistics is predicted by efficient coding of natural scenes. Elife. 2021;10.

  8. Wiesenfeld Z, Kornel EE. Receptive fields of single cells in the visual cortex of the hooded rat. Brain Res. 1975;94(3):401–12.

    Article  CAS  PubMed  Google Scholar 

  9. Girman SV, Sauvé Y, Lund RD. Receptive Field Properties of Single Neurons in Rat Primary Visual Cortex. J Neurophysiol. 1999;82(1):301–11.

    Article  CAS  PubMed  Google Scholar 

  10. Muir DR, Roth MM, Helmchen F, Kampa BM. Model-based analysis of pattern motion processing in mouse primary visual cortex. Front Neural Circuits. 2015;9:38.

  11. Matteucci G, Bellacosa Marotti R, Riggi M, Rosselli FB, Zoccolan D. Nonlinear processing of shape information in rat lateral extrastriate cortex. J Neurosci. 2019;39(9):1938–18.

  12. Vermaercke B, Gerich FJ, Ytebrouck E, Arckens L, Op de Beeck HP, Van den Bergh G. Functional specialization in rat occipital and temporal visual cortex. J Neurophysiol. 2014;112(8):1963–83.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Marques T, et al. A Role for Mouse Primary Visual Cortex in Motion Perception. Curr Biol. 2018;28(11):1703-1713.e6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Petruno SK, Clark RE, Reinagel P. Evidence That Primary Visual Cortex Is Required for Image, Orientation, and Motion Discrimination by Rats. PLoS ONE. 2013;8(2): e56543.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  15. Palagina G, Meyer JF, Smirnakis SM. Complex visual motion representation in mouse area V1. J Neurosci. 2017;37(1):164–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Juavinett AL, Callaway EM. Pattern and Component Motion Responses in Mouse Visual Cortical Areas. Curr Biol. 2015;25(13):1759–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Douglas RM, Neve A, Quittenbaum JP, Alam NM, Prusky GT. Perception of visual motion coherence by rats and mice. Vision Res. 2006;46(18):2842–7.

    Article  CAS  PubMed  Google Scholar 

  18. Stirman JN, Townsend LB, Smith SL. A touchscreen based global motion perception task for mice. Vision Res. 2016;127:74–83.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Matteucci G, Zattera B, Bellacosa Marotti R, Zoccolan D. Rats spontaneously perceive global motion direction of drifting plaids. PLOS Comput Biol. 2021;17(9):e1009415.

  20. Zilles K, Wree A, Schleicher A, Divac I. The monocular and binocular subfields of the rat’s primary visual cortex: A quantitative morphological approach. J Comp Neurol. 1984;226(3):391–402.

    Article  CAS  PubMed  Google Scholar 

  21. Kuo M-C, Dringenberg HC. Comparison of long-term potentiation (LTP) in the medial (monocular) and lateral (binocular) rat primary visual cortex. Brain Res. 2012;1488:51–9.

    Article  CAS  PubMed  Google Scholar 

  22. Griffen TC, Haley MS, Fontanini A, Maffei A. Rapid plasticity of visually evoked responses in rat monocular visual cortex. PLoS ONE. 2017;12(9):1–12.

    Article  Google Scholar 

  23. Katzner S, Nauhaus I, Benucci A, Bonin V, Ringach DL, Carandini M. Local Origin of Field Potentials in Visual Cortex. Neuron. 2009;61(1):35–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Logothetis NK, Pauis J, Augath M, Trinath T, Oeltermann A. Neurophysiological investigation of thebasis of the fMRI signal. Nature. 2001;412(6843):150–7.

    Article  ADS  CAS  PubMed  Google Scholar 

  25. Liu J, Newsome WT. Local Field Potential in Cortical Area MT: Stimulus Tuning and Behavioral Correlations. J Neurosci. 2006;26(30):7779–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Khawaja FA, Tsui JMG, Pack CC. Pattern Motion Selectivity of Spiking Outputs and Local Field Potentials in Macaque Visual Cortex. J Neurosci. 2009;29(43):13702–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Cui Y, Liu LD, Khawaja FA, Pack CC, Butts DA. Diverse Suppressive Influences in Area MT and Selectivity to Complex Motion Features. J Neurosci. 2013;33(42):16715–28.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Field KJ, White WJ, Lang CM. Anaesthetic effects of chloral hydrate, pentobarbitone and urethane in adult male rats. Lab Anim. 1993;27(3):258–69.

    Article  CAS  PubMed  Google Scholar 

  29. Paxinos G, Watson C. The Rat Brain in Stereotaxic Coordinates Sixth Edition by. Acad Press. 2006;170.

  30. Campisi P, Rocca DL. Brain waves for automatic biometric-based user recognition. IEEE Trans Inf Forensics Secur. 2014;9(5):782–800.

    Article  Google Scholar 

  31. Kisley MA, Cornwell ZM. Gamma and beta neural activity evoked during a sensory gating paradigm: Effects of auditory, somatosensory and cross-modal stimulation. Clin Neurophysiol. 2006;117(11):2549–63.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Rey HG, Fried I, Quian Quiroga R. Timing of single-neuron and local field potential responses in the human medial temporal lobe. Curr Biol. 2014;24(3):299–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Ktonas PY, Papp N. Instantaneous envelope and phase extraction from real signals: Theory, implementation, and an application to EEG analysis. Signal Process. 1980;2(4):373–85.

    Article  Google Scholar 

  34. Massey FJ. The Kolmogorov-Smirnov Test for Goodness of Fit. J Am Stat Assoc. 1951;46(253):68–78.

    Article  Google Scholar 

  35. Fulop SA, Fitz K. Algorithms for computing the time-corrected instantaneous frequency (reassigned) spectrogram, with applications. J Acoust Soc Am. 2006;119(1):360–71.

    Article  ADS  PubMed  Google Scholar 

  36. Phinyomark A, Thongpanja S, Hu H, Phukpattaranont P, Limsakul C. The Usefulness of Mean and Median Frequencies in Electromyography Analysis, in Computational Intelligence in Electromyography Analysis - A Perspective on Current Applications and Future Challenges, InTech. 2012

  37. Taghizadeh-Sarabi M, Daliri MR, Niksirat KS. Decoding Objects of Basic Categories from Electroencephalographic Signals Using Wavelet Transform and Support Vector Machines. Brain Topogr. 2014;28(1):33–46.

    Article  PubMed  Google Scholar 

  38. Hutter M, Zaffalon M. Distribution of mutual information from complete and incomplete data. Comput Stat Data Anal. 2005;48(3):633–57.

    Article  MathSciNet  Google Scholar 

  39. Maling N, McIntyre C. Local Field Potential Analysis for Closed-Loop Neuromodulation. In Closed Loop Neurosci. Elsevier Inc. 2016;67–8.

  40. Xu W, Huang X, Takagaki K, Wu J. Compression and Reflection of Visually Evoked Cortical Waves. Neuron. 2007;55(1):119–29.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Ray S, Maunsell JHR. Do gamma oscillations play a role in cerebral cortex? Trends Cogn Sci Elsevier Ltd. 2015;19(2):78–8.

    Article  Google Scholar 

  42. Hu L, Hu Q, Chen Y. Orientation and Distance Dependence of Pairwise Correlation in Macaque V1. In ACM Int Conf Proc Series. 2020;43–5.

  43. Deitch D, Rubin A, Ziv Y. Representational drift in the mouse visual cortex. Curr Biol. 2021;31(19):4327-4339.e6.

    Article  CAS  PubMed  Google Scholar 

  44. Andrei AR, Akil AE, Kharas N, Rosenbaum R, Josić K, Dragoi V. Rapid compensatory plasticity revealed by dynamic correlated activity in monkeys in vivo. Nat Neurosci. 2023;26(11):1960–9.

    Article  CAS  PubMed  Google Scholar 

  45. Gutnisky DA, Beaman CB, Lew SE, Dragoi V. Spontaneous fluctuations in visual cortical responses influence population coding accuracy. Cereb Cortex. 2017;27(2):1409–27.

    Article  PubMed  Google Scholar 

  46. Jin M, Glickfeld LL. Mouse Higher Visual Areas Provide Both Distributed and Specialized Contributions to Visually Guided Behaviors. Curr Biol. 2020;30(23):4682-4692.e7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Sceniak MP, MacIver MB. Cellular actions of urethane on rat visual cortical neurons in vitro. J Neurophysiol. 2006;95(6):3865–74.

    Article  CAS  PubMed  Google Scholar 

  48. Hama N, Ito SI, Hirota A. Optical imaging of the propagation patterns of neural responses in the rat sensory cortex: Comparison under two different anesthetic conditions. Neuroscience. 2015;284:125–33.

    Article  CAS  PubMed  Google Scholar 

  49. Gaese BH, Ostwald J. Anesthesia Changes Frequency Tuning of Neurons in the Rat Primary Auditory Cortex. J Neurophysiol. 2001;86(2):1062–6.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Alavie Mirfathollahi for helping in preparing the histology of the animals and Ali Rahimpour for the language editing of the paper.

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Neuroscience and Neuroengineering Research Lab, Department of Biomedical Engineering, School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, 16846-13114, Tehran, Iran

    Abbas Pourhedayat, Marzie Aghababaeipour Dehkordi & Mohammad Reza Daliri

Authors
  1. Abbas Pourhedayat
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marzie Aghababaeipour Dehkordi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammad Reza Daliri
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

AP and MRD designed the study; AP and MAD recorded the data; AP and MRD performed data analyses and interpretation of the data; and AP, MAD, and MRD wrote the paper.

Corresponding author

Correspondence to Mohammad Reza Daliri.

Ethics declarations

Ethics Approval and Consent to Participate

All experimental procedures were done in the Neuroscience & Neuroengineering Research Laboratory at Iran University of Science and Technology (IUST). All protocols in strict accordance with the Care and Use Guide of Laboratory animals of the National Institute of Health were approved in the Animal Care and Use Committee of Neuroscience & Neuroengineering Research Laboratory. Urethane was used for anesthesia, and all efforts were done to minimize the suffering. In the end, the animal was euthanized by overdosing on urethane. Decapitation was made to ensure the animal's death after its heart stopped beating, its body temperature dropped down, and had no respiration.

Consent for Publication

Not applicable.

Competing Interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pourhedayat, A., Aghababaeipour Dehkordi, M. & Daliri, M. Motion Selectivity of the Local Filed Potentials in the Primary Visual Cortex of Rats: A Machine Learning Approach. Cogn Comput (2024). https://doi.org/10.1007/s12559-024-10263-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12559-024-10263-7

Keywords

Search

Navigation