IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1004167.html
   My bibliography  Save this article

Identifying and Tracking Simulated Synaptic Inputs from Neuronal Firing: Insights from In Vitro Experiments

Author

Listed:
  • Maxim Volgushev
  • Vladimir Ilin
  • Ian H Stevenson

Abstract

Accurately describing synaptic interactions between neurons and how interactions change over time are key challenges for systems neuroscience. Although intracellular electrophysiology is a powerful tool for studying synaptic integration and plasticity, it is limited by the small number of neurons that can be recorded simultaneously in vitro and by the technical difficulty of intracellular recording in vivo. One way around these difficulties may be to use large-scale extracellular recording of spike trains and apply statistical methods to model and infer functional connections between neurons. These techniques have the potential to reveal large-scale connectivity structure based on the spike timing alone. However, the interpretation of functional connectivity is often approximate, since only a small fraction of presynaptic inputs are typically observed. Here we use in vitro current injection in layer 2/3 pyramidal neurons to validate methods for inferring functional connectivity in a setting where input to the neuron is controlled. In experiments with partially-defined input, we inject a single simulated input with known amplitude on a background of fluctuating noise. In a fully-defined input paradigm, we then control the synaptic weights and timing of many simulated presynaptic neurons. By analyzing the firing of neurons in response to these artificial inputs, we ask 1) How does functional connectivity inferred from spikes relate to simulated synaptic input? and 2) What are the limitations of connectivity inference? We find that individual current-based synaptic inputs are detectable over a broad range of amplitudes and conditions. Detectability depends on input amplitude and output firing rate, and excitatory inputs are detected more readily than inhibitory. Moreover, as we model increasing numbers of presynaptic inputs, we are able to estimate connection strengths more accurately and detect the presence of connections more quickly. These results illustrate the possibilities and outline the limits of inferring synaptic input from spikes.Author Summary: Synapses play a central role in neural information processing – weighting individual inputs in different ways allows neurons to perform a range of computations, and the changing of synaptic weights over time allows learning and recovery from injury. Intracellular recordings provide the most detailed view of the properties and dynamics of individual synapses, but studying many synapses simultaneously during natural behavior is not feasible with current methods. In contrast, extracellular recordings allow many neurons to be observed simultaneously, but the details of their synaptic interactions have to be inferred from spiking alone. By modeling how spikes from one neuron, statistically, affect the spiking of another neuron, statistical inference methods can reveal “functional” connections between neurons. Here we examine these methods using neuronal spiking evoked by intracellular injection of a defined artificial current that simulates input from a single presynaptic neuron or a large population of presynaptic neurons. We study how well functional connectivity methods are able to reconstruct the simulated inputs, and assess the validity and limitations of functional connectivity inference. We find that, with a sufficient amount of data, accurate inference is often possible, and can become more accurate as more of the presynaptic inputs are observed.

Suggested Citation

  • Maxim Volgushev & Vladimir Ilin & Ian H Stevenson, 2015. "Identifying and Tracking Simulated Synaptic Inputs from Neuronal Firing: Insights from In Vitro Experiments," PLOS Computational Biology, Public Library of Science, vol. 11(3), pages 1-31, March.
  • Handle: RePEc:plo:pcbi00:1004167
    DOI: 10.1371/journal.pcbi.1004167
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004167
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1004167&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1004167?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Yumiko Yoshimura & Jami L. M. Dantzker & Edward M. Callaway, 2005. "Excitatory cortical neurons form fine-scale functional networks," Nature, Nature, vol. 433(7028), pages 868-873, February.
    2. Kenneth D. Harris & Jozsef Csicsvari & Hajime Hirase & George Dragoi & György Buzsáki, 2003. "Organization of cell assemblies in the hippocampus," Nature, Nature, vol. 424(6948), pages 552-556, July.
    3. Jonathan W. Pillow & Jonathon Shlens & Liam Paninski & Alexander Sher & Alan M. Litke & E. J. Chichilnisky & Eero P. Simoncelli, 2008. "Spatio-temporal correlations and visual signalling in a complete neuronal population," Nature, Nature, vol. 454(7207), pages 995-999, August.
    4. Edward A. Stern & Dieter Jaeger & Charles J. Wilson, 1998. "Membrane potential synchrony of simultaneously recorded striatal spiny neurons in vivo," Nature, Nature, vol. 394(6692), pages 475-478, July.
    5. James M McFarland & Yuwei Cui & Daniel A Butts, 2013. "Inferring Nonlinear Neuronal Computation Based on Physiologically Plausible Inputs," PLOS Computational Biology, Public Library of Science, vol. 9(7), pages 1-18, July.
    6. Nathaniel Urban & Shreejoy Tripathy, 2012. "Circuits drive cell diversity," Nature, Nature, vol. 488(7411), pages 289-290, August.
    7. Vladimir Ilin & Ian H Stevenson & Maxim Volgushev, 2014. "Injection of Fully-Defined Signal Mixtures: A Novel High-Throughput Tool to Study Neuronal Encoding and Computations," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-10, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Daniel Soudry & Suraj Keshri & Patrick Stinson & Min-hwan Oh & Garud Iyengar & Liam Paninski, 2015. "Efficient "Shotgun" Inference of Neural Connectivity from Highly Sub-sampled Activity Data," PLOS Computational Biology, Public Library of Science, vol. 11(10), pages 1-30, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Niru Maheswaranathan & David B Kastner & Stephen A Baccus & Surya Ganguli, 2018. "Inferring hidden structure in multilayered neural circuits," PLOS Computational Biology, Public Library of Science, vol. 14(8), pages 1-30, August.
    2. Seif Eldawlatly & Karim G Oweiss, 2011. "Millisecond-Timescale Local Network Coding in the Rat Primary Somatosensory Cortex," PLOS ONE, Public Library of Science, vol. 6(6), pages 1-14, June.
    3. Julian Rossbroich & Daniel Trotter & John Beninger & Katalin Tóth & Richard Naud, 2021. "Linear-nonlinear cascades capture synaptic dynamics," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-27, March.
    4. Guillaume Viejo & Thomas Cortier & Adrien Peyrache, 2018. "Brain-state invariant thalamo-cortical coordination revealed by non-linear encoders," PLOS Computational Biology, Public Library of Science, vol. 14(3), pages 1-25, March.
    5. Arne F Meyer & Jan-Philipp Diepenbrock & Max F K Happel & Frank W Ohl & Jörn Anemüller, 2014. "Discriminative Learning of Receptive Fields from Responses to Non-Gaussian Stimulus Ensembles," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-15, April.
    6. Jonathan Rubin & Nachum Ulanovsky & Israel Nelken & Naftali Tishby, 2016. "The Representation of Prediction Error in Auditory Cortex," PLOS Computational Biology, Public Library of Science, vol. 12(8), pages 1-28, August.
    7. Sanaya N. Shroff & Eric Lowet & Sudiksha Sridhar & Howard J. Gritton & Mohammed Abumuaileq & Hua-An Tseng & Cyrus Cheung & Samuel L. Zhou & Krishnakanth Kondabolu & Xue Han, 2023. "Striatal cholinergic interneuron membrane voltage tracks locomotor rhythms in mice," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    8. Franklin Leong & Babak Rahmani & Demetri Psaltis & Christophe Moser & Diego Ghezzi, 2024. "An actor-model framework for visual sensory encoding," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    9. Lucas Rudelt & Daniel González Marx & Michael Wibral & Viola Priesemann, 2021. "Embedding optimization reveals long-lasting history dependence in neural spiking activity," PLOS Computational Biology, Public Library of Science, vol. 17(6), pages 1-51, June.
    10. Pengcheng Zhou & Shawn D Burton & Adam C Snyder & Matthew A Smith & Nathaniel N Urban & Robert E Kass, 2015. "Establishing a Statistical Link between Network Oscillations and Neural Synchrony," PLOS Computational Biology, Public Library of Science, vol. 11(10), pages 1-25, October.
    11. Remus Oşan & Liping Zhu & Shy Shoham & Joe Z Tsien, 2007. "Subspace Projection Approaches to Classification and Visualization of Neural Network-Level Encoding Patterns," PLOS ONE, Public Library of Science, vol. 2(5), pages 1-14, May.
    12. Richard Naud & Wulfram Gerstner, 2012. "Coding and Decoding with Adapting Neurons: A Population Approach to the Peri-Stimulus Time Histogram," PLOS Computational Biology, Public Library of Science, vol. 8(10), pages 1-14, October.
    13. Fanfan Li & Dingwei Li & Chuanqing Wang & Guolei Liu & Rui Wang & Huihui Ren & Yingjie Tang & Yan Wang & Yitong Chen & Kun Liang & Qi Huang & Mohamad Sawan & Min Qiu & Hong Wang & Bowen Zhu, 2024. "An artificial visual neuron with multiplexed rate and time-to-first-spike coding," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    14. Xing, Miaomiao & Song, Xinlin & Wang, Hengtong & Yang, Zhuoqin & Chen, Yong, 2022. "Frequency synchronization and excitabilities of two coupled heterogeneous Morris-Lecar neurons," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    15. Yoav Printz & Pritish Patil & Mathias Mahn & Asaf Benjamin & Anna Litvin & Rivka Levy & Max Bringmann & Ofer Yizhar, 2023. "Determinants of functional synaptic connectivity among amygdala-projecting prefrontal cortical neurons in male mice," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    16. Johannes Burge & Priyank Jaini, 2017. "Accuracy Maximization Analysis for Sensory-Perceptual Tasks: Computational Improvements, Filter Robustness, and Coding Advantages for Scaled Additive Noise," PLOS Computational Biology, Public Library of Science, vol. 13(2), pages 1-32, February.
    17. Kenneth W. Latimer & David J. Freedman, 2023. "Low-dimensional encoding of decisions in parietal cortex reflects long-term training history," Nature Communications, Nature, vol. 14(1), pages 1-24, December.
    18. Volker Pernice & Benjamin Staude & Stefano Cardanobile & Stefan Rotter, 2011. "How Structure Determines Correlations in Neuronal Networks," PLOS Computational Biology, Public Library of Science, vol. 7(5), pages 1-14, May.
    19. Dhanya Parameshwaran & Upinder S Bhalla, 2013. "Theta Frequency Background Tunes Transmission but Not Summation of Spiking Responses," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-12, January.
    20. Braden A W Brinkman & Alison I Weber & Fred Rieke & Eric Shea-Brown, 2016. "How Do Efficient Coding Strategies Depend on Origins of Noise in Neural Circuits?," PLOS Computational Biology, Public Library of Science, vol. 12(10), pages 1-34, October.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1004167. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.