[{"publisher":"American Physiological Society","article_type":"original","page":"501-512","quality_controlled":"1","publication_status":"published","date_created":"2023-02-15T14:46:14Z","article_processing_charge":"No","department":[{"_id":"SiHi"}],"title":"Loss of ETV1/ER81 in motor neurons leads to reduced monosynaptic inputs from proprioceptive sensory neurons","intvolume":"       129","pmid":1,"_id":"12562","author":[{"last_name":"Ladle","first_name":"David R.","full_name":"Ladle, David R."},{"id":"37B36620-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-2279-1061","full_name":"Hippenmeyer, Simon","first_name":"Simon","last_name":"Hippenmeyer"}],"issue":"3","volume":129,"acknowledgement":"The authors gratefully thank Dr. Silvia Arber, University of Basel and Friedrich Miescher Institute for Biomedical Research, for support and in whose lab the data were collected. For advice on statistical analysis, we thank Michael Bottomley from the Statistical Consulting Center, College of Science and Mathematics, Wright State University.","doi":"10.1152/jn.00172.2022","day":"01","abstract":[{"text":"Presynaptic inputs determine the pattern of activation of postsynaptic neurons in a neural circuit. Molecular and genetic pathways that regulate the selective formation of subsets of presynaptic inputs are largely unknown, despite significant understanding of the general process of synaptogenesis. In this study, we have begun to identify such factors using the spinal monosynaptic stretch reflex circuit as a model system. In this neuronal circuit, Ia proprioceptive afferents establish monosynaptic connections with spinal motor neurons that project to the same muscle (termed homonymous connections) or muscles with related or synergistic function. However, monosynaptic connections are not formed with motor neurons innervating muscles with antagonistic functions. The ETS transcription factor ER81 (also known as ETV1) is expressed by all proprioceptive afferents, but only a small set of motor neuron pools in the lumbar spinal cord of the mouse. Here we use conditional mouse genetic techniques to eliminate Er81 expression selectively from motor neurons. We find that ablation of Er81 in motor neurons reduces synaptic inputs from proprioceptive afferents conveying information from homonymous and synergistic muscles, with no change observed in the connectivity pattern from antagonistic proprioceptive afferents. In summary, these findings suggest a role for ER81 in defined motor neuron pools to control the assembly of specific presynaptic inputs and thereby influence the profile of activation of these motor neurons.","lang":"eng"}],"date_updated":"2023-09-05T12:13:34Z","year":"2023","citation":{"mla":"Ladle, David R., and Simon Hippenmeyer. “Loss of ETV1/ER81 in Motor Neurons Leads to Reduced Monosynaptic Inputs from Proprioceptive Sensory Neurons.” <i>Journal of Neurophysiology</i>, vol. 129, no. 3, American Physiological Society, 2023, pp. 501–12, doi:<a href=\"https://doi.org/10.1152/jn.00172.2022\">10.1152/jn.00172.2022</a>.","short":"D.R. Ladle, S. Hippenmeyer, Journal of Neurophysiology 129 (2023) 501–512.","ista":"Ladle DR, Hippenmeyer S. 2023. Loss of ETV1/ER81 in motor neurons leads to reduced monosynaptic inputs from proprioceptive sensory neurons. Journal of Neurophysiology. 129(3), 501–512.","apa":"Ladle, D. R., &#38; Hippenmeyer, S. (2023). Loss of ETV1/ER81 in motor neurons leads to reduced monosynaptic inputs from proprioceptive sensory neurons. <i>Journal of Neurophysiology</i>. American Physiological Society. <a href=\"https://doi.org/10.1152/jn.00172.2022\">https://doi.org/10.1152/jn.00172.2022</a>","ama":"Ladle DR, Hippenmeyer S. Loss of ETV1/ER81 in motor neurons leads to reduced monosynaptic inputs from proprioceptive sensory neurons. <i>Journal of Neurophysiology</i>. 2023;129(3):501-512. doi:<a href=\"https://doi.org/10.1152/jn.00172.2022\">10.1152/jn.00172.2022</a>","chicago":"Ladle, David R., and Simon Hippenmeyer. “Loss of ETV1/ER81 in Motor Neurons Leads to Reduced Monosynaptic Inputs from Proprioceptive Sensory Neurons.” <i>Journal of Neurophysiology</i>. American Physiological Society, 2023. <a href=\"https://doi.org/10.1152/jn.00172.2022\">https://doi.org/10.1152/jn.00172.2022</a>.","ieee":"D. R. Ladle and S. Hippenmeyer, “Loss of ETV1/ER81 in motor neurons leads to reduced monosynaptic inputs from proprioceptive sensory neurons,” <i>Journal of Neurophysiology</i>, vol. 129, no. 3. American Physiological Society, pp. 501–512, 2023."},"isi":1,"external_id":{"pmid":["36695533"],"isi":["000957721600001"]},"language":[{"iso":"eng"}],"keyword":["Physiology","General Neuroscience"],"oa_version":"None","month":"03","publication":"Journal of Neurophysiology","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","status":"public","publication_identifier":{"issn":["0022-3077"],"eissn":["1522-1598"]},"date_published":"2023-03-01T00:00:00Z","type":"journal_article"},{"language":[{"iso":"eng"}],"month":"10","oa_version":"Published Version","has_accepted_license":"1","publication":"Journal of Neurophysiology","status":"public","user_id":"D865714E-FA4E-11E9-B85B-F5C5E5697425","file":[{"file_name":"2014_JNeurophysiol_Tomm.pdf","content_type":"application/pdf","date_updated":"2020-07-16T10:12:13Z","file_size":1632295,"checksum":"7c06a086da6f924342650de6dc555c3f","date_created":"2020-07-16T10:12:13Z","creator":"cziletti","file_id":"8122","relation":"main_file","success":1,"access_level":"open_access"}],"oa":1,"publication_identifier":{"eissn":["1522-1598"],"issn":["0022-3077"]},"type":"journal_article","date_published":"2014-10-15T00:00:00Z","tmp":{"name":"Creative Commons Attribution 3.0 Unported (CC BY 3.0)","image":"/images/cc_by.png","short":"CC BY (3.0)","legal_code_url":"https://creativecommons.org/licenses/by/3.0/legalcode"},"article_type":"original","publisher":"American Physiological Society","file_date_updated":"2020-07-16T10:12:13Z","quality_controlled":"1","page":"1801-1814","intvolume":"       112","title":"Connection-type-specific biases make uniform random network models consistent with cortical recordings","article_processing_charge":"No","date_created":"2020-06-25T13:08:30Z","publication_status":"published","issue":"8","author":[{"full_name":"Tomm, Christian","first_name":"Christian","last_name":"Tomm"},{"last_name":"Avermann","first_name":"Michael","full_name":"Avermann, Michael"},{"first_name":"Carl","last_name":"Petersen","full_name":"Petersen, Carl"},{"last_name":"Gerstner","first_name":"Wulfram","full_name":"Gerstner, Wulfram"},{"id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","first_name":"Tim P","last_name":"Vogels","orcid":"0000-0003-3295-6181","full_name":"Vogels, Tim P"}],"license":"https://creativecommons.org/licenses/by/3.0/","pmid":1,"_id":"8023","ddc":["570"],"extern":"1","volume":112,"abstract":[{"lang":"eng","text":"Uniform random sparse network architectures are ubiquitous in computational neuroscience, but the implicit hypothesis that they are a good representation of real neuronal networks has been met with skepticism. Here we used two experimental data sets, a study of triplet connectivity statistics and a data set measuring neuronal responses to channelrhodopsin stimuli, to evaluate the fidelity of thousands of model networks. Network architectures comprised three neuron types (excitatory, fast spiking, and nonfast spiking inhibitory) and were created from a set of rules that govern the statistics of the resulting connection types. In a high-dimensional parameter scan, we varied the degree distributions (i.e., how many cells each neuron connects with) and the synaptic weight correlations of synapses from or onto the same neuron. These variations converted initially uniform random and homogeneously connected networks, in which every neuron sent and received equal numbers of synapses with equal synaptic strength distributions, to highly heterogeneous networks in which the number of synapses per neuron, as well as average synaptic strength of synapses from or to a neuron were variable. By evaluating the impact of each variable on the network structure and dynamics, and their similarity to the experimental data, we could falsify the uniform random sparse connectivity hypothesis for 7 of 36 connectivity parameters, but we also confirmed the hypothesis in 8 cases. Twenty-one parameters had no substantial impact on the results of the test protocols we used."}],"day":"15","doi":"10.1152/jn.00629.2013","external_id":{"pmid":["24944218"]},"year":"2014","citation":{"mla":"Tomm, Christian, et al. “Connection-Type-Specific Biases Make Uniform Random Network Models Consistent with Cortical Recordings.” <i>Journal of Neurophysiology</i>, vol. 112, no. 8, American Physiological Society, 2014, pp. 1801–14, doi:<a href=\"https://doi.org/10.1152/jn.00629.2013\">10.1152/jn.00629.2013</a>.","short":"C. Tomm, M. Avermann, C. Petersen, W. Gerstner, T.P. Vogels, Journal of Neurophysiology 112 (2014) 1801–1814.","ista":"Tomm C, Avermann M, Petersen C, Gerstner W, Vogels TP. 2014. Connection-type-specific biases make uniform random network models consistent with cortical recordings. Journal of Neurophysiology. 112(8), 1801–1814.","apa":"Tomm, C., Avermann, M., Petersen, C., Gerstner, W., &#38; Vogels, T. P. (2014). Connection-type-specific biases make uniform random network models consistent with cortical recordings. <i>Journal of Neurophysiology</i>. American Physiological Society. <a href=\"https://doi.org/10.1152/jn.00629.2013\">https://doi.org/10.1152/jn.00629.2013</a>","ama":"Tomm C, Avermann M, Petersen C, Gerstner W, Vogels TP. Connection-type-specific biases make uniform random network models consistent with cortical recordings. <i>Journal of Neurophysiology</i>. 2014;112(8):1801-1814. doi:<a href=\"https://doi.org/10.1152/jn.00629.2013\">10.1152/jn.00629.2013</a>","ieee":"C. Tomm, M. Avermann, C. Petersen, W. Gerstner, and T. P. Vogels, “Connection-type-specific biases make uniform random network models consistent with cortical recordings,” <i>Journal of Neurophysiology</i>, vol. 112, no. 8. American Physiological Society, pp. 1801–1814, 2014.","chicago":"Tomm, Christian, Michael Avermann, Carl Petersen, Wulfram Gerstner, and Tim P Vogels. “Connection-Type-Specific Biases Make Uniform Random Network Models Consistent with Cortical Recordings.” <i>Journal of Neurophysiology</i>. American Physiological Society, 2014. <a href=\"https://doi.org/10.1152/jn.00629.2013\">https://doi.org/10.1152/jn.00629.2013</a>."},"date_updated":"2021-01-12T08:16:35Z"},{"publication":"Journal of Neurophysiology","oa_version":"None","month":"07","language":[{"iso":"eng"}],"type":"journal_article","date_published":"2000-07-01T00:00:00Z","publication_identifier":{"issn":["0022-3077"]},"publist_id":"2854","user_id":"ea97e931-d5af-11eb-85d4-e6957dddbf17","status":"public","pmid":1,"_id":"3532","issue":"1","author":[{"full_name":"Henze, Darrell","first_name":"Darrell","last_name":"Henze"},{"last_name":"Borhegyi","first_name":"Zsolt","full_name":"Borhegyi, Zsolt"},{"first_name":"Jozsef L","last_name":"Csicsvari","orcid":"0000-0002-5193-4036","full_name":"Csicsvari, Jozsef L","id":"3FA14672-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Mamiya, Akira","last_name":"Mamiya","first_name":"Akira"},{"full_name":"Harris, Kenneth","first_name":"Kenneth","last_name":"Harris"},{"full_name":"Buzsáki, György","first_name":"György","last_name":"Buzsáki"}],"article_processing_charge":"No","date_created":"2018-12-11T12:03:49Z","publication_status":"published","intvolume":"        84","title":"Intracellular features predicted by extracellular recordings in the hippocampus in vivo","quality_controlled":"1","page":"390 - 400","publisher":"American Physiological Society","article_type":"original","year":"2000","citation":{"short":"D. Henze, Z. Borhegyi, J.L. Csicsvari, A. Mamiya, K. Harris, G. Buzsáki, Journal of Neurophysiology 84 (2000) 390–400.","mla":"Henze, Darrell, et al. “Intracellular Features Predicted by Extracellular Recordings in the Hippocampus in Vivo.” <i>Journal of Neurophysiology</i>, vol. 84, no. 1, American Physiological Society, 2000, pp. 390–400, doi:<a href=\"https://doi.org/10.1152/jn.2000.84.1.390\">10.1152/jn.2000.84.1.390</a>.","ista":"Henze D, Borhegyi Z, Csicsvari JL, Mamiya A, Harris K, Buzsáki G. 2000. Intracellular features predicted by extracellular recordings in the hippocampus in vivo. Journal of Neurophysiology. 84(1), 390–400.","apa":"Henze, D., Borhegyi, Z., Csicsvari, J. L., Mamiya, A., Harris, K., &#38; Buzsáki, G. (2000). Intracellular features predicted by extracellular recordings in the hippocampus in vivo. <i>Journal of Neurophysiology</i>. American Physiological Society. <a href=\"https://doi.org/10.1152/jn.2000.84.1.390\">https://doi.org/10.1152/jn.2000.84.1.390</a>","ama":"Henze D, Borhegyi Z, Csicsvari JL, Mamiya A, Harris K, Buzsáki G. Intracellular features predicted by extracellular recordings in the hippocampus in vivo. <i>Journal of Neurophysiology</i>. 2000;84(1):390-400. doi:<a href=\"https://doi.org/10.1152/jn.2000.84.1.390\">10.1152/jn.2000.84.1.390</a>","chicago":"Henze, Darrell, Zsolt Borhegyi, Jozsef L Csicsvari, Akira Mamiya, Kenneth Harris, and György Buzsáki. “Intracellular Features Predicted by Extracellular Recordings in the Hippocampus in Vivo.” <i>Journal of Neurophysiology</i>. American Physiological Society, 2000. <a href=\"https://doi.org/10.1152/jn.2000.84.1.390\">https://doi.org/10.1152/jn.2000.84.1.390</a>.","ieee":"D. Henze, Z. Borhegyi, J. L. Csicsvari, A. Mamiya, K. Harris, and G. Buzsáki, “Intracellular features predicted by extracellular recordings in the hippocampus in vivo,” <i>Journal of Neurophysiology</i>, vol. 84, no. 1. American Physiological Society, pp. 390–400, 2000."},"date_updated":"2023-05-02T14:31:13Z","external_id":{"pmid":["10899213"]},"day":"01","doi":"10.1152/jn.2000.84.1.390","abstract":[{"lang":"eng","text":"Multichannel tetrode array recording in awake behaving animals provides a powerful method to record the activity of large numbers of neurons. The power of this method could be extended if further information concerning the intracellular state of the neurons could be extracted from the extracellularly recorded signals. Toward this end, we have simultaneously recorded intracellular and extracellular signals from hippocampal CA1 pyramidal cells and interneurons in the anesthetized rat. We found that several intracellular parameters can be deduced from extracellular spike waveforms. The width of the intracellular action potential is defined precisely by distinct points on the extracellular spike. Amplitude changes of the intracellular action potential are reflected by changes in the amplitude of the initial negative phase of the extracellular spike, and these amplitude changes are dependent on the state of the network. In addition, intracellular recordings from dendrites with simultaneous extracellular recordings from the soma indicate that, on average, action potentials are initiated in the perisomatic region and propagate to the dendrites at 1.68 m/s. Finally we determined that a tetrode in hippocampal area CA1 theoretically should be able to record electrical signals from similar to 1,000 neurons. Of these, 60-100 neurons should generate spikes of sufficient amplitude to be detectable from the noise and to allow for their separation using current spatial clustering methods. This theoretical maximum is in contrast to the approximately six units that are usually detected per tetrode. From this, we conclude that a large percentage of hippocampal CA1 pyramidal cells are silent in any given behavioral condition."}],"acknowledgement":"We thank M. Recce for comments on the manuscript and J. Hetke and K.Wise for supplying us with the silicon probes (1P41RR09754).This work was supported by National Institutes of Health Grants NS-34994,MH-54671,  and  MH-12403  (to  D. A. Henze), the Epilepsy Foundation of American (D. A.Henze), and an Eotvos fellowship (Z. Borhegyi).","volume":84,"extern":"1"},{"extern":"1","volume":84,"acknowledgement":"The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked ‘‘advertisement’ ’in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. We thank R. Bruno for performing cluster analysis and drawing our attention to the AutoClass program, M. Recce and P. Mitra for suggestions withdata  analysis and comments on the manuscript, C. King, G. Dragoi, and X.Leinekugel for performing  cluster analysis, and  J. Hetke and K. Wise for supplying silicon probes. The data used in this paper are available on request by e-mail to G. Buzsaki. This work was supported by National Institutes of Health Grants NS-34994,413 MH-54671, and MH-12403 (to D. A. Henze) and by the Epilepsy Foundationof America (to D. A. Henze).","abstract":[{"lang":"eng","text":"Simultaneous recording from large numbers of neurons is a prerequisite for understanding their cooperative behavior. Various recording techniques and spike separation methods are being used toward this goal. However, the error rates involved in spike separation have not yet been quantified. We studied the separation reliability of “tetrode” (4-wire electrode) recorded spikes by monitoring simultaneously from the same cell intracellularly with a glass pipette and extracellularly with a tetrode. With manual spike sorting, we found a trade-off between Type I and Type II errors, with errors typically ranging from 0 to 30% depending on the amplitude and firing pattern of the cell, the similarity of the waveshapes of neighboring neurons, and the experience of the operator. Performance using only a single wire was markedly lower, indicating the advantages of multiple-site monitoring techniques over single-wire recordings. For tetrode recordings, error rates were increased by burst activity and during periods of cellular synchrony. The lowest possible separation error rates were estimated by a search for the best ellipsoidal cluster shape. Human operator performance was significantly below the estimated optimum. Investigation of error distributions indicated that suboptimal performance was caused by inability of the operators to mark cluster boundaries accurately in a high-dimensional feature space. We therefore hypothesized that automatic spike-sorting algorithms have the potential to significantly lower error rates. Implementation of a semi-automatic classification system confirms this suggestion, reducing errors close to the estimated optimum, in the range 0-8%."}],"day":"01","doi":"10.1152/jn.2000.84.1.401","external_id":{"pmid":["10899214 "]},"citation":{"apa":"Harris, K., Henze, D., Csicsvari, J. L., Hirase, H., &#38; Buzsáki, G. (2000). Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements. <i>Journal of Neurophysiology</i>. American Physiological Society. <a href=\"https://doi.org/10.1152/jn.2000.84.1.401\">https://doi.org/10.1152/jn.2000.84.1.401</a>","ama":"Harris K, Henze D, Csicsvari JL, Hirase H, Buzsáki G. Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements. <i>Journal of Neurophysiology</i>. 2000;84(1):401-414. doi:<a href=\"https://doi.org/10.1152/jn.2000.84.1.401\">10.1152/jn.2000.84.1.401</a>","ieee":"K. Harris, D. Henze, J. L. Csicsvari, H. Hirase, and G. Buzsáki, “Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements,” <i>Journal of Neurophysiology</i>, vol. 84, no. 1. American Physiological Society, pp. 401–414, 2000.","chicago":"Harris, Kenneth, Darrell Henze, Jozsef L Csicsvari, Hajima Hirase, and György Buzsáki. “Accuracy of Tetrode Spike Separation as Determined by Simultaneous Intracellular and Extracellular Measurements.” <i>Journal of Neurophysiology</i>. American Physiological Society, 2000. <a href=\"https://doi.org/10.1152/jn.2000.84.1.401\">https://doi.org/10.1152/jn.2000.84.1.401</a>.","mla":"Harris, Kenneth, et al. “Accuracy of Tetrode Spike Separation as Determined by Simultaneous Intracellular and Extracellular Measurements.” <i>Journal of Neurophysiology</i>, vol. 84, no. 1, American Physiological Society, 2000, pp. 401–14, doi:<a href=\"https://doi.org/10.1152/jn.2000.84.1.401\">10.1152/jn.2000.84.1.401</a>.","short":"K. Harris, D. Henze, J.L. Csicsvari, H. Hirase, G. Buzsáki, Journal of Neurophysiology 84 (2000) 401–414.","ista":"Harris K, Henze D, Csicsvari JL, Hirase H, Buzsáki G. 2000. Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements. Journal of Neurophysiology. 84(1), 401–414."},"year":"2000","date_updated":"2023-05-02T14:16:45Z","article_type":"original","publisher":"American Physiological Society","quality_controlled":"1","page":"401 - 414","intvolume":"        84","title":"Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements","date_created":"2018-12-11T12:03:54Z","article_processing_charge":"No","publication_status":"published","issue":"1","author":[{"first_name":"Kenneth","last_name":"Harris","full_name":"Harris, Kenneth"},{"full_name":"Henze, Darrell","first_name":"Darrell","last_name":"Henze"},{"id":"3FA14672-F248-11E8-B48F-1D18A9856A87","last_name":"Csicsvari","first_name":"Jozsef L","full_name":"Csicsvari, Jozsef L","orcid":"0000-0002-5193-4036"},{"full_name":"Hirase, Hajima","last_name":"Hirase","first_name":"Hajima"},{"full_name":"Buzsáki, György","first_name":"György","last_name":"Buzsáki"}],"pmid":1,"_id":"3548","status":"public","user_id":"ea97e931-d5af-11eb-85d4-e6957dddbf17","publist_id":"2837","publication_identifier":{"issn":["0022-3077"]},"type":"journal_article","date_published":"2000-07-01T00:00:00Z","language":[{"iso":"eng"}],"month":"07","oa_version":"None","publication":"Journal of Neurophysiology"}]