The Team




Video & Pictures







  • I. Segev. The propagation of action potentials along bifurcating axons. M.Sc Thesis. The Hebrew University of Jerusalem (1976).

  • I. Segev. The behavior of subthreshold potentials in a neuron with a nonlinear membrane. Ph.D. thesis. The Hebrew University of Jerusalem (1982).


  • Parnas, I. and Segev, I. (1979). A mathematical model of conduction of action potentials along bifurcating axons. J. Physiol. 295: 323-343. PDF(2.2MB)

  • Segev, I. and Parnas, I. (1983). Synaptic integration mechanisms: A theoretical and experimental investigation of temporal postsynaptic interaction between excitatory and inhibitory inputs. Biophys. J. 41: 41-50 PDF(1.7MB)

  • Segev, I. Fleshman, J.W., Bunow, B. and Miller, J. P. (1985). Modeling the electrical behavior of anatomically complex neurons using a network analysis program: Passive membrane. Biol. Cyber. 53: 27-40. PDF(1.5MB)

  • Bunow, B., Segev, I. and Fleshman, J.W. (1985). Modeling the electrical behavior of anatomically complex neurons using a network analysis program: Excitable membrane. Biol. Cyber. 53: 40-56.PDF(1.5MB)

  • Segev, I. and Parnas, I. (1985). Nonlinear cable properties of the giant axon of the cockroach Periplaneta Americana. J. Gen. Physiol. 85: 729-741. PDF(700KB)

  • Shepherd, G.M., Brayton, R.K., Miller, J.P., Segev, I., Rinzel, J. and Rall, W. (1985). Signal enhancement in distal cortical dendrites by means of interactions between active dendritic spines. Proc. Natl. Acad. Sci. 82: 2192-2195. PDF (624 KB)

  • Fleshman, J. W., Segev, I. and Burke, R. E. (1988). Electrotonic architecture of type-identified a-motoneurons in the cat spinal cord. J. Neurophysiol. 60: 60-85.

  • Segev, I. and Rall, W. (1988). Computational study of an excitable dendritic spine. J. Neurophysiol. 60: 499-523.

  • Burke, R. E., Fleshman, J. W. and Segev, I. (1988-1989). Factors that control the efficacy of group Ia synapses in -motoneurons. J. Physiol. (Paris), 83: 133-140.

  • Nitzan, R., Segev, I. and Yarom, Y. (1990). Voltage behavior along the irregular dendritic structure of morphologically and physiologically characterized vagal motoneurons of the guinea pig. J. Neurophysiol. 63: 333-346.

  • Segev, I. (1990). Computer study of presynaptic inhibition controlling the spread of action potentials into axonal terminals. J. Neurophysiol. 63:987-998. PDF (2.8MB)"

  • Segev, I., Fleshman, J. W., and Burke, R. E. (1990) Computer simulation of group Ia EPSPs using morphologically realistic models of cat a-motoneurons. J. Neurophysiol 64: 648-660.

  • Manor, Y., Gonczarowski, Y., and Segev, I. (1991) Propagation of action potentials along complex axonal tree: Model and implementation. Biophys. J. 60: 1411-1423.

  • Manor, Y., Koch, C., and Segev, I. (1991) Effect of geometrical irregularities on propagation delay in axonal trees. Biophys. J. 60: 1424-1437.

  • Rapp, M., Yarom, Y., and Segev, I. (1992) The impact of parallel fiber background activity on the cable properties of cerebellar Purkinje cells. Neural Computation 4: 518-532. PDF (3.1 MB)

  • Rall, W., Burke, R. E., Holmes, W. R., Jack. J. J. B., Redman, S. J., and Segev, I. (1992) Matching dendritic neuron models to experimental data. Physiological Reviews. S159-S186.PDF (7.4 MB)

  • Holmes, W. R., Segev, I., and Rall, W. (1992). Interpretation of time constant and electrotonic length estimate of multi-cylinder or branched neuronal structures. J. Neurophysiol 68:1401-1420.

  • Segev, I. (1992) Single neurone models: oversimple, complex and reduced. TINS 15: 414-421.

  • Agmon-Snir, H., and Segev, I. (1993) Signal delay and input synchronization in passive dendritic structures. J. Neurophys. 70 (5): 2066-2085. PDF(5.5 MB)

  • Rapp, M., Segev, I., and Yarom, Y. (1994) Physiology, morphology and detailed passive models of cerebellar Purkinje cells J. Physiol. 474: 101-118.

  • Segev, I., Friedman, A., White, E. and Gutnick, M. (1995) Electrical consequences of spine dimensions in a model of a cortical spiny stellate cell completely reconstructed from serial thin sections. J. Comput. Neurosci. 2(2):117-130. PDF (4.1 MB)

  • Gutfreund, Y., Yarom, Y. and Segev, I. (1995) Subthreshold oscillations and resonant frequency in guinea pig cortical neurons: physiology and modeling. J. Physiol. 483:621-639.

  • Zador, A., Agmon-Snir, H. and Segev, I. (1995) The Morphoelectrotonic Transform: A Graphical Approach to Dendritic Function. J. Neurosci. 15:1669-1682.

  • Koch, C., Rapp, M. and Segev, I. (1996) A Brief History of Time (Constants). Cerebral Cortex 6:93-101. PDF (1.5 MB)

  • Rapp, M., Yarom, Y., and Segev, I. (1996). Modeling back propagating action potential in weakly excitable dendrites of neocortical pyramidal cells. Proc. Natl. Acad. of Science 93:11985-11990. PDF (289 KB)

  • Gutfreund, Y., Yarom, Y., Segev, I., Flash, T., and Hochner, B. (1996). Organization of octopus arm movement: A model system for studying the control of flexible arm. J. Neurosci. 16:7297-7307. PDF (626 KB)

  • Manor, Y., Rinzel, J., Segev, I., and Yarom, Y. (1997). Low amplitude oscillations in the inferior olive: A model based on electrical coupling of neurons with heterogeneous channel density. J. Neurophys.:77: 2736-2752. PDF (350 KB)

  • Schneidman E., Freedman B., and Segev, I. (1998). Ion channel stochasticity may be critical in determining the reliability and precision of spike timing. Neural Computation 10: 1679-1694. PDF (1207 KB)

  • Senn, W., Segev, I., and Tsodyks, M. (1998). Reading neural synchrony with depressing synapses. Neural Computation 10: 815-819. PDF (460 KB)

  • Segev, I., and Rall W. (1998). Excitable dendrites and spines: earlier theoretical insights elucidate recent direct observations. TINS Vol. 21 No. 11: 453-459. PDF (3,984 KB)

  • Segev, I., (1998) Sound grounds for computing dendrites. Nature, Vol. 393: 207-208 (News & Views article) PDF (342 KB)

  • Segev, I. and Schneidman E. (1999). Axons as computing devices: Basic insights gained from models. J. Physiol (Paris) 93:263-270.

  • Anderson, J.C., Binzegger, T., Kahana, O., Martin, K.A.C., and Segev, I. (1999). Dendritic asymmetry cannot account for directional responses of neurons in visual cortex. Nature Neuroscience 2: 820-824. PDF (962KB)

  • London, M., Meunier, C., and Segev, I. (1999). Signal transfer in passive dendrites with non-uniform membrane conductance. J. Neurosci. 19: 8219-8233. PDF (300 KB)

  • Segev, I. (1999). Taming time in the olfactory bulb, Nature Neuroscience, 2: 1041-1043 (News & Views article). PDF (148 KB)

  • Schneidmann, E., Segev, I., and Tishby N. (2000). Information capacity and robustness of stochastic neuron models. NIPS S.A. Solla, T.K. Leen and K.-R. Muller (eds.), pp. 178 -184

  • Segev, I, London, M. (2000). Untangling dendrites with quantitative models. Science. 290: 744-50. PDF (739 KB)

  • Steinmetz, P.N, Manwani, A., Koch., C., London, M., and Segev I. (2000). Subthreshold voltage noise due to channel fluctuations in active neuronal membranes. J. Comput. Neurosci. 9(2): 133-48. PDF (155 KB)

  • Koch C., and Segev, I. (2000). The role of single neurons in information processing. Nature Neuroscience supp. 3: 1171-1177. PDF (481 KB)

  • London M., and Segev I.(2001) Synaptic scaling in vitro and in vivo. Nature Neuroscience, 4(9):853-855. PDF (539 KB)

  • Fuhrmann, G., Segev, I., Markram, H., and Tsodyks, M. (2002). Coding of Temporal Information by Activity-Dependent Synapses. J. Neurophys. 87(1):140-8. PDF (134 KB)

  • London M, Shribman A, Hausser M, Larkum M and Segev I.(2002). The information efficacy of a synapse. Nature Neuroscience, 5(4):333-340. PDF(945 KB)

  • Meunier C. and Segev I.(2002). Playing the devil's advocate: Is the Hodgkin-Huxley model useful? TINS 25(11): 558-563. PDF(142 KB)

  • Litvak V, Sompolinsky H, Segev I, and Abeles M.(2003). On the transmission of rate code in long feedforward networks with exitatory-inhibitory balance. J. Neurosci. 23: 3006-3015. PDF(610 KB)

  • Segev I. (2003) Synchrony is stubborn in feedforword cortical networks. Nature Neuroscience, 6(6):543-544. PDF(133 KB)

  • Fuhrmann G, Cowan A, Segev I, Tsodyks M, Stricker C (2004). Multiple mechanisms govern the dynamics of depression at neocortical synapses of young rats. J Physiol. Jun 1;557(Pt 2):415-38. PDF(536 KB)

  • London M, Segev I.(2004). synaptic music in dendrites. Nat Neurosci. 2004 Sep;7(9):904-5. PDF(104 KB)

  • Jacobson GA, Diba K, Yaron-Jakoubovitch A, Oz Y, Koch C, Segev I, Yarom Y (2005). voltage noise of rat neocortical pyramidal neurones. J Physiol. Apr 1;564(Pt 1):145-60. PDF(647 KB)

  • Banitt Y, Martin KA, Segev I (2005). Depressed responses of facilitatory synapses. J Neurophysiol. Jul;94(1):865-70. PDF(1.3 MB)

  • Segev I (2006). What do dendrites and their synapses tell the neuron? JN 95:1295-1297,2006. doi:10.1152/classicessays.00039.2005 PDF(79 KB)

  • Diba K, Koch C and Segev I. (2006). Spike propagation in dendrites with stochastic ion channels. J Comput Neurosci (2006) 20: 77-84. DOI 10.1007/s10870-006-4770-0 PDF(408 KB)

  • Rabinowitch I. and Segev I. (2006). The Interplay Between Homeostatic Synaptic Plasticity and Functional Dendritic Compartments. J Neurophysiol 96:276-283, 2006. First published Mar 22, 2006; doi:10.1152/jn.00074.2006 PDF(4.9 MB)

  • Rabinowitch I. and Segev I. (2006). The Endurance and Selectivity of Spatial Patterns of Long-Term Potentiation/Depression in Dendrites under Homeostatic Synaptic Plasticity, The Journal of Neuroscience, December 27,2006-26(52):13474-13484 PDF(473 KB)

  • Cuntz H, Haag J, Forstner F, Segev I, Borst A (2007). Robust coding of flow-field parameters by axo-axonal gap junctions between fly visual interneurons, PNAS published online Jun 5, 2007; doi:10.1073/pnas.0703697104 PDF(1.7 MB)

  • Cuntz H, Borst A, Segev I, (2007). Optimization principles of dendritic structure, Published: 8 June 2007, Theoretical Biology and Medical Modelling 2007, 4:21, doi:10.1186/1742-4682-4-21 PDF(1.1 MB)

  • Banitt Y, Martin K, Segev I., (2007). A Biologically Realistic Model of Contrast Invariant Orientation Tuning by Thalamocortical Synaptic Depression, The Journal of Neuroscience, September 19, 2007 - 27(38):10230-10239 PDF(641 KB)

  • Sarid, L., Bruno R., Sakmann B., Segev I., and Feldmeyer D., (2007). Modeling a L4-to-L2/3 module of a single column in rat neocortex - interweaving in vitro and in vivo experimental observations. Proc. Natl. Acad. Sci. USA, 104: 16353-16358 PDF(1.2 MB)

  • Druckmann, S., Banitt, Y., Gideon, A., Schurmann, F., Markram, H. and Segev I., (2007). A Novel Multiple Objective Optimization Framework for Automated Constraining of Conductance-Based Neuron Models by Noisy Experimental Data. Frontiers in Neuroscience, Vol. 1, iss. 1, 7-18 PDF(572 KB)

  • Rabinowitch I. and Segev I., (2008). Two opposing plasticity mechanisms pulling a single synapse. Trends in Neurosciences Vol.31 No.8, 377-383 PDF(759 KB)

  • Yaron-Jakoubovitch A., Jacobson G., Koch Ch., Segev I. and Yarom Y., (2008). A paradoxical isopotentiality: a spatially uniform noise spectrum in neocortical pyramidal cells, August 2008 | Volume 2 | Article 3 | 1-9 | www.frontiersin.org PDF(1.1 MB)

  • Druckmann S, Berger TK, Hill S, Schurmann F, Markram H, Segev I. (2008). Evaluating automated parameter constraining procedures of neuron models by experimental and surrogate data. Biol Cybern. 2008 Nov;99(4-5):371-9. PDF(287 KB)

  • Gidon A. and Segev I., (2009). Spike-Timing-Dependent Synaptic Plasticity and Synaptic Democracy in Dendrites. J Neurophysiol 101:3226-3234, 2009. First published Apr 8, 2009; doi:10.1152/jn.91349.2008. PDF(932 KB)

  • Bar Ilan L., Gidon A. and Segev I., Interregional synaptic competition in neurons with multiple STDP-inducing signals. J Neurophysiol 105:989-998, 2011. First published 1 December 2010; doi:10.1152/jn.00612.2010 PDF(609 KB)

  • Etay Hay, Sean Hill2, Felix Schurmann, Henry Markram, Idan Segev, Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties. PLoS Computational Biology July 2011 | Volume 7 | Issue 7 | e1002107 PDF(609 KB)

  • Druckmann, S, Berger TK, Schurmann F, Hill S, Markram H, Segev I. 2011. Effective Stimuli for Constructing Reliable Neuron Models. {PLoS} Comput Biol. 7:e1002133. PDF(1694KB)

  • Nowik I, Zamir Sh, Segev I. 2012. Losing the battle but winning the war: game theoretic analysis of the competition between motoneurons innervating a skeletal muscle. Frontiers in Computational Neuroscience, Volume 6, Article 16. doi: 10.3389/fncom.2012.00016 PDF(930KB)

  • Torben-Nielsen B., Segev I., Yosef Yarom. 2012. The Generation of Phase Differences and Frequency Changes in a Network Model of Inferior Olive Subthreshold Oscillations. PLoS Computational Biology, July 2012 | Volume 8 | Issue 7 | e1002580 PDF(1.2MB)

  • Gidon A.,Segev I., Principles Governing the Operation of Synaptic Inhibition in Dendrites, Neuron 75, 330-341, July 26, 2012 PDF(2.0MB) News&Views

  • Druckmann Sh., Hill S., Schurmann F., Markram H., and Segev I., A Hierarchical Structure of Cortical Interneuron Electrical Diversity Revealed by Automated Statistical Analysis. Cerebral Cortex Advance Access published September 17, 2012 PDF(6.5MB)

  • Etay Hay, Felix Schurmann, Henry Markram and Idan Segev, Preserving axosomatic spiking features despite diverse dendritic morphology. J Neurophysiol 109: 2972-2981, 2013. March 27, 2013; doi:10.1152/jn.00048.2013. PDF(1.9MB)

  • Lital Bar-Ilan, Albert Gidon and Idan Segev, The role of dendritic inhibition in shaping the plasticity of excitatory synapses, Frontiers in NEURAL CIRCUITS, doi: 10.3389/fncir.2012.00118, published: 03 April 2013. PDF(1.4MB)

  • Anat Yaron-Jakoubovitch, Christof Koch, Idan Segev, and Yosef Yarom, The unimodal distribution of subthreshold, ongoing activity in cortical networks, Frontiers In Neural Circuits, published: 11 July 2013, doi: 10.3389/fncir.2013.00116. PDF(1.2MB)

  • Leora Sarid; Dirk Feldmeyer; Albert Gidon; Bert Sakmann; Idan Segev; Contribution of Intracolumnar Layer 2/3-to-Layer 2/3 Excitatory Connections in Shaping the Response to Whisker Deflection in Rat Barrel Cortex. Cerebral Cortex 2013; doi: 10.1093/cercor/bht268 PDF(920KB)

  • Segev Idan and Schurmann Felix (2013). Brain projects think big. in: Frontiers for young minds. PDF(1.6MB)

  • Guy Eyal, Huibert D. Mansvelder, Christiaan P.J. de Kock, and Idan Segev, Dendrites Impact the Encoding Capabilities of the Axon, The Journal of Neuroscience, June 11, 2014 -34(24):8063-8071 - 8063 PDF(1.7MB)

  • Idan Segev, Luis M. Martinez and Robert J. Zatorre, Brain and Art, published: 27 June 2014, doi: 10.3389/fnhum.2014.00465. PDF(60KB)

  • Jonathan Laudanski, Benjamin Torben-Nielsen, Idan Segev, Shihab Shamma, Spatially Distributed Dendritic Resonance Selectively Filters Synaptic Input. PLOS Computational Biology August 2014 | Volume 10 | Issue 8 | e1003775 PDF(2.3MB)

  • Etay Hay, Idan Segev, Dendritic Excitability and Gain Control in Recurrent Cortical Microcircuits, Cerebral Cortex Advance Access published September 9, 2014, doi:10.1093/cercor/bhu200 PDF(1.1MB)

  • Mohan H, et al (2015). Dendritic and Axonal Architecture of Individual Pyramidal Neurons across Layers of Adult Human Neocortex. Cerebral Cortex, August 2015. PDF(21.1MB)

  • Markram H et al., (2015). Reconstruction and Simulation of Neocortical Microcircuitry. Cell, October 2015. PDF(14.1MB)

  • Ramaswamy S. E et al (2015). The neocortical microcircuit collaboration portal: a resource for rat somatosensory cortex. frontiers in Neural Circuits, October 2015. PDF(4.9MB)

  • Amsalem O., Van Geit W., Muller E., Markram H., Segev I. (2016), From Neuron Biophysics to Orientation Selectivity in Electrically Coupled Networks of Neocortical L2/3 Large Basket Cells. Cerebral Cortex Advance Access published June 9, 2016. PDF(1.1MB)

  • Rössert Ch., Pozzorini Ch., Chindemi G., Davison A. P., Eroe C., King J., Newton T. H., Nolte M. Ramaswamy S., Reimann M. W., Gewaltig M. O., Gerstner W. Markram H., Segev I., Muller E., (2016) Automated point-neuron simplification of data-driven microcircuit models. PDF(5.9MB)

  • Van Geit W., Gevaert M., Chindemi G., Rössert Ch., Courcol J. D., Muller E., Schürmann F., Segev I., Markram H., (2016), BluePyOpt: Leveraging open source software and cloud infrastructure to optimise model parameters in neuroscience. PDF(2.1MB)

  • DeFelipe J., Douglas R. J., Hill S. L., Lein E. S., Martin K. A. C., Rockland K. S., Segev I., Shepherd G. M., Tamás G., (2016), Comments and General Discussion on "The Anatomical Problem Posed by Brain Complexity and Size: A Potential Solution". Front. Neuroanat., 10 June 2016 | http://dx.doi.org/10.3389/fnana.2016.00060 PDF(0.5MB)

  • Guy Eyal, Matthijs B Verhoog, Guilherme Testa-Silva, Yair Deitcher, Johannes C Lodder, Ruth Benavides-Piccione, Juan Morales, Javier DeFelipe, Christiaan PJ de Kock, Huibert D Mansvelder, Idan Segev,(2016) Unique membrane properties and enhanced signal processing in human neocortical neurons. Eyal et al. eLife 2016;5:e16553. DOI: 10.7554/eLife.16553. PDF(6MB)

  • Eyal Gal, Michael London, Amir Globerson, Srikanth Ramaswamy, Michael W Reimann, Eilif Muller, Henry Markram & Idan Segev, (2017) Rich cell-type-specific network topology in neocortical microcircuitry. Nature Neuroscience, VOLUME 20 | NUMBER 7 | JULY 2017 PDF(3.1MB)

  • Yair Deitcher, Guy Eyal1, Lida Kanari, Matthijs B. Verhoog, Guy Antoine Atenekeng Kahou, Huibert D. Mansvelder, Christiaan P.J. de Kock, and Idan Segev, (2017) Comprehensive Morpho-Electrotonic Analysis Shows 2 Distinct Classes of L2 and L3 Pyramidal Neurons in Human Temporal Cortex. Cerebral Cortex, 2017; 1-17, doi: 10.1093/cercor/bhx226 PDF(1.7MB)

  • Michael Doron, Giuseppe Chindemi, Eilif Muller, Henry Markram, Idan Segev, (2017) Timed Synaptic Inhibition Shapes NMDA Spikes, Influencing Local Dendritic Processing and Global I/O Properties of Cortical Neurons. Doron et al., 2017, Cell Reports 21, 1550-1561 PDF(3.9MB)

  • Siwei Wang, Alexander Borst, Noga Zaslavsky, Naftali Tishby, Idan Segev, (2017), Efficient encoding of motion is mediated by gap junctions in the fly visual system, PLOS Computational Biology | journal.pcbi.1005846, December 4, 2017 PDF(3.8MB)

  • Ohad Dan, Elizabeth Hopp, Alexander Borst, Idan Segev, (2018), Non-uniform weighting of local motion inputs underlies dendritic computation in the fly visual system, SCIENTIFIC REPORTS | (2018) 8:5787 | DOI:10.1038/s41598-018-23998-9, PDF(2.2MB)

  • Guy Eyal, Matthijs B. Verhoog, Guilherme Testa-Silva, Yair Deitcher, Ruth Benavides-Piccione, Javier DeFelipe, Christiaan P. J. de Kock, Huibert D. Mansvelder, Idan Sege, (2018), Human Cortical Pyramidal Neurons: From Spines to Spikes via Models, published: 29 June 2018 doi: 10.3389/fncel.2018.00181, PDF(4.3MB)
    See also: Scientists have built an artificial human brain cell

  • Wilfrid Rall (1922-2018), Neuron 99, September 5, 2018 PDF(320KB)

  • Toviah Moldwin, Idan Segev, (2018), Perceptron learning and classification in a modeled cortical pyramidal cell, bioRxiv preprint first posted online Nov. 9, 2018; doi: http://dx.doi.org/10.1101/464826. PDF(1.3MB)

  • Yaara Lefler, Oren Amsalem, Idan Segev, Yosef Yarom,(2018), Using subthreshold events to characterize the functional architecture of electrically coupled networks. bioRxiv preprint first posted online Nov. 29, 2018; doi: http://dx.doi.org/10.1101/483156 PDF(2.6MB)

  • David Beniaguev, Idan Segev, Michael London. Single Cortical Neurons as Deep Artificial Neural Networks. bioRxiv preprint first posted online Apr. 18, 2019; https://doi.org/10.1101/613141

  • Eyal Gal, Rodrigo Perin, Henry Markram, Michael London, Idan Segev, Neuron Geometry Underlies a Universal Local Architecture in Neuronal Networks. bioRxiv preprint first posted online May. 31, 2019 https://doi.org/10.1101/656058

  • Oren Amsalem, Guy Eyal, Noa Rogozinski, Michael Gevaert, Pramod Kumbhar, Felix Schürman & Idan Segev, An efficient analytical reduction of detailed nonlinear neuron models, Nature Communications volume 11, Article number: 288 (2020), PDF(13.MB)

  • Yaara Lefler, Oren Amsalem, Nora Vrieler, Idan Segev, Yosef Yarom, Using subthreshold events to characterize the functional architecture of the electrically coupled inferior olive network, eLife 9:e43560 (2020), PDF(5.7MB)

  • Iascone et al., Whole-Neuron Synaptic Mapping Reveals Spatially Precise Excitatory/Inhibitory Balance Limiting Den- dritic and Somatic Spiking, Neuron (2020), PDF(4.5MB)

  • Giuseppe Chindemi, Marwan Abdellah, Oren Amsalem, Ruth Benavides-Piccione, Vincent Delattre, Michael Doron, Andras Ecker, James Gonzalo King, Pramod Kumbhar, Caitlin Claire Monney, Rodrigo Perin, Christian Rössert, Werner Van Geit, Javier DeFelipe, Michael Graupner, Idan Segev, Henry Markram, Eilif Benjamin Mülle, A calcium-based plasticity model predicts long-term potentiation and depression in the neocortex, bioRxiv 2020.04.19.043117 PDF(6.5MB)

  • Toviah Moldwin and Idan Segev, Perceptron Learning and Classification in a Modeled Cortical Pyramidal Cell, frontiers in Computational Neuroscience, (2020) PDF(2.6MB)

Book Chapters

  • Hay E., Gidon A., London M., Segev I.,(2016). A theoretical view of the neuron as an input-output computing device. Google Scholar

  • Rall, W., and Segev, I. (1985). Space clamp problems when voltage clamping branched neuron with intracellular microelectrodes. In: Voltage and Patch Clamping with Microelectrodes. (T.G. Smith, H. Lecar, S.J. Redman, and P.W. Gage, eds.), pp. 191-215. PDF(1.3 MB)

  • Rall, W., and Segev, I. (1987). Functional possibilities for synapses on dendrites and dendritic spines. In: Synaptic Function (Edelman, G.M., Gall, W.F., and Cowan, W.M., eds.). Neurosci. Res. Foundation, pp. 605-636, Wiley, New York.

  • Rall, W., and Segev, I. (1988). Synaptic integration and excitable dendritic spines clusters: structure/function. In: Intrinsic Determinants of Neuronal Form and Function. (Lasek, R. and Black, M. M., eds.), pp. 263-282, Alan R. Liss Inc.

  • Rall, W., and Segev, I. (1988). Excitable dendritic spine cluster: Nonlinear synaptic processing. In: Computer Simulation in Brain Science. (Cotterill, R. M. J., ed.), pp. 26-43, Cambridge Univ. Press.

  • Rall, W., and Segev, I. (1988). Dendritic spines synapses, excitable spine clusters and plasticity. In: Cellular Mechanisms of Conditioning and Behavioral Plasticity. (R. Lasek., D. L. Alkon., and N. V. McGaugh, eds.). pp. 221-236, Plenum Press.

  • Segev, I., Fleshman, J. W. and Burke, R. E. (1989). Compartmental models of complex neurons. In: Methods in Neuronal Modeling: From Synapses to Networks. (Koch, C. and Segev, I. eds.), pp. 63-96, MIT Press, Massachusetts.

  • Rall, W. and Segev, I. (1990). Dendritic branches, spines, synapses and excitable spine clusters. In: Computational Neuroscience.. (E. Schwartz ed.) pp. 69-81 MIT Press, Massachusetts.

  • Segev, I., Rapp., M., Manor, Y., and Yarom, Y. (1992). Analog and digital processing in single nerve cells: Dendritic integration and axonal propagation. In: Single Neuron Computation (McKenna, T., Davis, J., and Zornetzer, S. F. eds.), pp. 173-198, Academic Press.

  • Agmon-Snir, H., and Segev, I. (1993). Signal delay in passive dendritic trees. In: Computation and Neuronal Systems (Eeckman, F, H and Bower, J. M. eds). Kluwer Academic Publishers. pp. 73-78.

  • Segev, I. (1995). Dendritic processing. In: The handbook of Brain Theory and Neuronal Networks. (M. A. Arbib, ed). MIT Press

  • Segev, I. (1995). Cable and Compartmental Models of Dendritic Trees. In: The Book of GENESIS: Exploring Realistic Neural Models with the General Neural Simulation System. (Bower, J.M. and Beeman, D. eds.) Telos/Springer-Verlag, Santa Clara, California. pp.53-81.

  • Segev, I. (1995). Temporal Interactions Between Post-Synaptic Potentials. In: The Book of GENESIS: Exploring Realistic Neural Models with the General Neural Simulation System. (Bower, J.M. and Beeman, D. eds.) Telos/Springer-Verlag, Santa Clara, California. pp.83-101.

  • Agmon-Snir, H., and Segev, I. (1996). The concept of decision points as a tool in analyzing dendritic computation. In: Computational Neuroscience, (Bower, J. M. ed.). Acedemic Press, pp. 41-46.

  • Gradwohl, G., Grossman, Y., and Segev, I. (1996). Modeling the inhibition of Ia input in cat a-motoneurons based on morphological and physiological data. In: Computational Neuroscience, (Bower, J. M. ed.). Acedemic Press, pp. 71-76,

  • Manor, Y., Rinzel, J., Yarom, Y., and Segev, I. (1996). Subthreshold spontaneous oscillations in the inferior olive: An experimentaly-based minimal biophysical network model. In: Computational Neuroscience, (Bower, J. M. ed.). Academic Press, pp. 233-238.

  • Rapp, M., Yarom, Y. and Segev, I. (1997). A detailed model of signal transmission in excitable dendrites of rat neocortical pyramidal cells. In: Computational Neuroscience: Trends in Research (Bower, J. M., ed.) Plenum Publishing Corp. New York. pp. 183-188.

  • Segev, I., and Burke, R.E. (1998). Compartmental models of complex neurons. In: Methods in Neuronal Modeling: From Ions to Networks. (Koch, C. and Segev, I., eds.), pp. 63-96, MIT Press, Massachusetts.

  • Schneidman, E., Freedman, B., and Segev, I. (1998). Spike timing reliability in a stochastic Hodgkin-Huxley model. (Bower, J. ed.) Computational Neuroscience: Trends in Research, pp. 261-266, Plenum, NY.

  • Segev I. (1999). The Neuron as an Elementary Computational Unit, In: Enciclopedia Italiana, Frontiers Della Biologia, Vol. 4, The Brain of Homo Sapiens . (Bizzi, E., Calissano, P., and Volterra, V. eds.), in press.

  • Segev I. (2001). The Neuron as an Elementary Computational Unit, In: Frontiers of Life, Vol. 3, The intelligent Systems, Part 1: The Brain of Homo Sapiens . (Bizzi, E., Calissano, P., and Volterra, V. eds). pp. 51-66, Academic Press.

  • Segev I. and London, M. (1999). A theoretical view of passive and active dendrites. In: Dendrites (G. Stuart, N. Spruston and M. Hausser, eds.), Oxford Univ. Press, in press.

  • Meunier C. and Segev, I. (2001). Neurons as Physical Objects: Dynamics and Function. In: Neuro-Informatics and Neuronal Modelling. P. 353-467 (F. Moss and S. Gielen, eds.). Elsevier.

  • Segev, I. And London, M. (2002). Dendritic processing. In: The handbook of Brain Theory and Neuronal Networks. (M. A. Arbib, ed). MIT Press, 2nd edition (in press).

  • Shaul Druckmann, Albert Gidon, and Idan Segev (2013). Computational Neuroscience: Capturing the Essence. (C.G. Galizia, P.-M. Lledo (eds.), Neurosciences - From Molecule to Behavior: A University Textbook, 671 - 694, Springer-Verlag Berlin Heidelberg. PDF(2.3MB)


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