In this chapter, neural responses are generated by changing the Inter-Spike-Interval (ISI) of the stimulus. These responses are subsequently compared and a coincidence factor is obtained. Coincidence-factor, a measure of similarity, is expected to generate a high value for higher similarity and a low value for dissimilarity. It is observed that these coincidence-factors do not have a consistent trend over a simulation time window. Also, the lower-bound limit for faithful behaviour of coincidence factor shifts towards the right with the increase in the reference ISI of the stimulus. In principle, if two responses have a very high similarity, then their respective stimuli should be very similar and could possibly be considered the same. However, as results show, two spike trains generated by highly-varying stimuli have a high coincidence-factor. This is due to limitations imposed by the one-dimensional comparison of coincidence-factor.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Lundström I (1974). Mechanical wave propagation on nerve axons. Journal of Theoretical Biology, 45: 487–499.
Abbott L F, Kepler T B (1990). Model neurons: From Hodgkin Huxley to Hopfield. Statistical Mechanics of Neural Networks, Edited by Garrido L, pp. 5–18.
Hasegawa H (2000). Responses of a Hodgkin-Huxley neuron to various types of spike-train inputs. Physical Review E, 61(1): 718.
Kepecs A, Lisman J (2003). Information encoding and computation with spikes and bursts. Network: Computation in Neural Systems, 14: 103–118.
Fourcaud-Trocmé N, Hansel D, van Vreeswijk C, Brunel N (2003). How spike generation mechanisms determine the neuronal response to fluctuating inputs. The Journal of Neuroscience, 23(37): 11628–11640.
Bokil H S, Pesaran B, Andersen R A, Mitra P P (2006). A method for detection and classification of events in neural activity. IEEE Transactions on Biomedical Engineering, 53(8): 1678–1687.
Davies R M, Gerstein G L, Baker S N (2006). Measurement of time-dependent changes in the irregularity of neural spiking. Journal of Neurophysiology, 96: 906–918.
Diba K, Koch C, Segev I (2006). Spike propagation in dendrites with stochastic ion channels. Journal of Computational Neuroscience, 20: 77–84.
Dimitrov A G, Gedeon T (2006). Effects of stimulus transformations on estimates of sensory neuron selectivity. Journal of Computational Neuroscience, 20: 265–283.
Rinzel J (1985). Excitation dynamics: Insights from simplified membrane models. Theoretical Trends in Neuroscience Federal Proceedings, 44(15): 2944–2946.
Gabbiani F, Metzner W (1999). Encoding and processing of sensory information in neuronal spike trains. The Journal of Biology, 202: 1267–1279.
Panzeri S, Schultz S R, Treves A, Rolls E T (1999). Correlations and the encoding of information in the nervous system. Proceedings of the Royal Society of London, B 266(1423): 1001–1012.
Agüera y Arcas B, Fairhall A L (2003). What causes a neuron to spike? Neural Computation, 15: 1789–1807.
Agüera y Arcas B, Fairhall A L, Bialek W (2003). Computation in a single neuron: Hodgkin and Huxley revisited. Neural Computation, 15: 1715–1749.
Izhikevich E M (2006). Polychronization: Computation with spikes. Neural Computation, 18: 245–282.
Li X, Ascoli G A (2006). Computational simulation of the input-output relationship in hippocampal pyramidal cells. Journal of Computational. Neuroscience, 21: 191–209.
Kepler T B, Abbott L F, Marder E (1992). Reduction of conductance-based neuron models. Biological Cybernetics, 66: 381–387.
Joeken S, Schwegler H (1995). Predicting spike train responses of neuron models; inM.Verleysen (ed.), Proceedings of the 3rd European Symposium on Artificial Neural Networks, pp. 93–98.
Wang X J, Buzsáki G (1996). Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model. The Journal of Neuroscience, 16(20): 6402–6413.
Kistler W M, Gerstner W, Leo van Hemmen J (1997). Reduction of the Hodgkin-Huxley equations to a single-variable threshold model. Neural Computation, 9: 1015–1045.
Izhikevich E M (2003). Simple model of spiking neurons. IEEE Transactions on Neural Networks, 14(6): 1569–1572.
Shriki O, Hansel D, Sompolinsky H (2003). Rate models for conductance-based cortical neuronal networks. Neural Computation, 15: 1809–1841.
Jolivet R, Gerstner W (2004). Predicting spike times of a detailed conductance-based neuron model driven by stochastic spike arrival. Journal of Physiology — Paris, 98: 442–451.
Jolivet R, Lewis T J, Gerstner W (2004). Generalized integrate-and-fire models of neuronal activity approximate spike trains of a detailed model to a high degree of accuracy. Journal of Neurophysiology, 92: 959–976.
Jolivet R, Rauch A, Lüscher H-R, Gerstner W (2006). Integrate-and-fire models with adaptation are good enough: Predicting spike times under random current injection. Advances in Neural Information Processing Systems, 18: 595–602.
Jolivet R, Rauch A, Lüscher H-R, Gerstner W (2006). Predicting spike timing of neocortical pyramidal neurons by simple threshold models. Journal of Computational Neuroscience, 21: 35–49.
Clopath C, Jolivet R, Rauch A, Lüscher H-R, Gerstner W (2007). Predicting neuronal activity with simple models of the threshold type: Adaptive exponential integrate-and-fire model with two compartments. Neurocomputing, 70: 1668–1673.
Djabella K, Sorine M (2007). Reduction of a cardiac pacemaker cell model using singular perturbation theory. Proceedings of the European Control Conference 2007, Kos, Greece, pp. 3740–3746.
Hodgkin A, Huxley A (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology, 117:500–544.
Maršálek, P (2000). Coincidence detection in the Hodgkin—Huxley equations. Biosystems, 58(1–3).
Victor J D, Purpura K P (1997). Metric-space analysis of spike trains: Theory, algorithms and application. Network: Computation in Neural Systems, 8: 127–164.
Park M H, Kim S (1996). Analysis of phase models for two coupled Hodgkin-Huxley neurons. Journal of the Korean Physical Society, 29(1): 9–16.
Sarangdhar M, Kambhampati C (2008). Spiking neurons: Is coincidence-factor enough to compare responses with fluctuating membrane voltage? In World Congress on Engineering 2008: The 2008 International Conference of Systems Biology and Bioengineering, London, UK, 2ȓ4 July 2008, Vol. 2, pp. 1640–1645.
Sarangdhar M, Kambhampati C (2008). Spiking neurons and synaptic stimuli: Determining the fidelity of coincidence-factor in neural response comparison. Special Issue of IAENG Journal (in print).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer Science+Business Media B.V
About this chapter
Cite this chapter
Sarangdhar, M., Kambhampati, C. (2009). Spiking Neurons and Synaptic Stimuli: Neural Response Comparison Using Coincidence-Factor. In: Ao, SI., Gelman, L. (eds) Advances in Electrical Engineering and Computational Science. Lecture Notes in Electrical Engineering, vol 39. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-2311-7_58
Download citation
DOI: https://doi.org/10.1007/978-90-481-2311-7_58
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-2310-0
Online ISBN: 978-90-481-2311-7
eBook Packages: EngineeringEngineering (R0)