Predicting neuronal activity with simple models of the threshold type: Adaptive Exponential Integrate-and-Fire model with two compartments

Neurocomputing

Volumn 70, Issue 10-12, 2007, Pages 1668-1673

  Clopath, Claudia ^a   Jolivet, Renaud ^a   Rauch, Alexander ^b,c   Lüscher, Hans Rudolf ^c   Gerstner, Wulfram ^a  

^a EPFL   (Switzerland)

^b MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

^c UNIVERSITY OF BERN   (Switzerland)

Author keywords

Adaptation; Exponential integrate and fire; Neuron; Spike timing

Indexed keywords

ANIMALS; LIVING SYSTEMS STUDIES; MATHEMATICAL MODELS; NEURONS; NUMERICAL METHODS; OPTIMIZATION;

EXPONENTIAL INTEGRATE-AND-FIRE; NEOCORTEX; NEURONAL ACTIVITY; SPIKE TIMING;

NEUROLOGY;

ACCURACY; ANALYTICAL PARAMETERS; ANIMAL CELL; ARTICLE; COMPARTMENT MODEL; CONTROLLED STUDY; FILTRATION; IN VITRO STUDY; MATHEMATICAL COMPUTING; NEOCORTEX; NONHUMAN; PERCEPTIVE THRESHOLD; PREDICTION; PRIORITY JOURNAL; PROCESS OPTIMIZATION; PYRAMIDAL NERVE CELL; RAT; RELIABILITY; SPIKE WAVE;

EID: 34247491502     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2006.10.047     Document Type: Article

References (14)

1. Brette R., and Gerstner W. Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. J. Neurophysiol. 94 (2005) 3637-3642
2. Fourcaud-Trocmé N., Hansel D., van Vreeswijk C., and Brunel N. How spike generation mechanisms determine the neuronal response to fluctuating inputs. J. Neurosci. 23 (2003) 11628-11640
3. Gerstner W., and Kistler W. Spiking neuron Models: Single Neurons (2002), Populations, Plasticity, Cambridge University Press, Cambridge
4. Goldman M.S., Golowasch J., Marder E., and Abbott L.F. Global structure. robustness and modulation of neuronal models, J. Neurosci. 21 (2001) 5229-5238
5. Hodgkin A., and Huxley A. A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117 (1952) 500-544
6. Izhikevich E. Which model to use for cortical spiking neurons IEEE Trans. Neural Networks 15 (2004) 1063-1070
7. Jolivet R., Rauch A., Lüscher H.-R., and Gerstner W. Predicting spike timing of neocortical pyramidal neurons by simple threshold models. J. Comput. Neurosci. 21 (2006) 35-49
8. Kistler W., Gerstner W., and van Hemmen J. Reduction of Hodgkin-Huxley equations to a single-variable threshold model. Neural. Comput. 9 (1997) 1015-1045
9. Ljung L. System Identification: Theory for the User. second ed. (1999), Prentice-Hall, Englewood Cliffs, NJ
10. Mainen Z., and Sejnowski T. Reliability of spike timing in neocortical neurons. Science 268 (1995) 1503-1506
11. Naundorf B., Wolf F., and Volgushev M. Unique features of action potential initiation in cortical neurons. Nature 440 (2006) 1060-1063
12. Prinz A.A., Billimoria C.P., and Marder E. Alternative to hand-tuning conductance-based models: construction and analysis of databases of model neurons. J. Neurophysiol. 90 (2003) 3998-4015
13. Rauch A., La Camera G., Lüscher H.-R., Senn W., and Fusi S. Neocortical pyramidal cells respond as integrate-and-fire neurons to in vivo-like input currents. J. Neurophysiol. 90 (2003) 1598-1612
14. Richardson M.J., Brunnel N., and Hakim V. From subthreshold to firing-rate resonance. J. Neurophysiol. 89 (2003) 2538-2554