Predicting spike timing of neocortical pyramidal neurons by simple threshold models
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Neurons generate spikes reliably with millisecond precision if driven by a fluctuating current—is it then possible to predict the spike timing knowing the input? We determined parameters of an adapting threshold model using data recorded in vitro from 24 layer 5 pyramidal neurons from rat somatosensory cortex, stimulated intracellularly by a fluctuating current simulating synaptic bombardment in vivo. The model generates output spikes whenever the membrane voltage (a filtered version of the input current) reaches a dynamic threshold. We find that for input currents with large fluctuation amplitude, up to 75% of the spike times can be predicted with a precision of ±2 ms. Some of the intrinsic neuronal unreliability can be accounted for by a noisy threshold mechanism. Our results suggest that, under random current injection into the soma, (i) neuronal behavior in the subthreshold regime can be well approximated by a simple linear filter; and (ii) most of the nonlinearities are captured by a simple threshold process.
KeywordsSpike Response Model Stochastic input Adapting threshold Spike-timing reliability Predicting spike timing.
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- Abeles M (1991) Corticonics. Cambridge, Cambridge University Press.Google Scholar
- Bair W, Koch C (1996) Temporal precision of spike trains in extrastriate cortex of the behaving macaque monkey. Neural Comp. 8: 1185–1202.Google Scholar
- Braitenberg V, Schütz A (1991) Anatomy of the cortex. Berlin, Springer-Verlag.Google Scholar
- Cash S, Yuste R (1988) Input summation by cultured pyramidal neurons is linear and position-independent. J. Neurosci. 18: 10–15.Google Scholar
- Cox D, Miller H (1965) The Theory of Stochastic Processes. New-York, Chapman & Hall.Google Scholar
- De Weese M, Zador A (2003) Binary spiking in auditory Cortex. J. Neurosci. 23: 7940–7949.Google Scholar
- Gerstner W, Kistler W (2002) Spiking Neurons Models: Single Neurons, Populations, Plasticity. Cambridge, Cambridge University Press.Google Scholar
- Hill A (1936) Excitation and accommodation in nerve. Proc. Roy. Soc. B 119: 305–355.Google Scholar
- Jolivet R (2005) Effective minimal threshold models of neuronal activity. PhD Thesis, Lausanne, Ecole Polytechnique Fédérale de Lausanne (EPFL). http: //icwww.epfl.ch/∼rjolivet/publications/reports/PhDthesis.pdf.Google Scholar
- Lapicque L (1907) Recherches quantitatives sur l’excitation électrique des nerfs traitée comme une polarization. J. Physiol. Pathol. Gen. 9: 620–635.Google Scholar
- Lee Y, Schetzen M (1965) Measurement of the wiener kernels of a non-linear system by cross-correlation. Int. J. Control 2: 237–254.Google Scholar
- Rieke F, Warland D, de Ruyter Van Stevenick R, Bialek W (1996) Spikes—Exploring the neural code. Cambridge, MIT Press.Google Scholar
- Shadlen M, Newsome W (1988) The variable discharge of cortical neurons: implications for connectivity, computation, and information coding. J. Neurosci. 18: 3870–3896.Google Scholar
- Stevens C, Zador A (1998) Novel integrate-and-fire like model of repetitive firing in cortical neurons. 5th Joint Symposium on Neural Computation, UCSD, La Jolla, CA, Institute for Neural Computation.Google Scholar
- Tuckwell H (1988) Introduction to Theoretic Neurobiology. Cambridge, Cambridge University Press.Google Scholar
- Wiener N (1958) Nonlinear Problems in Random Theory. Cambridge, MIT Press.Google Scholar