[Comp-neuro] New papers: Predicting spike timing of neocortical pyramidal neurons by simple threshold models

Renaud Jolivet renaud.jolivet at epfl.ch
Tue Aug 8 14:26:03 CEST 2006

Dear colleagues,

I would like to advertise two papers that we recently published on 
effective Integrate-and-Fire-type models of neuronal activity as well as 
parameter estimation methods for such models. We developed a sequential 
procedure to quantitatively evaluate an equivalent 
Integrate-and-Fire-type model based on intracellular recordings of 
cortical pyramidal neurons. We found that the resulting effective model 
is sufficient to predict the spike train of real pyramidal neurons with 
high accuracy. In in vivo-like regimes, predicted and recorded traces 
are almost indistinguishable and a significant part of the spikes can be 
predicted at the correct timing. Slow processes like spike-frequency 
adaptation are shown to be a key feature in this context since they are 
necessary for the model to connect between different driving regimes.

First paper is available at http://dx.doi.org/10.1007/s10827-006-7074-5
Jolivet R, Rauch A, Lüscher H-R and Gerstner W. Predicting spike timing 
of neocortical pyramidal neurons by simple threshold models. Journal of 
Computational Neuroscience 21, 35-49, 2006.

Second paper is available at 
Jolivet R and Gerstner W. Predicting spike times of a detailed 
conductance-based neuron model driven by stochastic spike arrival. 
Journal of Physiology-Paris 98, 442-451, 2004.

Full abstracts

1. 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.

2. Reduced models of neuronal activity such as integrate-and-fire models 
allow a description of neuronal dynamics in simple, intuitive terms and 
are easy to simulate numerically. We present a method to fit an 
integrate-and-fire-type model of neuronal activity, namely a modified 
version of the spike response model, to a detailed Hodgkin–Huxley-type 
neuron model driven by stochastic spike arrival. In the Hogkin–Huxley 
model, spike arrival at the synapse is modeled by a change of synaptic 
conductance. For such conductance spike input, more than 70% of the 
postsynaptic action potentials can be predicted with the correct timing 
by the integrate-and-fire-type model. The modified spike response model 
is based upon a linearized theory of conductance-driven 
integrate-and-fire neurons.


Renaud Jolivet PhD
Brain Mind Institute
+41 21 693 9687

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