Coincidence detection in active neurons
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1
Ecole Normale Supérieure, France
Neuronal synchronization is ubiquitous in the nervous system, yet its functional role for information processing is still unclear. Because of the leak current, two input spikes are more likely to make a postsynaptic neuron fire when they are synchronous. This coincidence detection property has been demonstrated in vivo for thalamocortical processing (Usrey, Alonso and Reid (2000) J Neurosci 0(14)), but the theory of coincidence detection in active neurons is still sparse. We tried to answer the following question: in an active neuron (with background synaptic activity), what is the extra probability of firing a spike in response to two input spikes, as a function of the delay between them? Our approach is based on several properties of cortical neurons in vivo: the membrane time constant is short compared to typical inter-spike intervals; the membrane potential is stochastic, with a mean well below threshold (balanced regime); the autocorrelation time constant is dominated by slow inhibitory fluctuations. Incorporating these properties in a stochastic spiking model allowed us to obtain quantitative estimates of coincidence detection properties. We obtained an approximation of the extra firing probability as a function of the delay between two pre-synaptic spikes, from which we derived two quantities: the strength and timescale of coincidence detection. The strength of coincidence detection was defined as the relative increase in firing in response to coincident spikes compared to distant ones. We found that coincident spikes can be more than twice as efficient as distant spikes in realistic situations. We extended our results to calculate the extra firing rate induced by a pool of correlated input spike trains and found that the postsynaptic firing rate was very sensitive to the strength of correlations. The timescale of coincidence detection was defined from the decay of the extra firing function and corresponds to the temporal window of interaction between two input spikes. We found that the timescale of coincidence detection can be expressed as the product of the time constant of post-synaptic potentials (PSPs) and of a quantity defined by the membrane potential distribution and the synaptic weight (maximum of the PSP). This quantity is always smaller than 1, it approaches 1 at high noise level and 0 at low noise level. Our estimates were consistent with numerical simulations. Our modeling results show that, in realistic situations, 1) fine correlations can have a strong impact on the response of a post-synaptic neuron and 2) spiking models are sensitive to finer correlations than expected from the value of the membrane time constant.
Conference:
Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010.
Presentation Type:
Poster Presentation
Topic:
Poster session III
Citation:
Rossant
C and
Brette
R
(2010). Coincidence detection in active neurons.
Front. Neurosci.
Conference Abstract:
Computational and Systems Neuroscience 2010.
doi: 10.3389/conf.fnins.2010.03.00177
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Received:
02 Mar 2010;
Published Online:
02 Mar 2010.
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Correspondence:
Cyrille Rossant, Ecole Normale Supérieure, Paris, France, cyrille.rossant@gmail.com