Predicting spike timing of neocortical pyramidal neurons by simple threshold models. 2006

Renaud Jolivet, and Alexander Rauch, and Hans-Rudolf Lüscher, and Wulfram Gerstner
Ecol Polytechnique Federale de Lausanne (EPFL), School of Computer and Communication Sciences and Brain Mind Institute, Station 15, CH-1015, Lausanne, Switzerland. renuad.jolivet@epfl.ch

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.

UI MeSH Term Description Entries
D008297 Male Males
D008959 Models, Neurological Theoretical representations that simulate the behavior or activity of the neurological system, processes or phenomena; includes the use of mathematical equations, computers, and other electronic equipment. Neurologic Models,Model, Neurological,Neurologic Model,Neurological Model,Neurological Models,Model, Neurologic,Models, Neurologic
D009433 Neural Inhibition The function of opposing or restraining the excitation of neurons or their target excitable cells. Inhibition, Neural
D011237 Predictive Value of Tests In screening and diagnostic tests, the probability that a person with a positive test is a true positive (i.e., has the disease), is referred to as the predictive value of a positive test; whereas, the predictive value of a negative test is the probability that the person with a negative test does not have the disease. Predictive value is related to the sensitivity and specificity of the test. Negative Predictive Value,Positive Predictive Value,Predictive Value Of Test,Predictive Values Of Tests,Negative Predictive Values,Positive Predictive Values,Predictive Value, Negative,Predictive Value, Positive
D011336 Probability The study of chance processes or the relative frequency characterizing a chance process. Probabilities
D011930 Reaction Time The time from the onset of a stimulus until a response is observed. Response Latency,Response Speed,Response Time,Latency, Response,Reaction Times,Response Latencies,Response Times,Speed, Response,Speeds, Response
D004056 Differential Threshold The smallest difference which can be discriminated between two stimuli or one which is barely above the threshold. Difference Limen,Just-Noticeable Difference,Weber-Fechner Law,Difference Limens,Difference, Just-Noticeable,Differences, Just-Noticeable,Differential Thresholds,Just Noticeable Difference,Just-Noticeable Differences,Law, Weber-Fechner,Limen, Difference,Limens, Difference,Threshold, Differential,Thresholds, Differential,Weber Fechner Law
D005260 Female Females
D000200 Action Potentials Abrupt changes in the membrane potential that sweep along the CELL MEMBRANE of excitable cells in response to excitation stimuli. Spike Potentials,Nerve Impulses,Action Potential,Impulse, Nerve,Impulses, Nerve,Nerve Impulse,Potential, Action,Potential, Spike,Potentials, Action,Potentials, Spike,Spike Potential
D000818 Animals Unicellular or multicellular, heterotrophic organisms, that have sensation and the power of voluntary movement. Under the older five kingdom paradigm, Animalia was one of the kingdoms. Under the modern three domain model, Animalia represents one of the many groups in the domain EUKARYOTA. Animal,Metazoa,Animalia

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