Comparison of Coding Capabilities of Type I and Type II Neurons
- 1 May 2004
- journal article
- research article
- Published by Springer Nature in Journal of Computational Neuroscience
- Vol. 16 (3), 299-313
- https://doi.org/10.1023/b:jcns.0000025690.02886.93
Abstract
We consider the dependence of information transfer by neurons on the Type I vs. Type II classification of their dynamics. Our computational study is based on Type I and II implementations of the Morris-Lecar model. It mainly concerns neurons, such as those in the auditory or electrosensory system, which encode band-limited amplitude modulations of a periodic carrier signal, and which fire at random cycles yet preferred phases of this carrier. We first show that the Morris-Lecar model with additive broadband noise ("synaptic noise") can exhibit such firing patterns with either Type I or II dynamics, with or without amplitude modulations of the carrier. We then compare the encoding of band-limited random amplitude modulations for both dynamical types. The comparison relies on a parameter calibration that closely matches firing rates for both models across a range of parameters. In the absence of synaptic noise, Type I performs slightly better than Type II, and its performance is optimal for perithreshold signals. However, Type II performs well over a slightly larger range of inputs, and this range lies mostly in the subthreshold region. Further, Type II performs marginally better than Type I when synaptic noise, which yields more realistic baseline firing patterns, is present in both models. These results are discussed in terms of the tuning and phase locking properties of the models with deterministic and stochastic inputs.Keywords
This publication has 25 references indexed in Scilit:
- Interspike Interval Correlations, Memory, Adaptation, and Refractoriness in a Leaky Integrate-and-Fire Model with Threshold FatigueNeural Computation, 2003
- Spatiotemporal Spike Encoding of a Continuous External SignalNeural Computation, 2002
- Negative Interspike Interval Correlations Increase the Neuronal Capacity for Encoding Time-Dependent StimuliJournal of Neuroscience, 2001
- Evolution of Time Coding SystemsNeural Computation, 1999
- The neural code between neocortical pyramidal neurons depends on neurotransmitter release probabilityProceedings of the National Academy of Sciences, 1997
- Fast, accurate algorithm for numerical simulation of exponentially correlated colored noisePhysical Review A, 1988
- Coding properties of two classes of afferent nerve fibers: high-frequency electroreceptors in the electric fish, Eigenmannia.Journal of Neurophysiology, 1973
- Dynamics of Encoding in a Population of NeuronsThe Journal of general physiology, 1972
- The sense of flutter-vibration: comparison of the human capacity with response patterns of mechanoreceptive afferents from the monkey hand.Journal of Neurophysiology, 1968
- A Stochastic Model of the Repetitive Activity of NeuronsBiophysical Journal, 1966