# Characterizing signal encoding and transmission in class I and class II neurons via ordinal time-series analysis

## TL;DR

This study uses ordinal time-series analysis to compare how class I and class II neurons encode and transmit signals through spike sequences, revealing how coupling types and signal features influence encoding effectiveness.

## Contribution

It introduces a novel application of ordinal analysis to neuron spike sequences, comparing encoding in different neuron classes and coupling types under various signal conditions.

## Key findings

- Certain neuron/class and coupling combinations optimize signal encoding.
- Electrical and chemical couplings differently affect spike sequence properties.
- Signal amplitude and frequency influence transmission effectiveness.

## Abstract

Neurons encode and transmit information in spike sequences. However, despite the effort devoted to quantify their information content, little progress has been made in this regard. Here we use a nonlinear method of time-series analysis (known as ordinal analysis) to compare the statistics of spike sequences generated by applying an input signal to the neuronal model of Morris-Lecar. In particular we consider two different regimes for the neurons which lead to two classes of excitability: class I, where the frequency-current curve is continuous and class II, where the frequency-current curve is discontinuous. By applying ordinal analysis to sequences of inter-spike-intervals (ISIs) our goals are (1) to investigate if different neuron types can generate spike sequences which have similar symbolic properties; (2) to get deeper understanding on the effects that electrical (diffusive) and excitatory chemical (i.e., excitatory synapse) couplings have; and (3) to compare, when a small--amplitude periodic signal is applied to one of the neurons, how the signal features (amplitude and frequency) are encoded and transmitted in the generated ISI sequences for both class I and class II type neurons and electrical or chemical couplings. We find that depending on the frequency, specific combinations of neuron/class and coupling-type allow a more effective encoding, or a more effective transmission of the signal.