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

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. a) ElectronicSensory neurons detect, encode and transmit information of external temporal stimuli (input signals such as visual, auditory, olfactory, etc.) in sequences of spikes, also known as action potentials. Despite decades of effort to understand how information is processed, the underlying mechanisms of neuronal encoding are still not fully understood. Different coding mechanisms have been proposed in the literature, which can be more or less effective depending on the level of environmental noise, the level of the external signal, and its frequency. Here we focus on the encoding of a small-amplitude periodic signal. We use a well-known neuron model to investigate the role of the excitability class of the neurons (either class I or class II) and of the type of coupling (electrical or chemical) to a second neuron that does not perceive the external signal. We find that the neuron can encode the signal in the form of preferred (more expressed) temporal spike patterns, regardless of the class of neuron and of the type of coupling. On the contrary, depending on the signal frequency, specific combinations of neuron-class and coupling-type allow a more effective encoding, or a more effective transmission of the signal.

Dorsal and median raphe neuronal firing dynamics characterized by non-linear metrics

“…A recent work by Estarellas et al (2020) [77], used nonlinear metrics to investigate the encoding and information transmission in time series of sensory neurons. They found 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.…”

20 years of ordinal patterns: Perspectives and challenges

“…Using neuronal models it has been shown that, when a weak (subthreshold) periodic input is perceived by a stochastic neuron, the neuron fires a sequence of spikes with specific properties of ordinal probabilities. The ordinal probabilities depend on the amplitude and on the period of the input signal [28,30] and thus, they may be informative of these features of the signal. It was also found that this encoding mechanism can be enhanced in an ensemble of neurons, when they all perceive the weak signal [31].…”

Soft-wired long-term memory in a natural recurrent neuronal network

“…We also quantified the degree of complexity of the dynamical behavior using a standard measure of complexity, namely the permutation entropy [32][33][34] . To that end, we extracted all 3-element ordinal patterns from the time series (i.e.…”