Sub-threshold signal encoding in coupled FitzHugh-Nagumo neurons

Masoliver, Maria; Masoller, Cristina

doi:10.1038/s41598-018-26618-8

Cited by 21 publications

(20 citation statements)

References 51 publications

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“…In the FHN model theoretical and numerical studies have shown that, without noise, the signal is subthreshold (i.e. it does not induce spikes) if its period is long enough [61,62]. Our results consistently show that, at low frequencies, the spike rate of the laser and of the neuron is almost unaffected by the signal (blue regions at low frequencies in the four panels of figure 4).…”

Section: Resultssupporting

confidence: 71%

“…For D=3, we have 3!=6 possible OPs: 012 (ΔT 3 >ΔT 2 >ΔT 1 ), 021 (ΔT 2 >ΔT 3 >ΔT 1 ), 102 (ΔT 3 >ΔT 1 >ΔT 2 ), 120 (ΔT 2 >ΔT 1 > ΔT 3 ), 201 (ΔT 1 >ΔT 3 >ΔT 2 ) and 210 (ΔT 1 >ΔT 2 >ΔT 3 ). As in previous works [38,55,56,62,63] we use D=3, which allows identifying temporal relations among four consecutive spikes. As the number of possible patterns increases as D!, a larger D significantly increases the data requirements, because very long sequences of spikes are needed for a robust estimation of the probabilities of the D!…”

Section: Resultsmentioning

confidence: 99%

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Comparing the dynamics of periodically forced lasers and neurons

2019

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Neuromorphic photonics is a new paradigm for ultra-fast neuro-inspired optical computing that can revolutionize information processing and artificial intelligence systems. To implement practical photonic neural networks is crucial to identify low-cost energy-efficient laser systems that can mimic neuronal activity. Here we study experimentally the spiking dynamics of a semiconductor laser with optical feedback under periodic modulation of the pump current, and compare with the dynamics of a neuron that is simulated with the stochastic FitzHugh-Nagumo model, with an applied periodic signal whose waveform is the same as that used to modulate the laser current. Sinusoidal and pulsedown waveforms are tested. We find that the laser response and the neuronal response to the periodic forcing, quantified in terms of the variation of the spike rate with the amplitude and with the frequency of the forcing signal, is qualitatively similar. We also compare the laser and neuron dynamics using symbolic time series analysis. The characterization of the statistical properties of the relative timing of the spikes in terms of ordinal patterns unveils similarities, and also some differences. Our results indicate that semiconductor lasers with optical feedback can be used as low-cost, energy-efficient photonic neurons, the building blocks of all-optical signal processing systems; however, the length of the external cavity prevents optical feedback on the chip.

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Section: Resultssupporting

confidence: 71%

Section: Resultsmentioning

confidence: 99%

Comparing the dynamics of periodically forced lasers and neurons

2019

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“…In Figure 3 we show how the encoding mechanism is robust to different models, coupling types and neuron excitability classes. As in 15 we observe, for the two variations of the Morris-Lecar model, the resonant effect for both types of coupling (chemical and electrical) and for the two type of neurons (class I and class II). Figure 3 displays ordinal patterns probabilities as a function of the firing rate.…”

Section: A Robustness Of the Signal Encoding Via Ordinal Patternssupporting

confidence: 78%

“…In a recent study 15 , the encoding of a weak external signal by a neuron (modeled with the FitzHugh-Nagumo model) mutually coupled to a second neuron (which did not directly receive the signal) was studied. There it was shown that the encoding mechanism was robust to the coupling: the modulated neuron carried, under certain circumstances, information about the signal.…”

Section: A Robustness Of the Signal Encoding Via Ordinal Patternsmentioning

confidence: 99%

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

Estarellas

Masoliver

Masoller

et al. 2020

Chaos: An Interdisciplinary Journal of Nonlinear Science

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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.

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“…In contrast to other methods, PeEn is a time-series complexity measure that is simple to implement, is robust to noise and short time-series, and works for arbitrary data sets. 13,16,17,[20][21][22][23][24][25][26] In particular, it has been shown that PeEn applied to EEG signals captures different states associated with the level of consciousness, both during anesthesia [26][27][28][29] and sleep. 30,31 Hence, in order to study the thalamo-cortical function during W and sleep, PeEn is a practical and reliable method, where results can be understood from primary principles, and can be related to the signal characteristics.…”

Section: Introductionmentioning

confidence: 99%

Decreased electrocortical temporal complexity distinguishes sleep from wakefulness

González

Cavelli

Mondino

et al. 2019

Preprint

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In most mammals, the sleep-wake cycle is constituted by three behavioral states: wakefulness (W), non-NREM (NREM) sleep, and REM sleep. These states are associated with drastic changes in cognitive capacities, mostly determined by the function of the thalamo-cortical system. The intra-cranial electroencephalogram or electocorticogram (ECoG), is an important tool for measuring the changes in the thalamo-cortical activity during W and sleep. In the present study we analyzed broad-band ECoG recordings of the rat by means of a time-series complexity measure that is easy to implement and robust to noise: the Permutation Entropy (PeEn). We found that PeEn is maximal during W and decreases during sleep. These results bring to light the different thalamo-cortical dynamics emerging during sleep-wake states, which are associated with the well-known spectral changes that occur when passing from W to sleep. Moreover, the PeEn analysis allows to determine behavioral states independently of the electrodes' cortical location, which points to an underlying global pattern in the signal that differs among the cycle states that is missed by classical methods. Consequently, our data suggest that PeEn analysis of a single EEG channel could allow for cheap, easy, and efficient sleep monitoring. 10/11

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Sub-threshold signal encoding in coupled FitzHugh-Nagumo neurons

Cited by 21 publications

References 51 publications

Comparing the dynamics of periodically forced lasers and neurons

Comparing the dynamics of periodically forced lasers and neurons

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

Decreased electrocortical temporal complexity distinguishes sleep from wakefulness

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