The Neuroid revisited: A heuristic approach to model neural spike trains

AbstractIntroduction: Since it was introduced in 2012, the Neuroid has been used to aid in understanding how functionally different neural populations contribute to sensory information processing. However, insights about whether this neuron-model could perform better than others or about when its utilization should be considered have not been provided yet. Methods In an attempt to address this issue, a comparison between the Neuroid and the leaky-integrate-and-fire (LIF) model in terms of accuracy and computational cost was performed. Both models were tested for different stimulation amplitudes and stimulation periods, with time step sizes ranging from 10-4 to 1 ms. Results It was found that, although the Neuroid was able to produce more accurate results than its original version, its accuracy was lower than the achieved with the LIF model solved by the forward Euler method. On the other hand, the Neuroid performed its calculations in an amount of time significantly lower (Mulfactorial ANOVA test, p < 0.05) than that required by the LIF model when it was solved by using the forward Euler method. Moreover, it was possible to use Neuroid-based networks to replicate biologically relevant firing patterns produced by low-scale networks composed of more detailed neuron-models. Conclusion Results suggest that the Neuroid could be an interesting choice when computational resources are limited, although its use might be restricted to a narrow band of applications.

The implementation of the Neuroid in the Gate Control System leads to new ideas about pain processing

INTRODUCTION: Several theories have been proposed to elucidate the mechanisms related with pain perception, among which, the Gate Control Theory (GCT) provides one of the most explicit explanations. This theory, as elegantly conceived, is unable to explain how the Frequency-Intensity (F-I) curves exhibited by Aβ- and C-fibres influence pain processing. In this paper, a novel neuron-model known as the Neuroid, which emphasizes the functional rather the physiological character of nerve cells, was used as the main building block to replicate the Gate Control System (GCS). METHODS: Two Aβ-fibre models were built: one model that preserved the paradoxical relation between the activation threshold and the F-I curve slope, and one model based on the hypothetical average response across the receptive field. RESULTS: The results suggest that the average response of the Aβ-fibres does not increase monotonically but reaches a plateau for high intensity stimuli. In addition, it was seen that activation of C-fibres does not necessarily imply the activation of projection neurons and, therefore, the onset of pain sensation. Also, we observed that the activation of Aβ-fibres may both, decrease and increase the activity of the projections neurons, an aspect which has not been directly described in previous works. CONCLUSION: Hypothetical implications arise as a consequence of the implementation of the Neuroid, specifically, about the correlation between the intensity of stimulation and the physiological pain threshold.