TY - DATA T1 - The Neuroid revisited: A heuristic approach to model neural spike trains PY - 2018/01/17 AU - Erick Javier Argüello Prada AU - Ignacio Antonio Buscema Arteaga AU - Antonio José D’Alessandro Martínez UR - https://scielo.figshare.com/articles/dataset/The_Neuroid_revisited_A_heuristic_approach_to_model_neural_spike_trains/5792463 DO - 10.6084/m9.figshare.5792463.v1 L4 - https://ndownloader.figshare.com/files/10228659 L4 - https://ndownloader.figshare.com/files/10228668 L4 - https://ndownloader.figshare.com/files/10228671 L4 - https://ndownloader.figshare.com/files/10228683 L4 - https://ndownloader.figshare.com/files/10228686 L4 - https://ndownloader.figshare.com/files/10228689 L4 - https://ndownloader.figshare.com/files/10228695 L4 - https://ndownloader.figshare.com/files/10228701 L4 - https://ndownloader.figshare.com/files/10228704 L4 - https://ndownloader.figshare.com/files/10228710 L4 - https://ndownloader.figshare.com/files/10228722 KW - Neuroid KW - Spiking neuron-model KW - Frequency-intensity curve KW - Accuracy KW - Computational cost KW - Heuristic N2 - 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. ER -