A black-box model for neurons
Visualitza/Obre
10.1109/LASCAS.2019.8667586
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/176658
Tipus de documentComunicació de congrés
Data publicació2019
EditorInstitute of Electrical and Electronics Engineers (IEEE)
Condicions d'accésAccés obert
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continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
We explore the identification of neuronal voltage traces by artificial neural networks based on wavelets (Wavenet). More precisely, we apply a modification in the representation of dynamical systems by Wavenet which decreases the number of used functions; this approach combines localized and global scope functions (unlike Wavenet, which uses localized functions only). As a proof-of-concept, we focus on the identification of voltage traces obtained by simulation of a paradigmatic neuron model, the Morris-Lecar model. We show that, after training our artificial network with biologically plausible input currents, the network is able to identify the neuron's behaviour with high accuracy, thus obtaining a black box that can be then used for predictive goals. Interestingly, the interval of input currents used for training, ranging from stimuli for which the neuron is quiescent to stimuli that elicit spikes, shows the ability of our network to identify abrupt changes in the bifurcation diagram, from almost linear input-output relationships to highly nonlinear ones. These findings open new avenues to investigate the identification of other neuron models and to provide heuristic models for real neurons by stimulating them in closed-loop experiments, that is, using the dynamic-clamp, a well-known electrophysiology technique.
CitacióRoqueiro, N. [et al.]. A black-box model for neurons. A: IEEE Latin American Symposium on Circuits and Systems. "2019 IEEE 10th Latin American Symposium on Circuits & Systems (LASCAS) : Armenia, Colombia, February 24-27, 2019: technical proceedings". Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 129-132.
ISBN978-1-7281-0453-9
Versió de l'editorhttps://ieeexplore.ieee.org/document/8667586
Col·leccions
- Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial - Ponències/Comunicacions de congressos [1.500]
- SD - Sistemes Dinàmics de la UPC - Ponències/Comunicacions de congressos [22]
- Departament de Matemàtiques - Ponències/Comunicacions de congressos [1.080]
- ACES - Control Avançat de Sistemes d'Energia - Ponències/Comunicacions de congressos [230]
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Lascas_2019_RGF.pdf | Article | 898,0Kb | Visualitza/Obre |