P068 Modelling the dynamics of in vitro cultured neural networks by using biophysical neuronal models
Marco Fabiani1, Ludovico Iannello3, Fabrizio Tonelli4, Eleonora Crocco4, Federico Cremisi4, Lucio Calcagnile2, Riccardo Mannella1, Angelo Di Garbo1,2 1Department of Physics, University of Pisa 2Institute of Biophysics - IBF, CNR of Pisa 3Institute of Information Science and Technologies ”Alessandro Faedo” - ISTI, CNR of Pisa 4Scuola Normale Superiore - SNS, Pisa Email: m.fabiani5@studenti.unipi.it, angelo.digarbo@ibf.cnr.it IntroductionIn this contribution we study the dynamical behaviours arising in a biophys-ical inspired neuronal network of excitatory and inhibitory neurons. The setup of the corresponding model was done by using the electro-physiological data recorded on cultured neuronal networks. The recordingsof the local field potential generated by the neurons was carried outby using multielectrode array (MEA) apparatus [2]. In particular, weinvestigated the dynamics emerging in a cultured population of (Mapk/erkinhibition and BMP inhibition, MiBi) neurons of the entorhinal cortex [1]. MethodsThe MEA recordings were obtained from a grid of 64x 64 electrodes coveringan area of 3.8 mm x 3.8 mm of the neuronal culture. The correspondinglocal field potentials were acquired with a sampling frequency of 20 kHz.The spiking times of the cultured neurons were obtained by applying specificalgorithms to the local field potential signals. Then, an artificialbiophysical inspired neural network was built by employing Hodgkin-Huxley-type models for the single neuron.Finally,the parameters describing the computational neural network were chosen byrequiring that the simulation results were qualitatively in agreement with thecorresponding experimental data. ResultsAccording to the results described in [2, 3] we found that the MiBi culturedneuoronal network is capable of generating bursting activity. Moreoverthe analysis of these data show that the bursting activity is triggered bysome points of the cultured network (center of activity). In addition, thepropagation on the neural culture was characterized by the center of activitytrajectories (CAT). Furthermore, these cultures exhibit neuronalavalanches with power decay. We have shown that the computationalmodel is capable of reproducing the bursting dynamics observed in vivo cul-tured neural network by choosing suitable parameter values in an all-to-allcoupled network.By setting up a more detailed network model, obtained by modifying theconnectivity matrix and the density of neurons, we proved that such a neu-ronal network is capable of reproducing many of the experimental data and,qualitatively, their specific features. DiscussionAlthough the mathematical model has some intrinsic limitations, the corre-sponding numerical results helped us to shed light on some basic mechanismsresponsible for the generation of bursting in the network and this could beused to infer that such processes should be present also in the MiBi culturedneuronal network. It would be interesting to check if improving the qualityof the neuronal model will be sufficient to reproduce others experimental fea-tures that are not captured by the adopted model. This include, for instance,to use more realistic single neuron model, synaptic connectivity and synapticplasticity.
Acknowledgements The research was in part supported by the Matteo Caleo Foundation, by Scuola Normale Superiore (FC), by the PRIN AICult grant #2022M95RC7 from the Italian Ministry of University and Research (MUR) (FC) and by the Tuscany Health Ecosystem - THE grant from MUR (FC, GA, ADG). References [1]Tonelli F. et al. “Dual inhibition of MAPK/ERK and BMP signaling induces entorhinal-like identity in mouse ESC-derived pallial progeni- tors.” In: Stem Cell (2025). doi: 10.1016/j.stemcr.2024.12.002. [2] Ludovico Iannello et al. “Analysis of MEA recordings in cultured neural networks”. In: (2024), pp. 1–5. doi: 10.1109/COMPENG60905.2024.10741515. [3] Ludovico Iannello et al. “Criticality in neural cultures: Insights into memory and connectivity in entorhinal-hippocampal networks”. In: Chaos, Solitons and Fractals 194 (2025), p. 116184.
