Al, E., Iliopoulos, F., Forschack, N., Nierhaus, T., Grund, M., Motyka, P., Gaebler, M., Nikulin, V. V., & Villringer, A. (2020). Heart-brain interactions shape somatosensory perception and evoked potentials. Proceedings of the National Academy of Sciences, 117(19), 10575–10584.

Article  CAS  Google Scholar 

Amunts, K., DeFelipe, J., Pennartz, C., Destexhe, A., Migliore, M., Ryvlin, P., Furber, S., Knoll, A., Bitsch, L., Bjaalie, J. G., Ioannidis, Y., Lippert, T., Sanchez-Vives, M. V., Goebel, R., & Jirsa, V. (2022). Linking brain structure, activity, and cognitive function through computation. Eneuro, 9(2).

Bai, B., Wang, X., Li, Y., Chen, P.-C., Yu, K., Dey, K. K., Yarbro, J. M., Han, X., Lutz, B. M., Rao, S., Jiao, Y., Sifford, J. M., Han, J., Wang, M., Tan, H., Shaw, T. I., Cho, J.-H., Zhou, S., Wang, H., ... Peng, J. (2020). Deep multilayer brain proteomics identifies molecular networks in alzheimers disease progression. Neuron, 105(6), 975–991.

Candia-Rivera, D., Chavez, M., & de Vico Fallani, F. (2024). Measures of the coupling between fluctuating brain network organization and heartbeat dynamics. Network Neuroscience, 1–19.

Candia-Rivera, D., Catrambone, V., Barbieri, R., & Valenza, G. (2021). Integral pulse frequency modulation model driven by sympathovagal dynamics: Synthetic vs. real heart rate variability. Biomedical Signal Processing and Control, 68, 102736.

Article  Google Scholar 

Catrambone, V., & Valenza, G. (2023). Nervous–system–wise functional estimation of directed brain–heart interplay through microstate occurrences. IEEE Transactions on Biomedical Engineering.

Catrambone, V., Barbieri, R., Wendt, H., Abry, P., & Valenza, G. (2021). Functional brain-heart interplay extends to the multifractal domain. Philosophical Transactions of the Royal Society A, 379(2212), 20200260.

Article  Google Scholar 

Catrambone, V., Talebi, A., Barbieri, R., & Valenza, G. (2021). Time-resolved brain-to-heart probabilistic information transfer estimation using inhomogeneous point-process models. IEEE Transactions on Biomedical Engineering, 68(11), 3366–3374.

Article  PubMed  Google Scholar 

Corkrum, M., Covelo, A., Lines, J., Bellocchio, L., Pisansky, M., Loke, K., Quintana, R., Rothwell, P. E., Lujan, R., Marsicano, G., Martin, E. D., Thomas, M. J., Kofuji, P., & Araque, A. (2020). Dopamine-evoked synaptic regulation in the nucleus accumbens requires astrocyte activity. Neuron, 105(6), 1036–1047.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Deng, S., Li, J., He, Q., Zhang, X., Zhu, J., Li, L., Mi, Z., Yang, X., Jiang, M., Dong, Q., Mao, Y., & Shu, Y. (2019). Regulation of recurrent inhibition by asynchronous glutamate release in neocortex. Neuron.

Destexhe, A., & Mehta, M. (2022). Properties and computational consequences of fast dendritic spikes during natural behavior. Neuroscience, 489, 251–261.

Article  CAS  PubMed  Google Scholar 

Egger, R., Narayanan, R. T., Guest, J. M., Bast, A., Udvary, D., Messore, L. F., Das, S., De Kock, C. P. J., & Oberlaender, M. (2020). Cortical output is gated by horizontally projecting neurons in the deep layers. Neuron, 105(1), 122–137.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Filist, S., Al-Kasasbeh, R. T., Shatalova, O. V., Btoush, M. H., Namazov, M., Shaqadan, A. A., Alshamasin, M. S., Korenevskiy, N., Aloqeili, S., & Myasnyankin, M. B. (2024). Biotechnical neural network system for predicting cardiovascular health state using processing of bio-signals. International Journal of Medical Engineering and Informatics.

Gast, R., Solla, S. A., & Kennedy, A. (2024). Neural heterogeneity controls computations in spiking neural networks. Proceedings of the National Academy of Sciences, 121(3), e2311885121.

Article  CAS  Google Scholar 

Gleeson, P., Cantarelli, M., Marin, B., Quintana, A., Earnshaw, M., Sadeh, S., Piasini, E., Birgiolas, J., Cannon, R. C., Cayco-Gajic, N. A., Crook, S., Davison, A. P., Dura-Bernal, S., Ecker, A., Hines, M. L., Idili, G., Lanore, F., Larson, S. D., Lytton, W. W., ... Silver, R. A. (2019). Open source brain: A collaborative resource for visualizing, analyzing, simulating, and developing standardized models of neurons and circuits. Neuron, 103(3), 395–411.

Guest, O., & Martin, A. E. (2023). On logical inference over brains, behaviour, and artificial neural networks. Computational Brain & Behavior, 6(2), 213–227.

Article  Google Scholar 

Haberbusch, M., Frullini, S., & Moscato, F. (2021). A numerical model of the acute cardiac effects provoked by cervical vagus nerve stimulation. IEEE Transactions on Biomedical Engineering, 69(2), 613–623.

Article  Google Scholar 

Henkes, A., Eshraghian, J. K., & Wessels, H. (2024). Spiking neural networks for nonlinear regression. Royal Society Open Science, 11(5), 231606.

Article  PubMed  PubMed Central  Google Scholar 

Hosseini, E. A., Schrimpf, M., Zhang, Y., Bowman, S., Zaslavsky, N., & Fedorenko, E. (2024). Artificial neural network language models predict human brain responses to language even after a developmentally realistic amount of training. Neurobiology of Language, 5(1), 43–63.

Article  PubMed  PubMed Central  Google Scholar 

Hsieh, Y.-T., Anjum, K., & Pompili, D. (2024). Ultra-low power analog folded neural network for cardiovascular health monitoring. IEEE Journal of Biomedical and Health Informatics.

Izhikevich, E. M. (2003). Simple model of spiking neurons. IEEE Transactions on Neural Networks, 14(6), 1569–1572.

Article  CAS  PubMed  Google Scholar 

Kim, R., & Sejnowski, T. J. (2021). Strong inhibitory signaling underlies stable temporal dynamics and working memory in spiking neural networks. Nature Neuroscience, 24(1), 129–139.

Article  CAS  PubMed  Google Scholar 

Li, Y., Song, B., & Zeng, X. (2023). Spiking neural p systems with weights and delays on synapses. Theoretical Computer Science, 114028.

Li, Q., Sorscher, B., & Sompolinsky, H. (2024). Representations and generalization in artificial and brain neural networks. Proceedings of the National Academy of Sciences, 121(27), e2311805121.

Article  CAS  Google Scholar 

Liu, X., Zhao, Y., & Wang, L. (2023). Nonlinear neural-like p model for time series classification. Theoretical Computer Science, 114055.

Livneh, Y., Sugden, A. U., Madara, J. C., Essner, R. A., Flores, V. I., Sugden, L. A., Resch, J. M., Lowell, B. B., & Andermann, M. L. (2020). Estimation of current and future physiological states in insular cortex. Neuron, 105(6), 1094–1111.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Lombardi, F., Pepić, S., Shriki, O., Tkačik, G., & De Martino, D. (2023). Statistical modeling of adaptive neural networks explains co-existence of avalanches and oscillations in resting human brain. Nature Computational Science, 3(3), 254–263.

Article  PubMed  PubMed Central  Google Scholar 

Lucas, S., & Portillo, E. (2024). Methodology based on spiking neural networks for univariate time-series forecasting. Neural Networks, 173, 106171.

Article  PubMed  Google Scholar 

Malandrone, F., Catrambone, V., Carletto, S., Rossini, P. G., Coletti Moja, M., Oliva, F., Pagani, M., Valenza, G., & Ostacoli, L. (2024). Restoring bottom-up communication in brain-heart interplay after trauma-focused psychotherapy in breast cancer patients with post-traumatic stress disorder. Journal of Affective Disorders, 351, 143–150.

Article  CAS  PubMed  Google Scholar 

Mark, J. A., Curtin, A., Kraft, A. E., Ziegler, M. D., & Ayaz, H. (2024). Mental workload assessment by monitoring brain, heart, and eye with six biomedical modalities during six cognitive tasks. Frontiers in neuroergonomics, 5, 1345507.

Article  PubMed  PubMed Central  Google Scholar 

Mehmood, A., & Iqbal, M. J. (2024). Hybrid spiking neural networks for anomaly detection of brain, heart and pancreas. Arabian Journal for Science and Engineering, 1–11.

Mehmood, A., & Iqbal, M. J. (2023). Hybrid excitable spiking neural network for cardiovascular disease prediction. IEEE Access, 11, 128187–128197.

Article  Google Scholar 

Najafi, F., Elsayed, G. F., Cao, R., Pnevmatikakis, E., Latham, P. E., Cunningham, J. P., & Churchland, A. K. (2020). Excitatory and inhibitory subnetworks are equally selective during decision-making and emerge simultaneously during learning. Neuron, 105(1), 165–179.

Article  CAS  PubMed  Google Scholar 

Pagkalos, M., Chavlis, S., & Poirazi, P. (2023). Introducing the dendrify framework for incorporating dendrites to spiking neural networks. Nature Communications, 14(1), 131.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Paudel, R., Jafri, M. S., & Ullah, A. (2023). Pacing dynamics determines the arrhythmogenic mechanism of the cpvt2-causing casq2g112+ 5x mutation in a guinea pig ventricular myocyte computational model. Genes, 14(1), 23.

Article  CAS  Google Scholar 

Pegolotti, L., Pfaller, M. R., Rubio, N. L., Ding, K., Brugarolas Brufau, R., Darve, E., & Marsden, A. L. (2024). Learning reduced-order models for cardiovascular simulations with graph neural networks. Computers in Biology and Medicine, 168, 107676.

Article  PubMed  Google Scholar 

Roussarie, J.-P., Yao, V., Rodriguez-Rodriguez, P., Oughtred, R., Rust, J., Plautz, Z., Kasturia, S., Albornoz, C., Wang, W., Schmidt, E. F., Dannenfelser, R., Tadych, A., Brichta, L., Barnea-Cramer, A., Heintz, N., Hof, P. R., Heiman, M., Dolinski, K., Flajolet, M., ... Greengard, P. (2020). Selective neuronal vulnerability in alzheimer disease: A network-based analysis. Neuron, 107(5), 821–835.

Seibertz, F., Rapedius, M., Fakuade, F. E., Tomsits, P., Liutkute, A., Cyganek, L., Becker, N., Majumder, R., Clauß, S., Fertig, N., et al. (2022). A modern automated patch-clamp approach for high throughput electrophysiology recordings in native cardiomyocytes. Communications biology, 5(1), 1–10.

Article  Google Scholar 

Shaban, A., Bezugam, S. S., & Suri, M. (2021). An adaptive threshold neuron for recurrent spiking neural networks with nanodevice hardware implementation. Nature Communications, 12(1), 4234.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Shao, F., & Shen, Z. (2023). How can artificial neural networks approximate the brain? Frontiers in Psychology, 13, 970214.

Article  PubMed  PubMed Central  Google Scholar 

Singh, N., Gunjan, V. K., Shaik, F., & Roy, S. (2024). Detection of cardio vascular abnormalities using gradient descent optimization and cnn. Health and Technology, 14(1), 155–168.

Article  Google Scholar 

Subramoney, A., Bellec, G., Scherr, F., Legenstein, R., & Maass, W. (2024). Fast learning without synaptic plasticity in spiking neural networks. Scientific Reports, 14(1), 8557.

Sutanto, H. (2024). Transforming clinical cardiology through neural networks and deep learning: A guide for clinicians. Current Problems in Cardiology, 102454.

Turab, A., Sintunavarat, W., Ullah, F., Zaidi, S. A., Montoyo, A., & Nescolarde-Selva, J.-A. (2024). Computational modelling of animal behaviour in t-mazes: Insights from machine learning. Ecological Informatics, 81, 102639.

Venkatesan, C., Karthigaikumar, P., Paul, A., Satheeskumaran, S., & Kumar, R. (2018). Ecg signal preprocessing and svm classifier-based abnormality detection in remote healthcare applications. IEEE Access, 6, 9767–9773.