Knowledge about empathy is part of the study of artistic expressions, among which stand out works of personalities such as the Renaissance polymath Leonardo da Vinci, who was concerned with the connection between science and art during his creative research full of imagination and sensitivity to nature and human anatomy. The word empathy emerged among critics of German art as the term einfühlung, which was used within the aesthetic bias by philosophers and art historians. It emphasized the idea that a viewer perceiving an object could establish a link between it and themselves, projecting the object ‘into themselves’. That is, the artwork could be experienced by the observer as if the viewer belonged predominantly to the object, in such a way that its characteristics could be actually felt through the expression of emotions, feelings and thoughts. This analysis of art appreciation required a great deal of knowledge and contemplation of nature, as understood by the German Romanticists, who had enormous admiration for da Vinci and his universal and systematic mind—a mind which reacted against formalisms, building his intellectual and sensory systems based on both his observation of nature and his own criteria. In particular, the art of painting for Leonardo was a way to demonstrate a mental discourse, just as the most important aspect of human portraits is to represent—in gestures and facial expressions—the states of mind and emotions. These are facts that German Romanticists tried to explain as the relationship between empathy and a work of art. The present manuscript aims to describe empathy from an artistic view, considering the roots of this word in German Romanticism; to comment about Leonardo da Vinci and the expression of art in the Renaissance; and, finally, to discuss the expression of his art in relation to empathy., The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the following grants: PID2021-127924NB-I00 (to JD) funded by MCIN/AEI/10.13039/501100011033.

This article explores the historical progression of studying neuronal connections, beginning with nineteenth-century advancements in light microscopy and histological techniques. Early methods were limited in terms of their capacity to trace neuronal connections, but a breakthrough came with Camillo Golgi's "black reaction" staining method, later refined and extensively used by Santiago Ramón y Cajal. Cajal's observations supported the Neuron Theory, which proposed that neurons communicate via specialized points of contact, contradicting the prevailing Reticular Theory of a continuous neural network, which was supported by Golgi. This contrast is particularly intriguing because, although Golgi and Cajal used the same black reaction technique and similar microscopes, their interpretations of the microscopic world diverged significantly. An important consequence of the Neuron Theory was Cajal's Law of Dynamic Polarization, which proposed that neurons generally consist of three functionally distinct regions: a receptor apparatus (dendrites and soma), an emission apparatus (axon), and a distribution apparatus (terminal axonal arborization). He applied this principle across various parts of the nervous system and to different neuron types, enabling the generation of the first detailed circuit diagrams of the brain. Cajal's observations, concepts, and theories had a profound impact-not only on researchers of his time, but also on modern neuroscience. This article reflects on the early studies of neuronal connections, highlighting the scientific climate in which Golgi and Cajal initiated their groundbreaking research., Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was supported by the grant PID2021-127924NB-I00, funded by MCIN/AEI/https:// doi. org/ 10. 13039/ 50110 00110 33., Peer reviewed

Santiago Ramón y Cajal’s pioneering work laid the foundations for modern neuroscience and continues to impact the development of artificial intelligence, particularly deep learning. His neuron theory, the principle of dynamic polarization, and his insights into brain plasticity and network organization have significantly influenced both our understanding of the nervous system and the design of artificial neural networks. This article reviews Cajal’s key contributions, explores their role in the evolution of AI, and emphasizes the enduring links between neuroscience and machine learning in the digital era., The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by grants PID2021-127924NB-I00 (JD) and PID2022-137801NB-I00 (OH), both funded by MCIN/AEI/10.13039/501100011033, and by the VIRTUAL BRAIN TWIN project (HORIZON-HLTH-2023-TOOL-05-03, MG-L).

Santiago Ramón y Cajal’s pioneering work laid the foundations for modern neuroscience and continues to impact the development of artificial intelligence, particularly deep learning. His neuron theory, the principle of dynamic polarization, and his insights into brain plasticity and network organization have significantly influenced both our understanding of the nervous system and the design of artificial neural networks. This article reviews Cajal’s key contributions, explores their role in the evolution of AI, and emphasizes the enduring links between neuroscience and machine learning in the digital era., The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by grants PID2021-127924NB-I00 (JD) and PID2022-137801NB-I00 (OH), both funded by MCIN/AEI/10.13039/501100011033, and by the VIRTUAL BRAIN TWIN project (HORIZON-HLTH-2023-TOOL-05-03, MG-L).

Santiago Ramón y Cajal’s pioneering work laid the foundations for modern neuroscience and continues to impact the development of artificial intelligence, particularly deep learning. His neuron theory, the principle of dynamic polarization, and his insights into brain plasticity and network organization have significantly influenced both our understanding of the nervous system and the design of artificial neural networks. This article reviews Cajal’s key contributions, explores their role in the evolution of AI, and emphasizes the enduring links between neuroscience and machine learning in the digital era., The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by grants PID2021-127924NB-I00 (JD) and PID2022-137801NB-I00 (OH), both funded by MCIN/AEI/10.13039/501100011033, and by the VIRTUAL BRAIN TWIN project (HORIZON-HLTH-2023-TOOL-05-03, MG-L).

Santiago Ramón y Cajal’s pioneering work laid the foundations for modern neuroscience and continues to impact the development of artificial intelligence, particularly deep learning. His neuron theory, the principle of dynamic polarization, and his insights into brain plasticity and network organization have significantly influenced both our understanding of the nervous system and the design of artificial neural networks. This article reviews Cajal’s key contributions, explores their role in the evolution of AI, and emphasizes the enduring links between neuroscience and machine learning in the digital era., The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by grants PID2021-127924NB-I00 (JD) and PID2022-137801NB-I00 (OH), both funded by MCIN/AEI/10.13039/501100011033, and by the VIRTUAL BRAIN TWIN project (HORIZON-HLTH-2023-TOOL-05-03, MG-L).

Santiago Ramón y Cajal’s pioneering work laid the foundations for modern neuroscience and continues to impact the development of artificial intelligence, particularly deep learning. His neuron theory, the principle of dynamic polarization, and his insights into brain plasticity and network organization have significantly influenced both our understanding of the nervous system and the design of artificial neural networks. This article reviews Cajal’s key contributions, explores their role in the evolution of AI, and emphasizes the enduring links between neuroscience and machine learning in the digital era., The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by grants PID2021-127924NB-I00 (JD) and PID2022-137801NB-I00 (OH), both funded by MCIN/AEI/10.13039/501100011033, and by the VIRTUAL BRAIN TWIN project (HORIZON-HLTH-2023-TOOL-05-03, MG-L).