Methods for recording brain activity surged extraordinary development in recent years, and a significant discrepancy now exists between the abundance of available data and the limited headway in achieving a shared theoretical framework. This chasm is evident at the micro and meso scale, where a cohesive formalization describing interactions of large neural populations is still lacking. Here, we introduce a general mathematical formalism to interpret empirical observations from systems of interacting neurons in terms of lattice field theory, an established paradigm from theoretical particle physics and statistical mechanics. Bridging particle physics and neurophysiology, we derive an energy functional that generalizes the maximum entropy model for neural networks and includes a traditional approach such as the Principal Component Analysis as a sub-case. This ascribes otherwise unrelated results to universal physical principles, paving the way to particle physics-inspired models of the brain.
Neural activity in quarks language / Bardella, Giampiero; Franchini, Simone; Pan, Liming; Balzan, Riccardo; Fontana, Roberto; Giarrocco, Franco; Ramawat, Surabhi; Brunamonti, Emiliano; Pani, Pierpaolo; Ferraina, Stefano. - (2023). [10.48550/ARXIV.2310.09178]
Neural activity in quarks language
Bardella, Giampiero
Primo
Writing – Review & Editing
;Franchini, SimoneSecondo
Writing – Review & Editing
;Fontana, RobertoData Curation
;Giarrocco, Franco;Ramawat, Surabhi;Brunamonti, EmilianoFunding Acquisition
;Pani, PierpaoloResources
;Ferraina, StefanoUltimo
Funding Acquisition
2023
Abstract
Methods for recording brain activity surged extraordinary development in recent years, and a significant discrepancy now exists between the abundance of available data and the limited headway in achieving a shared theoretical framework. This chasm is evident at the micro and meso scale, where a cohesive formalization describing interactions of large neural populations is still lacking. Here, we introduce a general mathematical formalism to interpret empirical observations from systems of interacting neurons in terms of lattice field theory, an established paradigm from theoretical particle physics and statistical mechanics. Bridging particle physics and neurophysiology, we derive an energy functional that generalizes the maximum entropy model for neural networks and includes a traditional approach such as the Principal Component Analysis as a sub-case. This ascribes otherwise unrelated results to universal physical principles, paving the way to particle physics-inspired models of the brain.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.