  Music is a complex auditory stimulus capable of eliciting significant changes
in brain activity, influencing cognitive processes such as memory, attention,
and emotional regulation. However, the underlying mechanisms of music-induced
cognitive processes remain largely unknown. Organoid intelligence and deep
learning models show promise for simulating and analyzing these neural
responses to classical music, an area significantly unexplored in computational
neuroscience. Hence, we present the PyOrganoid library, an innovative tool that
facilitates the simulation of organoid learning models, integrating
sophisticated machine learning techniques with biologically inspired organoid
simulations. Our study features the development of the Pianoid model, a "deep
organoid learning" model that utilizes a Bidirectional LSTM network to predict
EEG responses based on audio features from classical music recordings. This
model demonstrates the feasibility of using computational methods to replicate
complex neural processes, providing valuable insights into music perception and
cognition. Likewise, our findings emphasize the utility of synthetic models in
neuroscience research and highlight the PyOrganoid library's potential as a
versatile tool for advancing studies in neuroscience and artificial
intelligence.
