Simulation of Neural Responses to Classical Music Using Organoid Intelligence Methods

TL;DR

The paper addresses the challenge of modeling neural responses to classical music through Organoid Intelligence (OI) and Organoid Learning (OL). It introduces the PyOrganoid library and the Pianoid model, which uses a Bidirectional LSTM to predict EEG from MFCC audio features and then maps those predictions to a 3D organoid-like simulation, enabling in vitro–style neural modeling of music perception. Evaluation shows a modest predictive correlation ($r \approx 0.51$) and spectral discrepancies in the predicted EEG, demonstrating feasibility while highlighting the need for more biologically realistic components and advanced architectures. Overall, the work provides a controllable, extensible platform for studying music cognition and biocomputing, bridging organoid systems with AI to advance neuroscience and neuroengineering.

Abstract

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.

Figures (9)

• Figure 1: A sample timestep from the EEG recording of subject #1 listening to classical piano music.
• Figure 2: Simulation history from an example Gene Regulation organoid Szelogowski_pyorganoid_A_Python_2022.
• Figure 3: Bidirectional LSTM Network, created with Tensorflow for OI interfacing.
• Figure 4: Bi-LSTM training and validation loss history.
• Figure 5: Bi-LSTM training and validation MAE history.