Molecular Neural Recording: A Conceptual and Computational Framework for Event-Driven Neural Encoding

Abstract Neural recording technologies have significantly advanced our understanding of brain function; however, they remain constrained by fundamental trade-offs among temporal resolution, spatial scalability, invasiveness, and long-term data acquisition. These limitations restrict the ability to capture large-scale neural dynamics over extended durations. To address this challenge, we propose a conceptual and computational framework for molecular neural recording, in which neural activity is encoded into nucleic acid–like representations through biologically inspired processes. In the proposed framework, high-frequency neural spike activity is first transformed through a temporal buffering mechanism that mimics intracellular biochemical integration, enabling the conversion of rapid signals into slower, recordable states. The buffered signals are then discretized into event-driven representations and mapped into simplified molecular encodings with stochastic variability, reflecting the probabilistic nature of biological systems. Rather than attempting precise spike-level recording, the approach focuses on capturing temporally integrated neural dynamics that can be reconstructed computationally. A simulation pipeline was developed to evaluate the feasibility of the framework, incorporating neural signal generation, buffering, encoding, decoding, and reconstruction. Reconstruction performance was assessed using correlation and normalized mean squared error (MSE). The results demonstrate that neural activity patterns can be recovered with moderate fidelity (correlation ≈ 0.584; normalized MSE ≈ 0.045), indicating preservation of essential temporal structure despite compression and noise. These findings establish molecular neural recording as a viable event-driven encoding paradigm that balances biological constraints with computational reconstruction. By integrating concepts from Neuroinformatics, synthetic biology, and signal processing, this work provides a foundation for scalable, long-term neural recording systems and opens new directions for brain mapping, neurotechnology, and biologically embedded data storage.