N. S. Jones

This paper shows that soundscapes can predict species occurrence in tropical forests without needing extensive labeled data, achieving high accuracy for many species.

This paper analyzes how bladder pressure is encoded by sensory fibers in cats using information theory, showing redundancy and independence among neuron types that improve decoding robustness.

The paper presents an open-source, real-time eco-acoustic monitoring network in Borneo's tropical rainforests, providing code, design, and infrastructure for data collection and analysis.

This paper develops a machine learning method using eco-acoustic data to monitor ecosystem changes and detect anomalies efficiently.

This study develops a theoretical framework linking mitochondrial network structure to genetic dynamics, showing that network state influences heteroplasmy variance and mutation clearance.

This study develops a stochastic model of rodent feeding behavior to analyze how anorectic agents affect feeding patterns and reveals that these agents do not mimic satiety.

The paper introduces an open-source, low-cost ecosystem monitoring system that autonomously captures and transmits continuous data, outperforming existing technologies at a fraction of the cost.