N. S. Jones

Soundscapes predict species occurrence in tropical forests

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

Bladder pressure encoding by near-independent fibre subpopulations - implications for decoding

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.

SAFE Acoustics: an open-source, real-time eco-acoustic monitoring network in the tropical rainforests of Borneo

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.

Combining machine learning and a universal acoustic feature-set yields efficient automated monitoring of ecosystems

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

Mitochondrial network state scales mtDNA genetic dynamics

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

The homeostatic dynamics of feeding behaviour identify novel mechanisms of anorectic agents

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.

Efficient peripheral nerve firing characterisation through massive feature extraction

The paper introduces a feature-based analysis method to characterize peripheral nerve firing by unsupervised dimensionality reduction, aiming to improve decoding in bioelectronic medicine devices.

Precision identification and prediction of high mortality phenotypes and disease progression pathways in severe malaria without requiring longitudinal data

Robust, real-time and autonomous monitoring of ecosystems with an open, low-cost, networked device

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.