arXiv subjects

Timothy W. Dunn

Publications and source records attributed to Timothy W. Dunn.

3 recordsLinked to original sources

arXiv2026

Liberata -- Graph Scientometrics for a Share Based System of Academic Publishing

Contemporary scientometric indicators remain anchored in paradigms and axioms from when academic research was conducted in small scholarly communities. With the global proliferation of scientific research, academia is now organized in large communities with high rates of information incompleteness regarding work impact and individual contributions. This has significant implications for how research output is measured and quality controlled, especially as the rate of academic publishing continues to rise. Exploits of complex systems are typically found at discrete transition points where rules turn on or off, and academia is not immune to this pattern. Exploitative career boosting strategies are a growing problem, largely enabled by misaligned incentives and traditional metrics that force discretization of credit to authors and prior works despite their fundamentally continuous nature. This article introduces Liberata's scientometrics, a share based framework for academic publishing and quality control. In this system, authorship positions are replaced with contribution shares that sum to unity and encode both ordinality and relative contribution distances. These shares can be traded on Liberata's academic marketplaces for quality control services such as peer review and replication, rewarding contributors based on the long term success of the work. Citations are weighted to guard against frivolous referencing and credit inflation, and modular correction factors allow multiple measures of impact. Liberata's metrics are formalized through two fundamental graphs, Shares and References, from which the system constructs academic capital and derives scientometrics capturing impact, risk, collaboration, collusion, value of quality control, and diversification. These metrics represent academic contributions and extend naturally to institutions, regions, time periods, and research fields.

cs.DL↗

arXiv2025

NLP4Neuro: Sequence-to-sequence learning for neural population decoding

Delineating how animal behavior arises from neural activity is a foundational goal of neuroscience. However, as the computations underlying behavior unfold in networks of thousands of individual neurons across the entire brain, this presents challenges for investigating neural roles and computational mechanisms in large, densely wired mammalian brains during behavior. Transformers, the backbones of modern large language models (LLMs), have become powerful tools for neural decoding from smaller neural populations. These modern LLMs have benefited from extensive pre-training, and their sequence-to-sequence learning has been shown to generalize to novel tasks and data modalities, which may also confer advantages for neural decoding from larger, brain-wide activity recordings. Here, we present a systematic evaluation of off-the-shelf LLMs to decode behavior from brain-wide populations, termed NLP4Neuro, which we used to test LLMs on simultaneous calcium imaging and behavior recordings in larval zebrafish exposed to visual motion stimuli. Through NLP4Neuro, we found that LLMs become better at neural decoding when they use pre-trained weights learned from textual natural language data. Moreover, we found that a recent mixture-of-experts LLM, DeepSeek Coder-7b, significantly improved behavioral decoding accuracy, predicted tail movements over long timescales, and provided anatomically consistent highly interpretable readouts of neuron salience. NLP4Neuro demonstrates that LLMs are highly capable of informing brain-wide neural circuit dissection.

q-bio.NC↗

arXiv2021

Leaving Flatland: Advances in 3D behavioral measurement

Animals move in three dimensions (3D). Thus, 3D measurement is necessary to report the true kinematics of animal movement. Existing 3D measurement techniques draw on specialized hardware, such as motion capture or depth cameras, as well as deep multi-view and monocular computer vision. Continued advances at the intersection of deep learning and computer vision will facilitate 3D tracking across more anatomical features, with less training data, in additional species, and within more natural, occlusive environments. 3D behavioral measurement enables unique applications in phenotyping, investigating the neural basis of behavior, and designing artificial agents capable of imitating animal behavior.

q-bio.NC↗