Biology subjects

Field, D. J.

Publications and source records attributed to Field, D. J..

23 records · Page 2Linked to original sources

bioRxiv2022

40 new specimens of Ichthyornis provide unprecedented insight into the postcranial morphology of crownward stem group birds.

Ichthyornis has long been recognized as a pivotally important fossil taxon for understanding the latest stages of the dinosaur-bird transition, but little significant new postcranial material has been brought to light since initial descriptions of partial skeletons in the 19th Century. Here, we present new information on the postcranial morphology of Ichthyornis from 40 previously undescribed specimens, providing the most detailed morphological assessment of Ichthyornis to date. The new material includes four partially complete skeletons and numerous well-preserved isolated elements, enabling new anatomical observations such as muscle attachments previously undescribed for Mesozoic euornitheans. Among the elements that were previously unknown or poorly represented for Ichthyornis, the new specimens include an almost-complete axial series, a hypocleideum-bearing furcula, radial carpal bones, fibulae, a complete tarsometatarsus bearing a rudimentary hypotarsus, and one of the first-known nearly complete three-dimensional sterna from a Mesozoic avialan. Several pedal phalanges are preserved, revealing a remarkably enlarged pes presumably related to foot-propelled swimming. Although diagnosable as Ichthyornis, the new specimens exhibit a substantial degree of morphological variation, some of which may relate to ontogenetic changes. Phylogenetic analyses incorporating our new data and employing alternative morphological datasets recover Ichthyornis stemward of Hesperornithes and Iaceornis, in line with some recent hypotheses regarding the topology of the crownward-most portion of the avian stem group, and we establish phylogenetically-defined clade names for relevant avialan subclades to help facilitate consistent discourse in future work. The new information provided by these specimens improves our understanding of morphological evolution among the crownward-most non-neornithine avialans immediately preceding the origin of crown group birds.

paleontology↗

bioRxiv2021

Dynamic Electrode-to-Image (DETI) Mapping Reveals the Human Brain's Spatiotemporal Code of Visual Information

A chief goal of systems neuroscience is to understand how the brain encodes information in our visual environments. Understanding that neural code is crucial to explaining how visual content is transformed via subsequent semantic representations to enable intelligent behavior. Although the visual code is not static, this reality is often obscured in voxel-wise encoding models of BOLD signals due to fMRIs poor temporal resolution. We leveraged the high temporal resolution of EEG to develop an encoding technique based in state-space theory. This approach maps neural signals to each pixel within a given image and reveals location-specific transformations of the visual code, providing a spatiotemporal signature for the image at each electrode. This technique offers a spatiotemporal visualization of the evolution of the neural code of visual information thought impossible to obtain from EEG and promises to provide insight into how visual meaning is developed through dynamic feedforward and recurrent processes.

neuroscience↗

bioRxiv2020

Wing musculature reconstruction in extinct flightless auks (Pinguinus and Mancalla) reveals incomplete convergence with penguins (Spheniscidae) due to differing ancestral states

Despite longstanding interest in convergent evolution, factors that result in deviations from fully convergent phenotypes remain poorly understood. In birds, the evolution of flightless wing-propelled diving has emerged as a classic example of convergence, having arisen in disparate lineages including penguins (Sphenisciformes) and auks (Pan-Alcidae, Charadriiformes). Nevertheless, little is known about the functional anatomy of the wings of flightless auks because all such taxa are extinct, and their morphology is almost exclusively represented by skeletal remains. Here, in order to re-evaluate the extent of evolutionary convergence among flightless wing-propelled divers, wing muscles and ligaments were reconstructed in two extinct flightless auks, representing independent transitions to flightlessness: Pinguinus impennis (a crown-group alcid), and Mancalla (a stem-group alcid). Extensive anatomical data were gathered from dissections of 12 species of extant charadriiforms and 4 aequornithine waterbirds including a penguin. It was found that the wings of both flightless auk taxa were characterized by an increased mechanical advantage of wing elevator/retractor muscles, and decreased mobility of distal wing joints, both of which are likely advantageous for wing-propelled diving and parallel similar functional specializations in penguins. However, the conformations of individual muscles and ligaments underlying these specializations differ markedly between penguins and flightless auks, instead resembling those in each respective groups close relatives. Thus, the wings of these flightless wing-propelled divers can be described as convergent as overall functional units, but are incompletely convergent at lower levels of anatomical organization--a result of retaining differing conditions from each groups respective volant ancestors. Detailed investigations such as this one may indicate that, even in the face of similar functional demands, courses of phenotypic evolution are dictated to an important degree by ancestral starting points.

evolutionary biology↗

bioRxiv2019

Selective and invariant features of neural response surfaces measured with principal curvature

The responses of most visual cortical neurons are highly nonlinear functions of image stimuli. With the sparse coding network, a recurrent model of V1 computation, we apply techniques from differential geometry to these nonlinear responses and classify them as forms of selectivity or invariance. The selectivity and invariance of responses of individual neurons are quantified by measuring the principal curvatures of neural response surfaces in high-dimensional image space. An extended two-layer version of the network model that captures some properties of higher visual cortical areas is also characterized using this approach. We argue that this geometric view allows for the quantification of feature selectivity and invariance in network models in a way that provides insight into the computations necessary for object recognition.

neuroscience↗

bioRxiv2019

Towards a state-space geometry of neural responses to natural scenes: A steady-state approach

Our understanding of information processing by the mammalian visual system has come through a variety of techniques ranging from psychophysics and fMRI to single unit recording and EEG. Each technique provides unique insights into the processing framework of the early visual system. Here, we focus on the nature of the information that is carried by steady state visual evoked potentials (SSVEPs). To study the information provided by SSVEPs, we presented human participants with a population of natural scenes and measured the relative SSVEP response. Rather than focus on particular features of this signal, we focused on the full state-space of possible responses and investigated how the evoked responses are mapped onto this space. Our results show that it is possible to map the relatively high-dimensional signal carried by SSVEPs onto a 2-dimensional space with little loss. We also show that a simple biologically plausible model can account for a high proportion of the explainable variance ([~]73%) in that space. Finally, we describe a technique for measuring the mutual information that is available about images from SSVEPs. The techniques introduced here represent a new approach to understanding the nature of the information carried by SSVEPs. Crucially, this approach is general and can provide a means of comparing results across different neural recording methods. Altogether, our study sheds light on the encoding principles of early vision and provides a much needed reference point for understanding subsequent transformations of the early visual response space to deeper knowledge structures that link different visual environments.

neuroscience↗