Biology subjects

Purdy, S.

Publications and source records attributed to Purdy, S..

3 recordsLinked to original sources

bioRxiv2023

Intra-horn insemination in the alpaca Vicugna pacos: Copulatory wounding and deep sperm deposition

Alpacas (Vicugna pacos) are reported to be the rare mammal in which the penis enters the uterus in mating. To date, however, only circumstantial evidence supports this assertion. Using female alpacas culled for meat, we determined that the alpaca penis penetrates to the very tips of the uterine horns, abrading the tract and breaking fine blood vessels. All female alpacas sacrificed one hour or 24 hours after mating showed conspicuous bleeding in the epithelium of some region of their reproductive tract, including the hymen, cervix and the tips of each uterine horn, but typically not in the vagina. Unmated females showed no evidence of conspicuous bleeding. Histological examination of mated females revealed widespread abrasion of the cervical and endometrial epithelium, injuries absent in unmated females. Within one hour of mating, sperm were already present in the oviduct. The male alpacas cartilaginous penis tip with a hardened urethral process is likely responsible for the copulatory abrasion. The entire female reproductive tract interacts with the penis, functioning like a vagina. Alpacas are induced ovulators, and wounding may hasten delivery of the seminal ovulation-inducing factor beta-NGF into the females blood stream. There is no evidence of sexual conflict in copulation in alpaca, and thus wounding may also be one of a variety of mechanisms devised by mammals to induce a beneficial, short-term inflammatory response that stimulates blastocyst implantation, the uterine remodeling associated with placental development, and thus the success of early pregnancy.

physiology↗

bioRxiv2018

A Framework for Intelligence and Cortical Function Based on Grid Cells in the Neocortex

How the neocortex works is a mystery. In this paper we propose a novel framework for understanding its function. Grid cells are neurons in the entorhinal cortex that represent the location of an animal in its environment. Recent evidence suggests that grid cell-like neurons may also be present in the neocortex. We propose that grid cells exist throughout the neocortex, in every region and in every cortical column. They define a location-based framework for how the neocortex functions. Whereas grid cells in the entorhinal cortex represent the location of one thing, the body relative to its environment, we propose that cortical grid cells simultaneously represent the location of many things. Cortical columns in somatosensory cortex track the location of tactile features relative to the object being touched and cortical columns in visual cortex track the location of visual features relative to the object being viewed. We propose that mechanisms in the entorhinal cortex and hippocampus that evolved for learning the structure of environments are now used by the neocortex to learn the structure of objects. Having a representation of location in each cortical column suggests mechanisms for how the neocortex represents object compositionality and object behaviors. It leads to the hypothesis that every part of the neocortex learns complete models of objects and that there are many models of each object distributed throughout the neocortex. The similarity of circuitry observed in all cortical regions is strong evidence that even high-level cognitive tasks are learned and represented in a location-based framework.

neuroscience↗

bioRxiv2018

Locations in the Neocortex: A Theory of Sensorimotor Object Recognition Using Cortical Grid Cells

The neocortex is capable of anticipating the sensory results of movement but the neural mechanisms are poorly understood. In the entorhinal cortex, grid cells represent the location of an animal in its environment, and this location is updated through movement and path integration. In this paper, we propose that sensory neocortex incorporates movement using grid cell-like neurons that represent the location of sensors on an object. We describe a two-layer neural network model that uses cortical grid cells and path integration to robustly learn and recognize objects through movement and predict sensory stimuli after movement. A layer of cells consisting of several grid cell-like modules represents a location in the reference frame of a specific object. Another layer of cells which processes sensory input receives this location input as context and uses it to encode the sensory input in the objects reference frame. Sensory input causes the network to invoke previously learned locations that are consistent with the input, and motor input causes the network to update those locations. Simulations show that the model can learn hundreds of objects even when object features alone are insufficient for disambiguation. We discuss the relationship of the model to cortical circuitry and suggest that the reciprocal connections between layers 4 and 6 fit the requirements of the model. We propose that the subgranular layers of cortical columns employ grid cell-like mechanisms to represent object specific locations that are updated through movement.

neuroscience↗