Biology subjects

Tripp, B.

Publications and source records attributed to Tripp, B..

3 recordsLinked to original sources

bioRxiv2022

Modelling the role of contour integration in visual inference

Under difficult viewing conditions, the brains visual system uses a variety of recurrent modulatory mechanisms to augment feed-forward processing. One resulting phenomenon is contour integration, which occurs in the primary visual (V1) cortex and strengthens neural responses to edges if they belong to a larger smooth contour. Computational models have contributed to an understanding of the circuit mechanisms of contour integration, but less is known about its role in visual perception. To address this gap, we embedded a biologically grounded model of contour integration in a task-driven artificial neural network, and trained it using a gradient-descent variant. We used this model to explore how brain-like contour integration may be optimized for high-level visual objectives as well as its potential roles in perception. When the model was trained to detect contours in a background of random edges, a task commonly used to examine contour integration in the brain, it closely mirrored the brain in terms of behavior, neural responses, and lateral connection patterns. When trained on natural images, the model enhanced weaker contours and distinguished whether two points lay on the same vs. different contours. The model learnt robust features that generalized well to out-of-training-distribution stimuli. Surprisingly, and in contrast with the synthetic task, a parameter-matched control network without recurrence performed the same or better than the model on the natural-image tasks. Thus a contour integration mechanism is not essential to perform these more naturalistic contour-related tasks. Finally, the best performance in all tasks was achieved by a modified contour integration model that did not distinguish between excitatory and inhibitory neurons. Author summaryDeep networks are machine-learning systems that consist of interconnected neuron-like elements. More than other kinds of artificial system, they rival human information processing in a variety of tasks. These structural and functional parallels have raised interest in using deep networks as simplified models of the brain, to better understand of brain function. For example, incorporating additional biological phenomena into deep networks may help to clarify how they affect brain function. In this direction, we adapted a deep network to incorporate a model of visual contour integration, a process in the brain that makes contours appear more visually prominent. We found that suitable training led this model to behave much like the corresponding brain circuits. We then investigated potential roles of the contour integration mechanism in processing of natural images, an important question that has been difficult to answer. The results were not straightforward. For example, the contour integration mechanism actually impaired the networks ability to tell whether two points lay on the same contour or not, but improved the networks ability to generalize this skill to a different group of images. Overall, this approach has raised more sophisticated questions about the role of contour integration in natural vision.

neuroscience↗

bioRxiv2021

CNN MouseNet: A biologically constrained convolutional neural network model for mouse visual cortex

Convolutional neural networks trained on object recognition derive inspiration from the neural architecture of the visual system in primates, and have been used as models of the feedforward computation performed in the primate ventral stream. In contrast to the deep hierarchical organization of primates, the visual system of the mouse has a shallower arrangement. Since mice and primates are both capable of visually guided behavior, this raises questions about the role of architecture in neural computation. In this work, we introduce a novel framework for building a biologically constrained convolutional neural network model of the mouse visual cortex. The architecture and structural parameters of the network are derived from experimental measurements, specifically the 100-micrometer resolution interareal connectome, the estimates of numbers of neurons in each area and cortical layer, and the statistics of connections between cortical layers. This network is constructed to support detailed task-optimized models of mouse visual cortex, with neural populations that can be compared to specific corresponding populations in the mouse brain. Using a well-studied image classification task as our working example, we demonstrate the computational capability of this mouse-sized network. Given its relatively small size, MouseNet achieves roughly 2/3rds the performance level on ImageNet as VGG16. In combination with the large scale Allen Brain Observatory Visual Coding dataset, we use representational similarity analysis to quantify the extent to which MouseNet recapitulates the neural representation in mouse visual cortex. Importantly, we provide evidence that optimizing for task performance does not improve similarity to the corresponding biological system beyond a certain point. We demonstrate that the distributions of some physiological quantities are closer to the observed distributions in the mouse brain after task training. We encourage the use of the MouseNet architecture by making the code freely available. Author summaryTask-driven deep neural networks have shown great potential in predicting functional responses of biological neurons. Nevertheless, they are not precise biological analogues, raising questions about how they should be interpreted. Here, we build new deep neural network models of the mouse visual cortex (MouseNet) that are biologically constrained in detail, not only in terms of the basic structure of their connectivity, but also in terms of the count and hence density of neurons within each area, and the spatial extent of their projections. Equipped with the MouseNet model, we can address key questions about mesoscale brain architecture and its role in task learning and performance.We ask, and provide a first set of answers, to: What is the performance of a mouse brain-sized - and mouse brain-structured - model on benchmark image classification tasks? How does the training of a network on this task affect the functional properties of specified layers within the biologically constrained architecture - both overall, and in comparison with recorded function of mouse neurons? We anticipate much future work on allied questions, and the development of more sophisticated models in both mouse and other species, based on the freely available MouseNet model and code which we develop and provide here.

neuroscience↗

bioRxiv2017

Phosphate starvation induces replacement of phospholipids with the betaine lipid diacylglycerol-N,N,N-trimethylhomoserine in the human fungal pathogen Candida albicans

We have previously demonstrated that phosphate starvation induces replacement of phosphatidylcholine with the betaine lipid diacylglyceryl-N,N,N-trimethylhomoserine (DGTS) in fungi. In Neurospora crassa, the BTA1 gene encodes the betaine lipid synthase, which is necessary and sufficient for DGTS synthesis. BTA1 expression and DGTS accumulation are part of the fungal phosphorus (Pi) deprivation (PHO) regulon, mediated by the NUC-1/Pho4p transcription factor. We now demonstrate that the human pathogen Candida albicans encodes a BTA1 ortholog (CaBTA1), which is activated during Pi scarcity. The CaBTA1 gene is also induced under certain biofilm-promoting conditions independent of Pi starvation. RNA-seq and qRT-PCR showed a significant increase in CaBTA1 expression in response to Pi limitation. Thin-layer chromatography and LC-ESI-MS/MS confirmed the replacement of PC with DGTS in wild-type under low Pi and showed the absence of DGTS in the bta1{Delta}{Delta} mutant.\n\nPi limitation in the gut of critically ill patients also triggers the switching of C. albicans into an invasive filamentous form. To assess the role of BTA1 and DGTS in the pathogenicity of C. albicans in vitro, we compared the growth and morphology of bta1{Delta}{Delta} and wild type in hyphaeinducing media and observed defects in biofilm formation and invasive growth in the bta1{Delta}{Delta} mutant. This observation is complemented by RNA-seq data demonstrating that Pi starvation in planktonic C. albicans cells induces the expression of virulence-associated cell surface proteins. Taken together, these results show novel functional interactions between lipid metabolism and remodeling, biofilm formation, and the phosphate starvation response of C. albicans.

microbiology↗