Biology subjects

Farrell, M. S.

Publications and source records attributed to Farrell, M. S..

2 recordsLinked to original sources

bioRxiv2021

Schizophrenia-linked protein tSNARE1 regulates endolysosomal trafficking in cortical neurons

TSNARE1, which encodes the protein tSNARE1, is a high-confidence gene candidate for schizophrenia risk, but nothing is known about its cellular or physiological function. We identified the major gene products of TSNARE1 and their cytoplasmic localization and function in endolysosomal trafficking in murine cortical neurons. We validated four primary isoforms of TSNARE1 expressed in human brain, all of which encode a syntaxin-like Qa SNARE domain. RNA-sequencing data from adult and fetal human brain suggested that the majority of tSNARE1 lacks a transmembrane domain that is thought to be necessary for membrane fusion. Live-cell imaging demonstrated that brain tSNARE1 isoforms localized to compartments of the endosomal network. The most abundant brain isoform, tSNARE1c, localized most frequently to Rab7+ late endosomal compartments. Expression of either tSNARE1b or tSNARE1c, which differ only in their inclusion or exclusion of a Myb-like domain, delayed the trafficking of known endosomal cargo, Neep21, into late endosomal and lysosomal compartments. These data suggest that tSNARE1 regulates endosomal trafficking in cortical neurons, likely through negatively regulating endocytic trafficking or maturation of early endosomes to late endosomes.

neuroscience↗

bioRxiv2019

Dynamic compression and expansion in a classifying recurrent network

Recordings of neural circuits in the brain reveal extraordinary dynamical richness and high variability. At the same time, dimensionality reduction techniques generally uncover low-dimensional structures underlying these dynamics when tasks are performed. In general, it is still an open question what determines the dimensionality of activity in neural circuits, and what the functional role of this dimensionality in task learning is. In this work we probe these issues using a recurrent artificial neural network (RNN) model trained by stochastic gradient descent to discriminate inputs. The RNN family of models has recently shown promise in revealing principles behind brain function. Through simulations and mathematical analysis, we show how the dimensionality of RNN activity depends on the task parameters and evolves over time and over stages of learning. We find that common solutions produced by the network naturally compress dimensionality, while variability-inducing chaos can expand it. We show how chaotic networks balance these two factors to solve the discrimination task with high accuracy and good generalization properties. These findings shed light on mechanisms by which artificial neural networks solve tasks while forming compact representations that may generalize well.

neuroscience↗