THINGSvision: A Python toolbox for streamlining the extraction of activations from deep neural networks

Over the past decade, deep neural network (DNN) models have received a lot of attention due to their near-human object classification performance and their excellent prediction of signals recorded from biological visual systems. To better understand the function of these networks and relate them to hypotheses about brain activity and behavior, researchers need to extract the activations to images across different DNN layers. The abundance of different DNN variants, however, can often be unwieldy, and the task of extracting DNN activations from different layers may be non-trivial and error-prone for someone without a strong computational background. Thus, researchers in the fields of cognitive science and computational neuroscience would benefit from a library or package that supports a user in the extraction task. THINGSvision is a new Python module that aims at closing this gap by providing a simple and unified tool for extracting layer activations for a wide range of pretrained and randomly-initialized neural network architectures, even for users with little to no programming experience. We demonstrate the general utility of THINGsvision by relating extracted DNN activations to a number of functional MRI and behavioral datasets using representational similarity analysis, which can be performed as an integral part of the toolbox. Together, THINGSvision enables researchers across diverse fields to extract features in a streamlined manner for their custom image dataset, thereby improving the ease of relating DNNs, brain activity, and behavior, and improving the reproducibility of findings in these research fields

VICE: Variational Interpretable Concept Embeddings

A central goal in the cognitive sciences is the development of numerical models for mental representations of object concepts. This paper introduces Variational Interpretable Concept Embeddings (VICE), an approximate Bayesian method for embedding object concepts in a vector space using data collected from humans in an odd-one-out triplet task. VICE uses variational inference to obtain sparse, non-negative representations of object concepts with uncertainty estimates for the embedding values. These estimates are used to automatically select the dimensions that best explain the data. We derive a PAC learning bound for VICE that can be used to estimate generalization performance or determine sufficient sample size in experimental design. VICE rivals or outperforms its predecessor, SPoSE, at predicting human behavior in the odd-one-out triplet task. Furthermore, VICE's object representations are more reproducible and consistent across random initializations

Selenoether oxytocin analogues have analgesic properties in a mouse model of chronic abdominal pain

Poor oral availability and susceptibility to reduction and protease degradation is a major hurdle in peptide drug development. However, drugable receptors in the gut present an attractive niche for peptide therapeutics. Here we demonstrate, in a mouse model of chronic abdominal pain, that oxytocin receptors are significantly upregulated in nociceptors innervating the colon. Correspondingly, we develop chemical strategies to engineer non-reducible and therefore more stable oxytocin analogues. Chemoselective selenide macrocyclization yields stabilized analogues equipotent to native oxytocin. Ultra-high-field nuclear magnetic resonance structural analysis of native oxytocin and the seleno-oxytocin derivatives reveals that oxytocin has a pre-organized structure in solution, in marked contrast to earlier X-ray crystallography studies. Finally, we show that these seleno-oxytocin analogues potently inhibit colonic nociceptors both in vitro and in vivo in mice with chronic visceral hypersensitivity. Our findings have potentially important implications for clinical use of oxytocin analogues and disulphide-rich peptides in general

Getting aligned on representational alignment

Biological and artificial information processing systems form representations that they can use to categorize, reason, plan, navigate, and make decisions. How can we measure the extent to which the representations formed by these diverse systems agree? Do similarities in representations then translate into similar behavior? How can a system's representations be modified to better match those of another system? These questions pertaining to the study of representational alignment are at the heart of some of the most active research areas in cognitive science, neuroscience, and machine learning. For example, cognitive scientists measure the representational alignment of multiple individuals to identify shared cognitive priors, neuroscientists align fMRI responses from multiple individuals into a shared representational space for group-level analyses, and ML researchers distill knowledge from teacher models into student models by increasing their alignment. Unfortunately, there is limited knowledge transfer between research communities interested in representational alignment, so progress in one field often ends up being rediscovered independently in another. Thus, greater cross-field communication would be advantageous. To improve communication between these fields, we propose a unifying framework that can serve as a common language between researchers studying representational alignment. We survey the literature from all three fields and demonstrate how prior work fits into this framework. Finally, we lay out open problems in representational alignment where progress can benefit all three of these fields. We hope that our work can catalyze cross-disciplinary collaboration and accelerate progress for all communities studying and developing information processing systems. We note that this is a working paper and encourage readers to reach out with their suggestions for future revisions.Comment: Working paper, changes to be made in upcoming revision

In situ conjugation of dithiophenol maleimide polymers and oxytocin for stable and reversible polymer–peptide conjugates

The in situ one-pot synthesis of peptide–polymer bioconjugates is reported. Conjugation occurs efficiently without the need for purification of dithiophenol maleimide functionalized polymer as a disulfide bridging agent for the therapeutic oxytocin. Conjugation of polymers was demonstrated to be reversible and to significantly improve the solution stability of oxytocin

Conopressin-T from Conus tulipa reveals an antagonist switch in vasopressin-like peptide

We report the discovery of conopressing-T, a novel bioactive peptide isolated from Conus tulipa venom. Conopressin-T belongs to the vasopressin-like peptide family and displays high sequence homology to the mammalian hormone oxytocin (OT) and to vasotocin, the endogenerous vasopressin analogue found in the teleost fish, the cone snail's prey

Characterization of a novel alpha-conotoxin TxID from Conus textile that potently blocks rat alpha3/beta4 nicotinic acetylcholine receptors

The alpha 3 beta 4 nAChRs are implicated in pain sensation in the PNS and addiction to nicotine in the CNS. We identified an alpha-4/6-conotoxin (CTx) TxID from Conus textile. The new toxin consists of 15 amino acid residues with two disulfide bonds. TxID was synthesized using solid phase methods, and the synthetic peptide was functionally tested on nAChRs heterologously expressed in Xenopus laevis oocytes. TxID blocked rat alpha 3 beta 4 nAChRs with a 12.5 nM IC50, which places it among the most potent alpha 3 beta 4 nAChR antagonists. TxID also blocked the closely related alpha 6/alpha 3 beta 4 with a 94 nM IC50 but showed little activity on other nAChR subtypes. NMR analysis showed that two major structural isomers exist in solution, one of which adopts a regular alpha-CTx fold but with different surface charge distribution to other 4/6 family members. alpha-CTx TxID is a novel tool with which to probe the structure and function of alpha 3 beta 4 nAChRs

Evaluation of diverse peptidyl motifs for cellular delivery of semiconductor quantum dots Optical Nanosensing in Cells

Cell-penetrating peptides (CPPs) have rapidly become a mainstay technology for facilitating the delivery of a wide variety of nanomaterials to cells and tissues. Currently, the library of CPPs to choose from is still limited, with the HIV TAT-derived motif still being the most used. Among the many materials routinely delivered by CPPs, nanoparticles are of particular interest for a plethora of labeling, imaging, sensing, diagnostic, and therapeutic applications. The development of nanoparticle-based technologies for many of these uses will require access to a much larger number of functional peptide motifs that can both facilitate cellular delivery of different types of nanoparticles to cells and be used interchangeably in the presence of other peptides and proteins on the same surface. Here, we evaluate the utility of four peptidyl motifs for their ability to facilitate delivery of luminescent semiconductor quantum dots (QDs) in a model cell culture system. We find that an LAH4 motif, derived from a membrane-inserting antimicrobial peptide, and a chimeric sequence that combines a sweet arrow peptide with a portion originating from the superoxide dismutase enzyme provide effective cellular delivery of QDs. Interestingly, a derivative of the latter sequence lacking just a methyl group was found to be quite inefficient, suggesting that even small changes can have significant functional outcomes. Delivery was effected using 1 h incubation with cells, and fluorescent counterstaining strongly suggests an endosomal uptake process that requires a critical minimum number or ratio of peptides to be displayed on the QD surface. Concomitant cytoviability testing showed that the QD-peptide conjugates are minimally cytotoxic in the model COS-1 cell line tested. Potential applications of these peptides in the context of cellular delivery of nanoparticles and a variety of other (bio)molecules are discussed