Psychophysical identity and free energy

An approach to implementing variational Bayesian inference in biological systems is considered, under which the thermodynamic free energy of a system directly encodes its variational free energy. In the case of the brain, this assumption places constraints on the neuronal encoding of generative and recognition densities, in particular requiring a stochastic population code. The resulting relationship between thermodynamic and variational free energies is prefigured in mind-brain identity theses in philosophy and in the Gestalt hypothesis of psychophysical isomorphism.Comment: 22 pages; published as a research article on 8/5/2020 in Journal of the Royal Society Interfac

A Defense of Pure Connectionism

Connectionism is an approach to neural-networks-based cognitive modeling that encompasses the recent deep learning movement in artificial intelligence. It came of age in the 1980s, with its roots in cybernetics and earlier attempts to model the brain as a system of simple parallel processors. Connectionist models center on statistical inference within neural networks with empirically learnable parameters, which can be represented as graphical models. More recent approaches focus on learning and inference within hierarchical generative models. Contra influential and ongoing critiques, I argue in this dissertation that the connectionist approach to cognitive science possesses in principle (and, as is becoming increasingly clear, in practice) the resources to model even the most rich and distinctly human cognitive capacities, such as abstract, conceptual thought and natural language comprehension and production. Consonant with much previous philosophical work on connectionism, I argue that a core principle—that proximal representations in a vector space have similar semantic values—is the key to a successful connectionist account of the systematicity and productivity of thought, language, and other core cognitive phenomena. My work here differs from preceding work in philosophy in several respects: (1) I compare a wide variety of connectionist responses to the systematicity challenge and isolate two main strands that are both historically important and reflected in ongoing work today: (a) vector symbolic architectures and (b) (compositional) vector space semantic models; (2) I consider very recent applications of these approaches, including their deployment on large-scale machine learning tasks such as machine translation; (3) I argue, again on the basis mostly of recent developments, for a continuity in representation and processing across natural language, image processing and other domains; (4) I explicitly link broad, abstract features of connectionist representation to recent proposals in cognitive science similar in spirit, such as hierarchical Bayesian and free energy minimization approaches, and offer a single rebuttal of criticisms of these related paradigms; (5) I critique recent alternative proposals that argue for a hybrid Classical (i.e. serial symbolic)/statistical model of mind; (6) I argue that defending the most plausible form of a connectionist cognitive architecture requires rethinking certain distinctions that have figured prominently in the history of the philosophy of mind and language, such as that between word- and phrase-level semantic content, and between inference and association

Relative representations for cognitive graphs

Although the latent spaces learned by distinct neural networks are not generally directly comparable, recent work in machine learning has shown that it is possible to use the similarities and differences among latent space vectors to derive "relative representations" with comparable representational power to their "absolute" counterparts, and which are nearly identical across models trained on similar data distributions. Apart from their intrinsic interest in revealing the underlying structure of learned latent spaces, relative representations are useful to compare representations across networks as a generic proxy for convergence, and for zero-shot model stitching. In this work we examine an extension of relative representations to discrete state-space models, using Clone-Structured Cognitive Graphs (CSCGs) for 2D spatial localization and navigation as a test case. Our work shows that the probability vectors computed during message passing can be used to define relative representations on CSCGs, enabling effective communication across agents trained using different random initializations and training sequences, and on only partially similar spaces. We introduce a technique for zero-shot model stitching that can be applied post hoc, without the need for using relative representations during training. This exploratory work is intended as a proof-of-concept for the application of relative representations to the study of cognitive maps in neuroscience and AI.Comment: 19 pages, 1 table, 6 figures. Accepted paper at the 4th International Workshop on Active Inference (Ghent, Belgium 2023

Target 2035-update on the quest for a probe for every protein

Twenty years after the publication of the first draft of the human genome, our knowledge of the human proteome is still fragmented. The challenge of translating the wealth of new knowledge from genomics into new medicines is that proteins, and not genes, are the primary executers of biological function. Therefore, much of how biology works in health and disease must be understood through the lens of protein function. Accordingly, a subset of human proteins has been at the heart of research interests of scientists over the centuries, and we have accumulated varying degrees of knowledge about approximately 65% of the human proteome. Nevertheless, a large proportion of proteins in the human proteome (∼35%) remains uncharacterized, and less than 5% of the human proteome has been successfully targeted for drug discovery. This highlights the profound disconnect between our abilities to obtain genetic information and subsequent development of effective medicines. Target 2035 is an international federation of biomedical scientists from the public and private sectors, which aims to address this gap by developing and applying new technologies to create by year 2035 chemogenomic libraries, chemical probes, and/or biological probes for the entire human proteome

The First Post-Kepler Brightness Dips of KIC 8462852

We present a photometric detection of the first brightness dips of the unique variable star KIC 8462852 since the end of the Kepler space mission in 2013 May. Our regular photometric surveillance started in October 2015, and a sequence of dipping began in 2017 May continuing on through the end of 2017, when the star was no longer visible from Earth. We distinguish four main 1-2.5% dips, named "Elsie," "Celeste," "Skara Brae," and "Angkor", which persist on timescales from several days to weeks. Our main results so far are: (i) there are no apparent changes of the stellar spectrum or polarization during the dips; (ii) the multiband photometry of the dips shows differential reddening favoring non-grey extinction. Therefore, our data are inconsistent with dip models that invoke optically thick material, but rather they are in-line with predictions for an occulter consisting primarily of ordinary dust, where much of the material must be optically thin with a size scale <<1um, and may also be consistent with models invoking variations intrinsic to the stellar photosphere. Notably, our data do not place constraints on the color of the longer-term "secular" dimming, which may be caused by independent processes, or probe different regimes of a single process

Moderate hypercapnia exerts beneficial effects on splanchnic energy metabolism during endotoxemia.

PURPOSE: Low tidal volume ventilation and permissive hypercapnia are required in patients with sepsis complicated by ARDS. The effects of hypercapnia on tissue oxidative metabolism in this setting are unknown. We therefore determined the effects of moderate hypercapnia on markers of systemic and splanchnic oxidative metabolism in an animal model of endotoxemia. METHODS: Anesthetized rats maintained at a PaCO(2) of 30, 40 or 60 mmHg were challenged with endotoxin. A control group (PaCO(2) 40 mmHg) received isotonic saline. Hemodynamic variables, arterial lactate, pyruvate, and ketone bodies were measured at baseline and after 4 h. Tissue adenosine triphosphate (ATP) and lactate were measured in the small intestine and the liver after 4 h. RESULTS: Endotoxin resulted in low cardiac output, increased lactate/pyruvate ratio and decreased ketone body ratio. These changes were not influenced by hypercapnia, but were more severe with hypocapnia. In the liver, ATP decreased and lactate increased independently from PaCO(2) after endotoxin. In contrast, the drop of ATP and the rise in lactate triggered by endotoxin in the intestine were prevented by hypercapnia. CONCLUSIONS: During endotoxemia in rats, moderate hypercapnia prevents the deterioration of tissue energetics in the intestine

Global Phylogeny of the Brassicaceae Provides Important Insights into Gene Discordance

The mustard family (Brassicaceae) is a scientifically and economically important family, containing the model plant Arabidopsis thaliana and numerous crop species that feed billions worldwide. Despite its relevance, most published family phylogenies are incompletely sampled, generally contain massive polytomies, and/or show incongruent topologies between datasets. Here, we present the most complete Brassicaceae genus-level family phylogenies to date (Brassicaceae Tree of Life, or BrassiToL) based on nuclear (>1,000 genes, almost all 349 genera and 53 tribes) and plastome (60 genes, 79% of the genera, all tribes) data. We found cytonuclear discordance between nuclear and plastome-derived phylogenies, which is likely a result of rampant hybridisation among closely and more distantly related species, and highlight rogue taxa. To evaluate the impact of this rampant hybridisation on the nuclear phylogeny reconstruction, we performed four different sampling routines that increasingly removed variable data and likely paralogs. Our resulting cleaned subset of 297 nuclear genes revealed high support for the tribes, while support for the main lineages remained relatively low. Calibration based on the 20 most clock-like nuclear genes suggests a late Eocene to late Oligocene ‘icehouse origin’ of the family. Finally, we propose five new or re-established tribes, including the recognition of Arabidopsideae, a monotypic tribe to accommodate Arabidopsis. With a worldwide community of thousands of researchers working on this family, our new, densely sampled family phylogeny will be an indispensable tool to further highlight Brassicaceae as an excellent model family for studies on biodiversity and plant biology

Intérêts et décision : élaboration d'un inventaire de décisions professionnelles simulées.

Approximately 90% of humans are right-handed. Handedness is a heritable trait, yet the genetic basis is not well understood. Here we report a genome-wide association study for a quantitative measure of relative hand skill in individuals with dyslexia [reading disability (RD)]. The most highly associated marker, rs11855415 (P = 4.7 × 10(−7)), is located within PCSK6. Two independent cohorts with RD show the same trend, with the minor allele conferring greater relative right-hand skill. Meta-analysis of all three RD samples is genome-wide significant (n = 744, P = 2.0 × 10(−8)). Conversely, in the general population (n = 2666), we observe a trend towards reduced laterality of hand skill for the minor allele (P = 0.0020). These results provide molecular evidence that cerebral asymmetry and dyslexia are linked. Furthermore, PCSK6 is a protease that cleaves the left–right axis determining protein NODAL. Functional studies of PCSK6 promise insights into mechanisms underlying cerebral lateralization and dyslexia