Dopamine, affordance and active inference.

The role of dopamine in behaviour and decision-making is often cast in terms of reinforcement learning and optimal decision theory. Here, we present an alternative view that frames the physiology of dopamine in terms of Bayes-optimal behaviour. In this account, dopamine controls the precision or salience of (external or internal) cues that engender action. In other words, dopamine balances bottom-up sensory information and top-down prior beliefs when making hierarchical inferences (predictions) about cues that have affordance. In this paper, we focus on the consequences of changing tonic levels of dopamine firing using simulations of cued sequential movements. Crucially, the predictions driving movements are based upon a hierarchical generative model that infers the context in which movements are made. This means that we can confuse agents by changing the context (order) in which cues are presented. These simulations provide a (Bayes-optimal) model of contextual uncertainty and set switching that can be quantified in terms of behavioural and electrophysiological responses. Furthermore, one can simulate dopaminergic lesions (by changing the precision of prediction errors) to produce pathological behaviours that are reminiscent of those seen in neurological disorders such as Parkinson's disease. We use these simulations to demonstrate how a single functional role for dopamine at the synaptic level can manifest in different ways at the behavioural level

A Pluralist Account of Knowledge as a Natural Kind

In an attempt to address some long-standing issues of epistemology, Hilary Kornblith proposes that knowledge is a natural kind the identification of which is the unique responsibility of one particular science: cognitive ethology. As Kornblith sees it, the natural kind thus picked out is knowledge as construed by reliabilism. Yet the claim that cognitive ethology has this special role has not convinced all critics. The present article argues that knowledge plays a causal and explanatory role within many of our more fruitful current theories, diverging from the reliabilist conception even in disciplines that are closely related to cognitive ethology, and thus still dealing with knowledge as a natural as opposed to a social phenomenon, where special attention will be given to cognitive neuroscience. However, rather than discarding the natural kind approach altogether, it is argued that many of Kornblith’s insights can in fact be preserved within a framework that is both naturalist and pluralist

A New Replicator: A theoretical framework for analysing replication

<p>Abstract</p> <p>Background</p> <p>Replicators are the crucial entities in evolution. The notion of a replicator, however, is far less exact than the weight of its importance. Without identifying and classifying multiplying entities exactly, their dynamics cannot be determined appropriately. Therefore, it is importance to decide the nature and characteristics of any multiplying entity, in a detailed and formal way.</p> <p>Results</p> <p>Replication is basically an autocatalytic process which enables us to rest on the notions of formal chemistry. This statement has major implications. Simple autocatalytic cycle intermediates are considered as non-informational replicators. A consequence of which is that any autocatalytically multiplying entity is a replicator, be it simple or overly complex (even nests). A stricter definition refers to entities which can inherit acquired changes (informational replicators). Simple autocatalytic molecules (and nests) are excluded from this group. However, in turn, any entity possessing copiable information is to be named a replicator, even multicellular organisms. In order to deal with the situation, an abstract, formal framework is presented, which allows the proper identification of various types of replicators. This sheds light on the old problem of the units and levels of selection and evolution. A hierarchical classification for the partition of the replicator-continuum is provided where specific replicators are nested within more general ones. The classification should be able to be successfully applied to known replicators and also to future candidates.</p> <p>Conclusion</p> <p>This paper redefines the concept of the replicator from a bottom-up theoretical approach. The formal definition and the abstract models presented can distinguish between among all possible replicator types, based on their quantity of variable and heritable information. This allows for the exact identification of various replicator types and their underlying dynamics. The most important claim is that replication, in general, is basically autocatalysis, with a specific defined environment and selective force. A replicator is not valid unless its working environment, and the selective force to which it is subject, is specified.</p

Event Timing in Associative Learning: From Biochemical Reaction Dynamics to Behavioural Observations

Associative learning relies on event timing. Fruit flies for example, once trained with an odour that precedes electric shock, subsequently avoid this odour (punishment learning); if, on the other hand the odour follows the shock during training, it is approached later on (relief learning). During training, an odour-induced Ca++ signal and a shock-induced dopaminergic signal converge in the Kenyon cells, synergistically activating a Ca++-calmodulin-sensitive adenylate cyclase, which likely leads to the synaptic plasticity underlying the conditioned avoidance of the odour. In Aplysia, the effect of serotonin on the corresponding adenylate cyclase is bi-directionally modulated by Ca++, depending on the relative timing of the two inputs. Using a computational approach, we quantitatively explore this biochemical property of the adenylate cyclase and show that it can generate the effect of event timing on associative learning. We overcome the shortage of behavioural data in Aplysia and biochemical data in Drosophila by combining findings from both systems

Rapid Reversal of Chondroitin Sulfate Proteoglycan Associated Staining in Subcompartments of Mouse Neostriatum during the Emergence of Behaviour

BACKGROUND: The neostriatum, the mouse homologue of the primate caudate/putamen, is the input nucleus for the basal ganglia, receiving both cortical and dopaminergic input to each of its sub-compartments, the striosomes and matrix. The coordinated activation of corticostriatal pathways is considered vital for motor and cognitive abilities, yet the mechanisms which underlie the generation of these circuits are unknown. The early and specific targeting of striatal subcompartments by both corticostriatal and nigrostriatal terminals suggests activity-independent mechanisms, such as axon guidance cues, may play a role in this process. Candidates include the chondroitin sulfate proteoglycan (CSPG) family of glycoproteins which have roles not only in axon guidance, but also in the maturation and stability of neural circuits where they are expressed in lattice-like perineuronal nets (PNNs). METHODOLOGY/PRINCIPAL FINDINGS: The expression of CSPG-associated structures and PNNs with respect to neostriatal subcompartments has been examined qualitatively and quantitatively using double-labelling for Wisteria floribunda agglutinin (WFA), and the mu-opioid receptor (muOR), a marker for striosomes, at six postnatal ages in mice. We find that at the earliest ages (postnatal day (P)4 and P10), WFA-positive clusters overlap preferentially with the striosome compartment. By P14, these clusters disappear. In contrast, PNNs were first seen at P10 and continued to increase in density and spread throughout the caudate/putamen with maturation. Remarkably, the PNNs overlap almost exclusively with the neostriatal matrix. CONCLUSIONS/SIGNIFICANCE: This is the first description of a reversal in the distribution of CSPG associated structures, as well as the emergence and maintenance of PNNs in specific subcompartments of the neostriatum. These results suggest diverse roles for CSPGs in the formation of functional corticostriatal and nigrostriatal connectivity within the striosome and matrix compartments of the developing caudate/putamen

Natural Intelligence and Anthropic Reasoning

This paper aims to justify the concept of natural intelligence in the biosemiotic context. I will argue that the process of life is (i) a cognitive/semiotic process and (ii) that organisms, from bacteria to animals, are cognitive or semiotic agents. To justify these arguments, the neural-type intelligence represented by the form of reasoning known as anthropic reasoning will be compared and contrasted with types of intelligence explicated by four disciplines of biology – relational biology, evolutionary epistemology, biosemiotics and the systems view of life – not biased towards neural intelligence. The comparison will be achieved by asking questions related to the process of observation and the notion of true observers. To answer the questions I will rely on a range of established concepts including SETI (search for extraterrestrial intelligence), Fermi’s paradox, bacterial cognition, versions of the panspermia theory, as well as some newly introduced concepts including biocivilisations, cognitive/semiotic universes, and the cognitive/semiotic multiverse. The key point emerging from the answers is that the process of cognition/semiosis – the essence of natural intelligence – is a biological universal.Brunel University Londo

The impact of viral mutations on recognition by SARS-CoV-2 specific T cells.

We identify amino acid variants within dominant SARS-CoV-2 T cell epitopes by interrogating global sequence data. Several variants within nucleocapsid and ORF3a epitopes have arisen independently in multiple lineages and result in loss of recognition by epitope-specific T cells assessed by IFN-γ and cytotoxic killing assays. Complete loss of T cell responsiveness was seen due to Q213K in the A∗01:01-restricted CD8+ ORF3a epitope FTSDYYQLY207-215; due to P13L, P13S, and P13T in the B∗27:05-restricted CD8+ nucleocapsid epitope QRNAPRITF9-17; and due to T362I and P365S in the A∗03:01/A∗11:01-restricted CD8+ nucleocapsid epitope KTFPPTEPK361-369. CD8+ T cell lines unable to recognize variant epitopes have diverse T cell receptor repertoires. These data demonstrate the potential for T cell evasion and highlight the need for ongoing surveillance for variants capable of escaping T cell as well as humoral immunity.This work is supported by the UK Medical Research Council (MRC); Chinese Academy of Medical Sciences(CAMS) Innovation Fund for Medical Sciences (CIFMS), China; National Institute for Health Research (NIHR)Oxford Biomedical Research Centre, and UK Researchand Innovation (UKRI)/NIHR through the UK Coro-navirus Immunology Consortium (UK-CIC). Sequencing of SARS-CoV-2 samples and collation of data wasundertaken by the COG-UK CONSORTIUM. COG-UK is supported by funding from the Medical ResearchCouncil (MRC) part of UK Research & Innovation (UKRI),the National Institute of Health Research (NIHR),and Genome Research Limited, operating as the Wellcome Sanger Institute. T.I.d.S. is supported by a Well-come Trust Intermediate Clinical Fellowship (110058/Z/15/Z). L.T. is supported by the Wellcome Trust(grant number 205228/Z/16/Z) and by theUniversity of Liverpool Centre for Excellence in Infectious DiseaseResearch (CEIDR). S.D. is funded by an NIHR GlobalResearch Professorship (NIHR300791). L.T. and S.C.M.are also supported by the U.S. Food and Drug Administration Medical Countermeasures Initiative contract75F40120C00085 and the National Institute for Health Research Health Protection Research Unit (HPRU) inEmerging and Zoonotic Infections (NIHR200907) at University of Liverpool inpartnership with Public HealthEngland (PHE), in collaboration with Liverpool School of Tropical Medicine and the University of Oxford.L.T. is based at the University of Liverpool. M.D.P. is funded by the NIHR Sheffield Biomedical ResearchCentre (BRC – IS-BRC-1215-20017). ISARIC4C is supported by the MRC (grant no MC_PC_19059). J.C.K.is a Wellcome Investigator (WT204969/Z/16/Z) and supported by NIHR Oxford Biomedical Research Centreand CIFMS. The views expressed are those of the authors and not necessarily those of the NIHR or MRC

Investigating perturbed pathway modules from gene expression data via structural equation models

BACKGROUND: It is currently accepted that the perturbation of complex intracellular networks, rather than the dysregulation of a single gene, is the basis for phenotypical diversity. High-throughput gene expression data allow to investigate changes in gene expression profiles among different conditions. Recently, many efforts have been made to individuate which biological pathways are perturbed, given a list of differentially expressed genes (DEGs). In order to understand these mechanisms, it is necessary to unveil the variation of genes in relation to each other, considering the different phenotypes. In this paper, we illustrate a pipeline, based on Structural Equation Modeling (SEM) that allowed to investigate pathway modules, considering not only deregulated genes but also the connections between the perturbed ones. RESULTS: The procedure was tested on microarray experiments relative to two neurological diseases: frontotemporal lobar degeneration with ubiquitinated inclusions (FTLD-U) and multiple sclerosis (MS). Starting from DEGs and dysregulated biological pathways, a model for each pathway was generated using databases information biological databases, in order to design how DEGs were connected in a causal structure. Successively, SEM analysis proved if pathways differ globally, between groups, and for specific path relationships. The results confirmed the importance of certain genes in the analyzed diseases, and unveiled which connections are modified among them. CONCLUSIONS: We propose a framework to perform differential gene expression analysis on microarray data based on SEM, which is able to: 1) find relevant genes and perturbed biological pathways, investigating putative sub-pathway models based on the concept of disease module; 2) test and improve the generated models; 3) detect a differential expression level of one gene, and differential connection between two genes. This could shed light, not only on the mechanisms affecting variations in gene expression, but also on the causes of gene-gene relationship modifications in diseased phenotypes