scholarly journals Active Inference, Evidence Accumulation, and the Urn Task

2015 ◽  
Vol 27 (2) ◽  
pp. 306-328 ◽  
Author(s):  
Thomas H. B. FitzGerald ◽  
Philipp Schwartenbeck ◽  
Michael Moutoussis ◽  
Raymond J. Dolan ◽  
Karl Friston
Keyword(s):  
Bayesian Inference ◽  
Dopaminergic Neurons ◽  
Functional Anatomy ◽  
Generic Model ◽  
Active Inference ◽  
Evidence Accumulation ◽  
Dopaminergic Dysfunction ◽  
Markov Decision ◽  
Information Sampling ◽  
The Brain

Deciding how much evidence to accumulate before making a decision is a problem we and other animals often face, but one that is not completely understood. This issue is particularly important because a tendency to sample less information (often known as reflection impulsivity) is a feature in several psychopathologies, such as psychosis. A formal understanding of information sampling may therefore clarify the computational anatomy of psychopathology. In this theoretical letter, we consider evidence accumulation in terms of active (Bayesian) inference using a generic model of Markov decision processes. Here, agents are equipped with beliefs about their own behavior—in this case, that they will make informed decisions. Normative decision making is then modeled using variational Bayes to minimize surprise about choice outcomes. Under this scheme, different facets of belief updating map naturally onto the functional anatomy of the brain (at least at a heuristic level). Of particular interest is the key role played by the expected precision of beliefs about control, which we have previously suggested may be encoded by dopaminergic neurons in the midbrain. We show that manipulating expected precision strongly affects how much information an agent characteristically samples, and thus provides a possible link between impulsivity and dopaminergic dysfunction. Our study therefore represents a step toward understanding evidence accumulation in terms of neurobiologically plausible Bayesian inference and may cast light on why this process is disordered in psychopathology.

Sensory evidence integration and action initiation occur in parallel during perceptual decisions

2020 ◽  
Author(s):  
Lluís Hernández-Navarro ◽  
Ainhoa Hermoso-Mendizabal ◽  
Daniel Duque ◽  
Alexandre Hyafil ◽  
Jaime de la Rocha
Keyword(s):  
Time Pressure ◽  
Reaction Times ◽  
Stimulus Presentation ◽  
Perceptual Decision Making ◽  
Evidence Accumulation ◽  
Action Preparation ◽  
Action Model ◽  
The Brain ◽  
Evidence Integration ◽  
Action Initiation

It is commonly assumed that, during perceptual decisions, the brain integrates stimulus evidence until reaching a decision, and then performs the response. There are conditions, however (e.g. time pressure), in which the initiation of the response must be prepared in anticipation of the stimulus presentation. It is therefore not clear when the timing and the choice of perceptual responses depend exclusively on evidence accumulation, or when preparatory motor signals may interfere with this process. Here, we find that, in a free reaction time auditory discrimination task in rats, the timing of fast responses does not depend on the stimulus, although the choices do, suggesting a decoupling of the mechanisms of action initiation and choice selection. This behavior is captured by a novel model, the Parallel Sensory Integration and Action Model (PSIAM), in which response execution is triggered whenever one of two processes, Action Initiation or Evidence Accumulation, reaches a bound, while choice category is always set by the latter. Based on this separation, the model accurately predicts the distribution of reaction times when the stimulus is omitted, advanced or delayed. Furthermore, we show that changes in Action Initiation mediates both post-error slowing and a gradual slowing of the responses within each session. Overall, these results extend the standard models of perceptual decision-making, and shed a new light on the interaction between action preparation and evidence accumulation.

Comparison of Cytocidal Activities of L-DOPA and Dopamine in S. cerevisiae and C. glabrata

2020 ◽  
Vol 16 (1) ◽  
pp. 90-93
Author(s):  
Carmen E. Iriarte ◽  
Ian G. Macreadie
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Dopaminergic Neurons ◽  
Candida Glabrata ◽  
High Sensitivity ◽  
Time Dependent ◽  
Colony Forming Units ◽  
Dependent Cell ◽  
The Brain

Background: Parkinson's Disease results from a loss of dopaminergic neurons, and reduced levels of the neurotransmitter dopamine. Parkinson's Disease treatments involve increasing dopamine levels through administration of L-DOPA, which can cross the blood brain barrier and be converted to dopamine in the brain. The toxicity of dopamine has previously studied but there has been little study of L-DOPA toxicity. Methods: We have compared the toxicity of dopamine and L-DOPA in the yeasts, Saccharomyces cerevisiae and Candida glabrata by cell viability assays, measuring colony forming units. Results: L-DOPA and dopamine caused time-dependent cell killing in Candida glabrata while only dopamine caused such effects in Saccharomyces cerevisiae. The toxicity of L-DOPA is much lower than dopamine. Conclusion: Candida glabrata exhibits high sensitivity to L-DOPA and may have advantages for studying the cytotoxicity of L-DOPA.

Consciousness

2018 ◽  
Author(s):  
Anil K. Seth
Keyword(s):  
Conscious Experience ◽  
Real Problem ◽  
Active Inference ◽  
Biological Mechanisms ◽  
Specific Content ◽  
Brain Responses ◽  
Biomimetic Approach ◽  
Multiple Levels ◽  
In Virtue Of ◽  
The Brain

Consciousness is perhaps the most familiar aspect of our existence, yet we still do not know its biological basis. This chapter outlines a biomimetic approach to consciousness science, identifying three principles linking properties of conscious experience to potential biological mechanisms. First, conscious experiences generate large quantities of information in virtue of being simultaneously integrated and differentiated. Second, the brain continuously generates predictions about the world and self, which account for the specific content of conscious scenes. Third, the conscious self depends on active inference of self-related signals at multiple levels. Research following these principles helps move from establishing correlations between brain responses and consciousness towards explanations which account for phenomenological properties—addressing what can be called the “real problem” of consciousness. The picture that emerges is one in which consciousness, mind, and life, are tightly bound together—with implications for any possible future “conscious machines.”

scholarly journals Synergistic Effects of Milk-Derived Exosomes and Galactose on α-Synuclein Pathology in Parkinson’s Disease and Type 2 Diabetes Mellitus

2021 ◽  
Vol 22 (3) ◽  
pp. 1059
Author(s):  
Bodo C. Melnik
Keyword(s):  
Oxidative Stress ◽  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Dopaminergic Neurons ◽  
Vagal Nerve ◽  
Β Cells ◽  
Pancreatic Β Cells ◽  
The Brain

Epidemiological studies associate milk consumption with an increased risk of Parkinson’s disease (PD) and type 2 diabetes mellitus (T2D). PD is an α-synucleinopathy associated with mitochondrial dysfunction, oxidative stress, deficient lysosomal clearance of α-synuclein (α-syn) and aggregation of misfolded α-syn. In T2D, α-syn promotes co-aggregation with islet amyloid polypeptide in pancreatic β-cells. Prion-like vagal nerve-mediated propagation of exosomal α-syn from the gut to the brain and pancreatic islets apparently link both pathologies. Exosomes are critical transmitters of α-syn from cell to cell especially under conditions of compromised autophagy. This review provides translational evidence that milk exosomes (MEX) disturb α-syn homeostasis. MEX are taken up by intestinal epithelial cells and accumulate in the brain after oral administration to mice. The potential uptake of MEX miRNA-148a and miRNA-21 by enteroendocrine cells in the gut, dopaminergic neurons in substantia nigra and pancreatic β-cells may enhance miRNA-148a/DNMT1-dependent overexpression of α-syn and impair miRNA-148a/PPARGC1A- and miRNA-21/LAMP2A-dependent autophagy driving both diseases. MiRNA-148a- and galactose-induced mitochondrial oxidative stress activate c-Abl-mediated aggregation of α-syn which is exported by exosome release. Via the vagal nerve and/or systemic exosomes, toxic α-syn may spread to dopaminergic neurons and pancreatic β-cells linking the pathogenesis of PD and T2D.

scholarly journals Importance of Nanoparticles for the Delivery of Antiparkinsonian Drugs

Pharmaceutics ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 508
Author(s):  
Sara Silva ◽  
António J. Almeida ◽  
Nuno Vale
Keyword(s):  
Side Effects ◽  
Dopaminergic Neurons ◽  
Lewy Bodies ◽  
Pharmacokinetic Profile ◽  
The Elderly ◽  
Motor Symptoms ◽  
Administration Routes ◽  
Pharmaceutical Therapy ◽  
Drug Pharmacokinetic ◽  
The Brain

Parkinson’s disease (PD) affects around ten million people worldwide and is considered the second most prevalent neurodegenerative disease after Alzheimer’s disease. In addition, there is a higher risk incidence in the elderly population. The main PD hallmarks include the loss of dopaminergic neurons and the development of Lewy bodies. Unfortunately, motor symptoms only start to appear when around 50–70% of dopaminergic neurons have already been lost. This particularly poses a huge challenge for early diagnosis and therapeutic effectiveness. Actually, pharmaceutical therapy is able to relief motor symptoms, but as the disease progresses motor complications and severe side-effects start to appear. In this review, we explore the research conducted so far in order to repurpose drugs for PD with the use of nanodelivery systems, alternative administration routes, and nanotheranostics. Overall, studies have demonstrated great potential for these nanosystems to target the brain, improve drug pharmacokinetic profile, and decrease side-effects.

scholarly journals Modules or Mean-Fields?

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 552 ◽  
Author(s):  
Thomas Parr ◽  
Noor Sajid ◽  
Karl J. Friston
Keyword(s):  
Steady State ◽  
Message Passing ◽  
Mean Field Theory ◽  
Functional Anatomy ◽  
Mean Field ◽  
State Density ◽  
Stochastic Dynamical Systems ◽  
Conditional Independency ◽  
The Brain ◽  
Non Equilibrium

The segregation of neural processing into distinct streams has been interpreted by some as evidence in favour of a modular view of brain function. This implies a set of specialised ‘modules’, each of which performs a specific kind of computation in isolation of other brain systems, before sharing the result of this operation with other modules. In light of a modern understanding of stochastic non-equilibrium systems, like the brain, a simpler and more parsimonious explanation presents itself. Formulating the evolution of a non-equilibrium steady state system in terms of its density dynamics reveals that such systems appear on average to perform a gradient ascent on their steady state density. If this steady state implies a sufficiently sparse conditional independency structure, this endorses a mean-field dynamical formulation. This decomposes the density over all states in a system into the product of marginal probabilities for those states. This factorisation lends the system a modular appearance, in the sense that we can interpret the dynamics of each factor independently. However, the argument here is that it is factorisation, as opposed to modularisation, that gives rise to the functional anatomy of the brain or, indeed, any sentient system. In the following, we briefly overview mean-field theory and its applications to stochastic dynamical systems. We then unpack the consequences of this factorisation through simple numerical simulations and highlight the implications for neuronal message passing and the computational architecture of sentience.

A bimodal probe for fluorescence and synchrotron X-ray fluorescence imaging of dopaminergic neurons in brain

2022 ◽  
Author(s):  
Meiling Yan ◽  
Tingting Zuo ◽  
Jichao Zhang ◽  
Yiyang Wang ◽  
Ying Zhu ◽  
...  
Keyword(s):  
Fluorescence Imaging ◽  
Immunoglobulin G ◽  
Dopaminergic Neurons ◽  
X Ray ◽  
Erythrosine B ◽  
The Brain

A bimodal probe, erythrosine B (EB) conjugated immunoglobulin G complex (EB/IgG), has been developed for fluorescence and synchrotron X-ray fluorescence (SXRF) imaging of dopaminergic neurons in the brain.

scholarly journals Pre-stimulus alpha activities predict the confidence in subjective judgment by modulating post-stimulus theta oscillation

2021 ◽  
Author(s):  
Haoyue Qian ◽  
Yang Lu ◽  
Zhiyuan Liu ◽  
Xue Weng ◽  
Xiuyan Guo ◽  
...  
Keyword(s):  
Judgment Task ◽  
Neural Oscillation ◽  
Brain Areas ◽  
Subjective Judgment ◽  
Alpha Oscillations ◽  
Evidence Accumulation ◽  
Decision Confidence ◽  
Boundary Process ◽  
Specific Pathway ◽  
The Brain

AbstractEven when making arbitrary decisions, people tend to feel varying levels of confidence, which is associated with the pre-stimulus neural oscillation of the brain. We investigated varying confidence in a pure subjective judgment task, and how this confidence was predicted by pre-stimulus alpha oscillations. Participants made pure subjective judgments where their prior experience seems to be helpful but actually useless, and their fluctuating confidence was related to the choice boundary process rather than the evidence accumulation process, suggesting participants underwent varying confidence resulting from the internal signals. With EEG and MEG analyses, we not only revealed the linkage between confidence and pre-stimulus alpha activities, but also successfully located this linkage onto decision-making relevant brain areas, i.e. MCC/PCC and SMA. Moreover, we unveiled a specific pathway underlying such linkage, that is, the influence of pre-stimulus alpha activities on decision confidence was fulfilled through modulating post-stimulus theta activities of SMA.

scholarly journals Keep your interoceptive stream under control: an active inference perspective on Anorexia Nervosa.

2018 ◽  
Author(s):  
Laura Barca ◽  
giovanni pezzulo
Keyword(s):  
Eating Disorders ◽  
Anorexia Nervosa ◽  
Cognitive Control ◽  
Western World ◽  
Active Inference ◽  
Extensive Body ◽  
The Brain ◽  
Computational Level

Eating disorders, and in particular anorexia nervosa (AN), are widespread in the western world. Despite an extensive body of research, the mechanisms underlying anorexia nervosa and its striking eating restriction are still elusive. Here we propose an innovative account of anorexia, which elaborates on recent theories of the brain as a predictive machine (Friston, 2010; Friston et al., 2017; Pezzulo, Barca, Friston, 2015). Here we use this (active) interoceptive inference account to explain the starvation behavior characterizing restrictive anorexia. This novel perspective aims at merging computational-level constructs of active inference and altered interoceptive processing to psychological-level theories of cognitive control and self-coherence.

scholarly journals Active Inferants: An Active Inference Framework for Ant Colony Behavior

2021 ◽  
Vol 15 ◽  
Author(s):  
Daniel Ari Friedman ◽  
Alec Tschantz ◽  
Maxwell J. D. Ramstead ◽  
Karl Friston ◽  
Axel Constant
Keyword(s):  
Theoretical Biology ◽  
Behavioral Model ◽  
Ant Colony ◽  
Behavioral Modeling ◽  
Multiscale Approach ◽  
Active Inference ◽  
Behavioral Experiments ◽  
Case System ◽  
Markov Decision ◽  
Trail Formation

In this paper, we introduce an active inference model of ant colony foraging behavior, and implement the model in a series of in silico experiments. Active inference is a multiscale approach to behavioral modeling that is being applied across settings in theoretical biology and ethology. The ant colony is a classic case system in the function of distributed systems in terms of stigmergic decision-making and information sharing. Here we specify and simulate a Markov decision process (MDP) model for ant colony foraging. We investigate a well-known paradigm from laboratory ant colony behavioral experiments, the alternating T-maze paradigm, to illustrate the ability of the model to recover basic colony phenomena such as trail formation after food location discovery. We conclude by outlining how the active inference ant colony foraging behavioral model can be extended and situated within a nested multiscale framework and systems approaches to biology more generally.

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