1,987 publications from this institution
Abstract Under natural conditions, animals repeatedly encounter the same visual scenes, objects or patterns repeatedly. These repetitions constitute statistical regularities, which the brain captures in an internal model through learning. A signature of such learning in primate visual areas V1 and V4 is the gradual strengthening of gamma synchronization. We used a V1-V4 Dynamic Causal Model (DCM) to explain visually induced responses in early and late epochs from a sequence of several hundred grating presentations. The DCM reproduced the empirical increase in local and inter-areal gamma synchronization, revealing specific intrinsic connectivity effects that could explain the phenomenon. In a sensitivity analysis, the isolated modulation of several connection strengths induced increased gamma. Comparison of alternative models showed that empirical gamma increases are better explained by (1) repetition effects in both V1 and V4 intrinsic connectivity (alone or together with extrinsic) than in extrinsic connectivity alone, and (2) repetition effects on V1 and V4 population input rather than output gain. The best input gain model included effects in V1 granular and superficial excitatory populations and in V4 granular and deep excitatory populations. Our findings are consistent with gamma reflecting bottom-up signal precision, which increases with repetition and, therefore, with predictability and learning. Highlights We model learning effects in macaque visual cortex using Dynamic Causal Modeling. Microcircuit-level changes explain the repetition-induced gamma increases. The best models include changes 1) within V1 and V4 and 2) in neuronal input gain. Gamma may reflect bottom-up signal precision.
In this paper, we pursue recent observations that, through selective dendritic filtering, single neurons respond to specific sequences of presynaptic inputs. We try to provide a principled and mechanistic account of this selectivity by applying the free energy principle to a dendrite that is immersed in its neuropil or environment. We assume that neurons self-organize to minimise a free energy bound on the self-information or surprise of presynaptic inputs that are sampled. We model this as a selective pruning of dendritic spines that are expressed on a dendritic branch. This pruning occurs when the optimized postsynaptic gain falls below a threshold. Crucially, postsynaptic gain is itself optimized with respect to free energy. Pruning suppresses free energy as the dendrite selects presynaptic signals that conform to its expectations, specified by a generative model implicit in its intracellular kinetics. Not only does this provide a principled account of how neurons organize and selectively sample the myriad of potential presynaptic inputs they are exposed to, but it also connects the optimization of elemental neuronal (dendritic) processing to generic (surprise or evidence-based) schemes in statistics and machine learning, such as Bayesian model selection and automatic relevance determination.
Methods used in artificial intelligence (AI) overlap with methods used in computational psychiatry (CP). Hence, considerations from AI ethics are also relevant to ethical discussions of CP. Ethical issues include, among others, fairness and data ownership and protection. Apart from this, morally relevant issues also include potential transformative effects of applications of AI—for instance, with respect to how we conceive of autonomy and privacy. Similarly, successful applications of CP may have transformative effects on how we categorise and classify mental disorders and mental health. Since many mental disorders go along with disturbed conscious experiences, it is desirable that successful applications of CP improve our understanding of disorders involving disruptions in conscious experience. Here, we discuss prospects and pitfalls of transformative effects that CP may have on our understanding of mental disorders. In particular, we examine the concern that even successful applications of CP may fail to take all aspects of disordered conscious experiences into account.
We describe an implementation of the general linear model that facilitates the characterization of evoked hemodynamic responses to sensorimotor or cognitive processing, when the exact form of these responses is not known. The importance of this approach is that one can test for differential responses among tasks that may elude more conventional analyses. In particular, we suppose that an evoked response has early and late components and that a differential response may involve (i) both components to the same degree, as in a conventional "activation" or (ii) differential expression of the early and late components in two tasks, as might be seen in differential adaptation, or differences associated with the tasks (e.g., requiring and not requiring sustained attention). Using this approach we were able to demonstrate that the anterior cingulate differentiates, in terms of its response, between two motor tasks that did and did not require sustained attention. This differential response was observed even though there was no classical "activation" (i.e., there was no difference in the mean activity associated with the two conditions). It is suggested that these demonstration results point to the possibility of making greater use of the temporal resolution afforded by fast fMR1 techniques.
Over the past 20 years, neuroimaging has become a predominant technique in systems neuroscience. One might envisage that over the next 20 years the neuroimaging of distributed processing and connectivity will play a major role in disclosing the brain's functional architecture and operational principles. The inception of this journal has been foreshadowed by an ever-increasing number of publications on functional connectivity, causal modeling, connectomics, and multivariate analyses of distributed patterns of brain responses. I accepted the invitation to write this review with great pleasure and hope to celebrate and critique the achievements to date, while addressing the challenges ahead.
A scientific paradigmatic account suffices to interpret behavioral evolution in early Homo. Cognitive surprises, favoring anomalous behavioral propensities to sporadic expression, can explain “snakes-and-ladders” appearances and disappearances of Paleolithic skills in the Early and Middle Pleistocene record. The account applies the principle of stationary action, which underpins the free energy principle, to self-organizing systems at an evolutionary timescale. Unusual personal attainments, often explained by invoking progressive ascent of evolutionary phylogenetic “ladders” of cognitive and technical abilities, could be disregarded in a hominin community that failed to imagine or articulate possible advantages for its survivability. Such failure, as well as diverse fortuitous demographic accidents, could erase from collective memory the recollection of exceptional individual conduct which disappeared down a “snake”, so to speak, of the human evolutionary “puzzle”. The puzzle discomforts paleoanthropologists. Some explain it away with the self-justifying assertion that separate paleospecies of Homo differentially possessed cognitive abilities that allegedly underlay the differential presence or absence in the Pleistocene archeological record of traces of particular behavioral outcomes or skills. An alternative methodological perspective, grounded in the fundamental relationships between organisms and their environments, affords a parsimonious, prosaic, deflationary account for appearances and disappearances of behavioral outcomes and skills.
In this paper we present an approach to making inferences about generic activations in groups of subjects using fMRI. In particular we suggest that activations common to all subjects reflect aspects of functional anatomy that may be “typical” of the population from which that group was sampled. These commonalities can be identified by a conjunction analysis of the activation effects in which the contrasts, testing for an activation, are specified separately for each subject. A conjunction is the joint refutation of multiple null hypotheses, in this instance, of no activation in any subject. The motivation behind this use of conjunctions is that fixed-effect analyses are generally more “sensitive” than equivalent random-effect analyses. This is because fixed-effect analyses can harness the large degrees of freedom and small scan-to-scan variability (relative to the variability in responses from subject to subject) when assessing the significance of an estimated response. The price one pays for the apparent sensitivity of fixed-effect analyses is that the ensuing inferences pertain to, and only to, the subjects studied. However, a conjunction analysis, using a fixed-effect model, allows one to infer: (i) that every subject studied activated and (ii) that at least a certain proportion of the population would have shown this effect. The second inference depends upon a meta-analytic formulation in terms of a confidence region for this proportion. This approach retains the sensitivity of fixed-effect analyses when the inference that only a substantial proportion of the population activates is sufficient.
In the past decade functional neuroimaging has been extremely successful in establishing functional segregation as a principle of organisation in the human brain. The nature of the functional specialisation is attributed to the sensori-motor or cognitive process that has been manipulated experimentally. Newer approaches have introduced a number of concepts into neuroimaging. An experiment that studies the effect of attention on visual pathways is described. The analysis reveals a change in the connectivity of two cortical areas involved in visual motion analysis. A nonlinear modulation of this connectivity by the prefrontal regions is shown.
