This article is about how the brain data mines its sensory inputs. There are several architectural principles of functional brain anatomy that have emerged from careful anatomic and physiologic studies over the past century. These principles are considered in the light of representational learning to see if they could have been predicted a priori on the basis of purely theoretical considerations. We first review the organisation of hierarchical sensory cortices, paying special attention to the distinction between forward and backward connections. We then review various approaches to representational learning as special cases of generative models, starting with supervised learning and ending with learning based upon empirical Bayes. The latter predicts many features, such as a hierarchical cortical system, prevalent top-down backward influences and functional asymmetries between forward and backward connections that are seen in the real brain. The key points made in this article are: (i) hierarchical generative models enable the learning of empirical priors and eschew prior assumptions about the causes of sensory input that are inherent in non-hierarchical models. These assumptions are necessary for learning schemes based on information theory and efficient or sparse coding, but are not necessary in a hierarchical context. Critically, the anatomical infrastructure that may implement generative models in the brain is hierarchical. Furthermore, learning based on empirical Bayes can proceed in a biologically plausible way. (ii) The second point is that backward connections are essential if the processes generating inputs cannot be inverted, or the inversion cannot be parameterised. Because these processes involve many-to-one mappings, are non-linear and dynamic in nature, they are generally non-invertible. This enforces an explicit parameterisation of generative models (i.e. backward

Large-scale neural networks are thought to be an essential substrate for the implementation of cognitive function by the brain. If so, then a thorough understanding of cognition is not possible without knowledge of how the large-scale neural networks of cognition (neurocognitive networks) operate. Of necessity, such understanding requires insight into structural, functional, and dynamical aspects of network operation, the intimate interweaving of which may be responsible for the intricacies of cognition. Knowledge of anatomical structure is basic to understanding how neurocognitive networks operate. Phylogenetically and ontogenetically determined patterns of synaptic connectivity form a structural network of brain areas, allowing communication between widely distributed collections of areas. The function of neurocognitive networks depends on selective activation of anatomically linked cortical and subcortical areas in a wide variety of configurations. Large-scale functional networks provide the cooperative processing which gives expression to cognitive function. The dynamics of neurocognitive network function relates to the evolving patterns of interacting brain areas that express cognitive function in real time. This article considers the proposition that a basic similarity of the structural, functional, and dynamical features of all neurocognitive networks in the brain causes them to function according to common operational principles. The formation of neural context through the coordinated mutual constraint of multiple interacting cortical areas, is considered as a guiding principle underlying all cognitive functions. Increasing knowledge of the operational principles of neurocognitive networks is likely to promote the advancement of cognitive theories, and to seed strategies for the enhancement of cognitive abilities.

This article is about intersubject variability in the functional integration of activity in different brain regions. Previous studies of functional and effective connectivity have dealt with intersubject variability by analyzing data from different subjects separately or pretending the data came from the same subject. These approaches do not allow one to test for differences among subjects. The aim of this work was to illustrate how differences in connectivity among subjects can be addressed explicitly using structural equation modeling. This is enabled by constructing a multisubject network that comprises m regions of interest for each of the n subjects studied, resulting in a total of m � n nodes. Constructing a network of regions from different subjects may seem counterintuitive

Effective instructional methods are now an important national issue. We reviewed the current research techniques used in cognitive neuroscience and what is currently known about the neurocognition of reading and mathematics. We found that while the neurocognition aspects of reading and mathematics share common processes associated with language, certain aspects of semantics and comprehension are unique to reading and certain aspects of mathematics entail visual-spatial processing not observed during reading. We conclude that although significant advances have been made in the understanding of the underlying neurocognitive process in the last decade more research is needed before the neurosciences can make a direct contribution to instructional practice.

10 related responses in the ventral temporal (VT) lobe during visual object 11 identification were overlapping and distributed in topography. This 12 observation contrasts with prevailing views that object codes are focal 13 and localized to specific areas such as the fusiform and para-14 hippocampal gyri. We provide a critical test of Haxby’s hypothesis 15 using a neural network (NN) classifier that can detect more general 16 topographic representations and achieves 83 % correct generalization 17 performance on patterns of voxel responses in out-of-sample tests. 18 Using voxel-wise sensitivity analysis we show that substantially the 19 same VT lobe voxels contribute to the classification of all object 20 categories, suggesting the code is combinatorial. Moreover, we found 21 no evidence for local single category representations. The neural 22 network representations of the voxel codes were sensitive to both 23 category and superordinate level features that were only available 24 implicitly in the object categories.

Abstract: The convergence between telecommunication, virtual reality and artificial intelligence technologies resulted in a dramatical increase and modification of the opportunities to experience the physical and social world. Their diffusion and integration into multi-user and multi-agent virtual worlds highlighted the relevance of addressing from a common psychological perspective the domain of communication and the domain of presence. New theoretical and practical questions are emerging, in the double intent to explain phenomena at the interplay between mind and technology and to design effective technological applications. This chapter has the goal to start an exploration of the links and reciprocal contributions between communication and presence, analyzed at

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