The creative re-use of existing cognitive capacities may have played a significant role in the evolutionary development of the brain. There are obvious evolutionary advantages to such redeployment, and the data presented here confirm three important empirical predictions of this account of the development of cognition: (1) a typical brain area will be utilized by many cognitive functions in diverse task categories, (2) evolutionarily older brain areas will be deployed in more cognitive functions and (3) more recent cognitive functions will utilize more, and more widely scattered brain areas. These findings have implications not just for our understanding of the evolutionary origins of cognitive function, but also for the practice of both clinical and experimental neuroscience. 1 Evolution and redeployment Part of understanding the functional organization of the brain is understanding how it evolved. The current study suggests that while the brain may have originally emerged as an organ with functionally dedicated regions, the creative re-use of these regions has played a significant role in its evolutionary development. This would parallel the evolution of other capabilities wherein existing structures, evolved for other purposes, are re-used and built upon in the course of continuing evolutionary development

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.

It has previously been shown that generic cortical microcircuit models can perform complex real-time computations on continuous input streams, provided that these computations can be carried out with a rapidly fading memory. We investigate the computational capability of such circuits in the more realistic case where not only readout neurons, but in addition a few neurons within the circuit, have been trained for specific tasks. This is essentially equivalent to the case where the output of trained readout neurons is fed back into the circuit. We show that this new model overcomes the limitation of a rapidly fading memory. In fact, we prove that in the idealized case without noise it can carry out any conceivable digital or analog computation on time-varying inputs. But even with noise, the resulting computational model can perform a large class of biologically relevant real-time computations that require a nonfading memory. We demonstrate these computational implications of feedback both theoretically, and through computer simulations of detailed cortical microcircuit models that are subject to noise and have complex inherent dynamics. We show that the application of simple learning procedures (such as linear regression or perceptron learning) to a few neurons enables such circuits to represent time over behaviorally relevant long time spans, to integrate evidence from incoming spike trains over longer periods of time, and to process new information contained in such spike trains in diverse ways according to the current internal state of the circuit. In particular we show that such generic cortical microcircuits with feedback provide a new model for working memory that is consistent with a large set of biological constraints.

This paper introduces a very simple analytic method for mining large numbers of brain imaging experiments to discover functional cooperation between regions. We then report some preliminary results of its application, illustrate some of the many future projects in which we expect the technique will be of considerable use (including a way to relate fMRI to EEG), and describe a research resource for investigating functional cooperation in the cortex that will be made publicly available through the lab website. One significant finding is that differences between cognitive domains appear to be attributable more to differences in patterns of cooperation between brain regions, rather than to differences in which brain regions are used in each domain. This is not a result that is predicted by prevailing localization‐based and modular accounts of the organization of the cortex. Introduction and Background Hardly an issue of Science or Nature goes by without creating a stir over the discovery of “the” gene for some disease, trait, or predisposition, or “the ” brain area responsible for some behavior or cognitive capacity. Of course, we know better; the isolable parts of complex systems like the

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Rhythmic voltage oscillations resulting from the summed activity of neuronal populations occur in many nervous systems. Contemporary observations suggest that coexistent oscillations interact and, in time, may switch in dominance. We recently reported an example of these interactions recorded from in vitro preparations of rat somatosensory cortex. We found that following an initial interval of coexistent gamma (,25 ms period) and beta2 (,40 ms period) rhythms in the superficial and deep cortical layers, respectively, a transition to a synchronous beta1 (,65 ms period) rhythm in all cortical layers occurred. We proposed that the switch to beta1 activity resulted from the novel mechanism of period concatenation of the faster rhythms: gamma period (25 ms)+beta2 period (40 ms) = beta1 period (65 ms). In this article, we investigate in greater detail the fundamental mechanisms of the beta1 rhythm. To do so we describe additional in vitro experiments that constrain a biologically realistic, yet simplified, computational model of the activity. We use the model to suggest that the dynamic building blocks (or motifs) of the gamma and beta2 rhythms combine to produce a beta1 oscillation that exhibits crossfrequency interactions. Through the combined approach of in vitro experiments and mathematical modeling we isolate the specific components that promote or destroy each rhythm. We propose that mechanisms vital to establishing the beta1 oscillation include strengthened connections between a population of deep layer intrinsically bursting cells and a transition from antidromic to orthodromic spike generation in these cells. We conclude that neural activity in the superficial and deep

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Abstract. The topic of evolutionary trends in complexity has drawn much controversy in the artificial life community. Rather than investigate the evolution of overall complexity, here we investigate the evolution of topology of networks in the Polyworld artificial life system. Our investigation encompasses both the actual structure of neural networks of agents in this system, and logical or functional networks inferred from statistical dependencies between nodes in the networks. We find interesting trends across several topological measures, which together imply a trend of more integrated activity across the networks (with the networks taking on a more “small-world ” character) with evolutionary time. 1

Complex networks from domains like Biology or Sociology are present in many e-Science data sets. Dealing with networks can often form a workflow bottleneck as several related algorithms are computationally hard. One example is detecting characteristic patterns or “network motifs” – a problem involving subgraph mining and graph isomorphism. This paper provides a review and runtime comparison of current motif detection algorithms in the field. We present the strategies and the corresponding algorithms in pseudo-code yielding a framework for comparison. We categorize the algorithms outlining the main differences and advantages of each strategy. We finally implement all strategies in a common platform to allow a fair and objective efficiency comparison using a set of benchmark networks. We hope to inform the choice of strategy and critically discuss future improvements in motif detection.

Long-standing debates about the role of natural selection in the growth of biological complexity over geological time scales are difficult to resolve from the paleobiological record. Using an evolutionary model–a computational ecosystem subjected to natural selection–we investigate evolutionary trends in an information-theoretic measure of the complexity of the neural dynamics of artificial agents inhabiting the model. Our results suggest that evolution always guides complexity change, just not in a single direction. We also demonstrate that neural complexity correlates well with behavioral adaptation, but only when complexity increases are achieved through natural selection (as opposed to increases generated randomly or optimized via a genetic algorithm). We conclude with a suggested research direction that might be able to use the artificial neural data generated in these experiments to determine which aspects of network structure give rise to evolutionarily meaningful neural complexity. I.