Even in the absence of sensory inputs, cortical and thalamic neurons can show structured patterns of ongoing spontaneous activity, whose origins and functional significance are not well understood. We use computer simulations to explore the conditions under which spontaneous activity emerges from a simplified model of multiple interconnected thalamocortical columns linked by long-range, top-down excitatory axons, and to examine its interactions with stimulus-induced activation. Simulations help characterize two main states of activity. First, spontaneous gamma-band oscillations emerge at a precise threshold controlled by ascending neuromodulator systems. Second, within a spontaneously active network, we observe the sudden ‘‘ignition’ ’ of one out of many possible coherent states of high-level activity amidst cortical neurons with long-distance projections. During such an ignited state, spontaneous activity can block external sensory processing. We relate those properties to experimental observations on the neural bases of endogenous states of consciousness, and particularly the blocking of access to consciousness that occurs in the psychophysical phenomenon of ‘‘inattentional blindness,’ ’ in which normal subjects intensely engaged in mental activity fail to notice salient but irrelevant sensory stimuli. Although highly simplified, the generic properties of a minimal network may help clarify some of the basic cerebral phenomena underlying the autonomy of consciousness. Citation: Dehaene S, Changeux JP (2005) Ongoing spontaneous activity controls access to consciousness: A neuronal model for inattentional blindness. PLoS Biol 3(5): e141.

Dehaene (this volume) articulates a naturalistic approach to the cognitive foundations of mathematics. Further, he argues that the `number line' (analog magnitude) system of representation is the evolutionary and ontogenetic foundation of numerical concepts. Here I endorse Dehaene's naturalistic stance and also his characterization of analog magnitude number representations. Although analog magnitude representations are part of the evolutionary foundations of numerical concepts, I argue that they are unlikely to be part of the ontogenetic foundations of the capacity to represent natural number. Rather, the developmental source of explicit integer list representations of number are more likely to be systems such as the object--file representations that articulate mid--level object based attention, systems that build parallel representations of small sets of individuals.

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.

A simple model of self-organised learning with no classical (Hebbian) reinforcement is presented. Synaptic connections involved in mistakes are depressed. The model operates at a highly adaptive, probably critical, state reached by extremal dynamics similar to that of recent evolution models. Thus, one might think of the mechanism as synaptic Darwinism. It is widely believed that learning in the brain resides in alterations of synaptic efficacy. Without exception, contemporary formulations of such learning follows Hebb’s ideas [1] of reinforcement: synaptic connections among neurons excited during a a given firing pattern are strengthened by a process of long term potentiation (LTP). However, long term synaptic depression (LTD) in the mammalian brain is almost as prevalent as potentiation, but there appears to be little or no understanding of its functional role. Working hypotheses covers a wide range, where depression is given always an auxiliary function to potentiation [2]. A recent review [3], reflecting the current variety of ideas regarding the functional role of LTD, speculates: “Although it is conceivable that LTP is

Introduction: the challenge of a science of consciousness Understanding consciousness has become the ultimate intellectual challenge of this new millennium. Even if philosophers now accept the notion that it is a "real , natural, biological phenomenon literally located in the brain" (Revonsuo, 2001), a view in harmony with the neuroscientist conception that "consciousness is entirely caused by neurobiological processes and realized in brain structures" (Changeux, 1983; Crick, 1994; Edelman, 1989), the real issue becomes: how to elaborate a science of consciousness? This challenging problem raises two questions. A first one is how to empirically define experimental paradigms in order to delineate a relevant and ultimately causal relationship between subjective phenomena and objective measurements of neural activity. Cognitive psychologists have now defined a variety of minimal experimental protocols which allow a fair comparison between conscious and non-conscious processing of informa

Recent progress in the molecular biology of synaptic transmission, in particular of neurotransmitter receptors, offers novel information relevant to `realistic' modeling of neural processes at the single cell and network level. Sophisticated computer analyses of two-dimensional crystals by high resolution electron microscopy yield images of single neurotransmitter receptor molecules with tentative identifications of ligand binding sites and of conformational transitions. The dynamics of conformational changes can be accounted for by a `multistate allosteric network' model. Allosteric receptors also possess the structural and functional properties required to serve as coincidence detectors between pre- and post-synaptic signals and, therefore, can be used as building blocks for a chemical Hebb synapse. These properties were introduced into networks of formal neurons capable of producing and detecting temporal sequences. In more elaborate models of pre-frontal cortex functions, allosteric receptors control the selection of transient `pre-representations' and their stabilization by external or internal reward signals. We apply this scheme to Shallice's Tower of London test, and we show how a hierarchical neuronal architecture can implement Z. executive or planning functions associated with frontal areas. Academie des sciences#Elsevier, Paris. # 2000 Elsevier Science B.V. All rights reserved.

This paper series focusses on the intersection of neural networks and evolutionary computation. It is addressed to researchers from artificial intelligence as well as the neurosciences.

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