Computational models of association cortex

The impact of romantic relationships on deception detection: Exploring the gender differences and the mediating role of mentalizing

In evolution, romantic relationships serve as the foundation for breeding and producing offspring. The ability to detect deception in these relationships can safeguard the investment and cultivation of descendants, leading to greater chances of survival and reproduction. However, barely any research has been carried out within this domain. The current study investigated the preliminary relationship between romantic relationships, mentalizing ability, and deception detection ability through an empirical experiment. Participants were primed by their romantic experiences and neutral experiences, and then went through a Reading the Mind in the Eyes (RTM) task and the deception detecting task for real person crime-type videos. Results showed that romantic relationships can improve participants' emotion recognition ability toward negative emotions, and females performed better in the deception detection task than males did. Most importantly, romantic relationships can improve participants' deception detection ability through the mediator of mentalizing ability. Though gender difference was not statistically significant in the RTM task, the results lay a solid foundation for further investigation into females' mentalizing ability and disclose the evolutionary meaning of romantic relationships.

The biological Maxwell's demons: exploring ideas about the information processing in biological systems

This work is based on ideas supported by some of the biologists who discovered foundational facts of twentieth-century biology and who argued that Maxwell's demons are physically implemented by biological devices. In particular, JBS Haldane first, and later J. Monod, A, Lwoff and F. Jacob argued that enzymes and molecular receptors implemented Maxwell's demons that operate in systems far removed from thermodynamic equilibrium and that were responsible for creating the biological order. Later, these ideas were extended to other biological processes. In this article, we argue that these biological Maxwell's demons (BMD) are systems that have information processing capabilities that allow them to select their inputs and direct their outputs toward targets. In this context, we propose the idea that these BMD are information catalysts in which the processed information has broad thermodynamic consequences.

What makes the prefrontal cortex so appealing in the era of brain imaging? a network analytical perspective

It is thought that the prefrontal cortex (PFC) subserves cognitive control processes by coordinating the flow of information in the cerebral cortex. In the network of cortical areas the central position of the PFC makes difficult to dissociate processing and the cognitive function mapped to this region, especially when using whole brain imaging techniques, which can detect frequently activated regions. Accordingly, the present study showed particularly high rate of increase of published studies citing the PFC and imaging as compared to other fields of the neurosciences on the PubMed. Network measures used to characterize the role of the areas in signal flow indicated specialization of the different regions of the PFC in cortical processing. Notably, areas of the dorsolateral PFC and the anterior cingulate cortex, which received the highest number of citations, were identified as global convergence points in the network. These prefrontal regions also had central position in the dominant cluster consisted exclusively by the associational areas of the cortex. We also present findings relevant to models suggesting that control processes of the PFC are depended on serial processing, which results in bottleneck effects. The findings suggest that PFC is best understood via its role in cortical information processing.

Deciding between conflicting motivations: what mice make of their prefrontal cortex

We investigated the contribution of the mouse prefrontal cortex and, more specifically, the prelimbic area, to a learning task that highlights a choice between two conflicting motivations, the one for food seeking and the one for novelty exploration. We used a learning paradigm in a cross maze task that targets first motivation conflict and second flexible rule adaptation following environmental changes. We show that mice with prelimbic lesions, which showed normal spatial learning, exhibit impairment in switching from one type of reward - food retrieval - to another type - novelty exploration - and sustained difficulties in adapting their behaviour when the rule is changing repeatedly. Mice, like other mammals, possess a prefrontal cortex that participates in the control of the flexible switch between concurrent natural motivations and in the rapid and flexible adaptation to external changes. These results open a way to study in mice models motivation conflict and cognitive adaptation, brain functions known to be compromised in several psychiatric conditions in humans in which the prefrontal cortex functioning is altered.

Copying and evolution of neuronal topology

We propose a mechanism for copying of neuronal networks that is of considerable interest for neuroscience for it suggests a neuronal basis for causal inference, function copying, and natural selection within the human brain. To date, no model of neuronal topology copying exists. We present three increasingly sophisticated mechanisms to demonstrate how topographic map formation coupled with Spike-Time Dependent Plasticity (STDP) can copy neuronal topology motifs. Fidelity is improved by error correction and activity-reverberation limitation. The high-fidelity topology-copying operator is used to evolve neuronal topologies. Possible roles for neuronal natural selection are discussed.

How do high-level specifications of the brain relate to variational approaches?

High-level specification of how the brain represents and categorizes the causes of its sensory input allows to link "what is to be done" (perceptual task) with "how to do it" (neural network calculation). In this article, we describe how the variational framework, which encountered a large success in modeling computer vision tasks, has some interesting relationships, at a mesoscopic scale, with computational neuroscience. We focus on cortical map computations such that "what is to be done" can be represented as a variational approach, i.e., an optimization problem defined over a continuous functional space. In particular, generalizing some existing results, we show how a general variational approach can be solved by an analog neural network with a given architecture and conversely. Numerical experiments are provided as an illustration of this general framework, which is a promising framework for modeling macro-behaviors in computational neuroscience.

The neural basis of love as a subliminal prime: an event-related functional magnetic resonance imaging study

Throughout the ages, love has been defined as a motivated and goal-directed mechanism with explicit and implicit mechanisms. Recent evidence demonstrated that the explicit representation of love recruits subcorticocortical pathways mediating reward, emotion, and motivation systems. However, the neural basis of the implicit (unconscious) representation of love remains unknown. To assess this question, we combined event-related functional magnetic resonance imaging (fMRI) with a behavioral subliminal priming paradigm embedded in a lexical decision task. In this task, the name of either a beloved partner, a neutral friend, or a passionate hobby was subliminally presented before a target stimulus (word, nonword, or blank), and participants were required to decide if the target was a word or not. Behavioral results showed that subliminal presentation of either a beloved's name (love prime) or a passion descriptor (passion prime) enhanced reaction times in a similar fashion. Subliminal presentation of a friend's name (friend prime) did not show any beneficial effects. Functional results showed that subliminal priming with a beloved's name (as opposed to either a friend's name or a passion descriptor) specifically recruited brain areas involved in abstract representations of others and the self, in addition to motivation circuits shared with other sources of passion. More precisely, love primes recruited the fusiform and angular gyri. Our findings suggest that love, as a subliminal prime, involves a specific neural network that surpasses a dopaminergic-motivation system.

Non-sensory cortical and subcortical connections of the primary auditory cortex in Mongolian gerbils: bottom-up and top-down processing of neuronal information via field AI

In the present study, we will provide further anatomical evidence that the primary auditory cortex (field AI) is not only involved in sensory processing of its own modality, but also in complex bottom-up and top-down processing of multimodal information. We have recently shown that AI in the Mongolian gerbil (Meriones unguiculatus) has substantial connections with non-auditory sensory and multisensory brain structures [Budinger, E., Heil, P., Hess, A., Scheich, H., 2006. Multisensory processing via early cortical stages: Connections of the primary auditory cortical field with other sensory systems. Neuroscience 143, 1065-1083]. Here we will report about the direct connections of AI with non-sensory cortical areas and subcortical structures. We approached this issue by means of the axonal transport of the sensitive bidirectional neuronal tracers fluorescein-labelled (FD) and tetramethylrhodamine-labelled dextran (TMRD), which were simultaneously injected into different frequency regions of the gerbil's AI. Of the total number of retrogradely labelled cell bodies found in non-sensory brain areas, which identify cells of origin of direct projections to AI, approximately 24% were in cortical areas and 76% in subcortical structures. Of the cell bodies in the cortical areas, about 4.4% were located in the orbital, 11.1% in the infralimbic medial prefrontal (areas DPC, IL), 18.2% in the cingulate (3.2% in CG1, 2.9% in CG2, 12.1% in CG3), 9.5% in the frontal association (area Fr2), 12.0% in the insular (areas AI, DI), 10.8% in the retrosplenial, and 34.0% in the perirhinal cortex. The cortical regions with retrogradely labelled cells, as well as the entorhinal cortex, also contained anterogradely labelled axons and their terminations, which means that they are also target areas of direct projections from AI. The laminar pattern of corticocortical connections indicates that AI receives primarily cortical feedback-type inputs and projects in a feedforward manner to its target areas. The high number of double-labelled somata, the non-topographic distribution of single FD- and TMRD-labelled somata, and the overlapping spatial distribution of FD- and TMRD-labelled axonal elements suggest rather non-tonotopic connections between AI and the multimodal cortices. Of the labelled cell bodies in the subcortical structures, about 38.8% were located in the ipsilateral basal forebrain (10.6% in the lateral amygdala LA, 11.5% in the globus pallidus GP, 3.7% in the ventral pallidum VPa, 13.0% in the nucleus basalis NB), 13.1% in the ipsi- and contralateral diencephalon (6.4% in the posterior paraventricular thalamic nuclei, 6.7% in the hypothalamic area), and 48.1% in the midbrain (20.0% in the ipsilateral substantia nigra, 9.8% in the ipsi- and contralateral ventral tegmental area, 5.0% in the ipsi- and contralateral locus coeruleus, 13.3% the ipsi- and contralateral dorsal raphe nuclei). Thus, the majority of subcortical inputs to AI was related to different neurotransmitter systems. Anterograde labelling was only found in some ipsilateral basal forebrain structures, namely, the LA, basolateral amygdala, GP, VPa, and NB. As for the cortex, the proportion and spatial distribution of single FD-, TMRD-, and double-labelled neuronal elements suggests rather non-tonotopic connections between AI and the neuromodulatory subcortical structures.

Stem cell growth becomes predominant while neural plate progenitor pool decreases during spinal cord elongation

The antero-posterior dispersion of clonally related cells is a prominent feature of axis elongation in vertebrate embryos. Two major models have been proposed: (i) the intercalation of cells by convergent-extension and (ii) the sequential production of the forming axis by stem cells. The relative importance of both of these cell behaviors during the long period of elongation is poorly understood. Here, we use a combination of single cell lineage tracing in the mouse embryo, computer modeling and confocal video-microscopy of GFP labeled cells in the chick embryo to address the mechanisms involved in the antero-posterior dispersion of clones. In the mouse embryo, clones appear as clusters of labeled cells separated by intervals of non-labeled cells. The distribution of intervals between clonally related clusters correlates with a statistical model of a stem cell mode of growth only in the posterior spinal cord. A direct comparison with published data in zebrafish suggests that elongation of the anterior spinal cord involves similar intercalation processes in different vertebrate species. Time-lapse analyses of GFP labeled cells in cultured chick embryos suggest a decrease in the size of the neural progenitor pool and indicate that the dispersion of clones involves ordered changes of neighborhood relationships. We propose that a pre-existing stem zone of growth becomes predominant to form the posterior half of the axis. This temporal change in tissue-level motion is discussed in terms of the clonal and genetic continuities during axis elongation.

Learning in ex-vivo developing networks of cortical neurons

This contribution describes the use of multi-site interaction with large cortical networks in the study of learning. The general physiological properties of the network are described, and the concept of learning is mapped to the experimental network preparation. Learning is then analyzed in terms of exploration (defined as changes in the configuration of associations within the biological network) and recognition (the stabilization of "worthy" associations).

Dopamine-Induced Dispersion of Correlations Between Action Potentials in Networks of Cortical Neurons

The involvement of dopamine in the process of learning, at the cellular and behavioral levels, has been studied extensively. Evidently, dopamine is released from midbrain nuclei neurons on exposure to salient unpredicted stimuli and binds to neurons of cortical and subcortical structures, where its neuromodulatory effects are exerted. The neuromodulatory effects of dopamine at the synaptic and cellular levels are very rich, but it is difficult to extrapolate from these elementary levels what their effect might be at the behaviorally relevant level of neuronal ensembles. Using multi-site recordings from networks of cortical neurons developing ex vivo, we studied the effects of dopamine on connectivity within neuronal ensembles. We found that dopamine disperses correlations between individual neuronal activities while preserving the global distribution of correlations at the network level. Using selective D1 and D2 modulators, we show that both receptor types are contributing to dopamine-induced dispersion. Our results indicate that, at the neuronal ensemble level, dopamine acts to enhance changes in network connectivity rather than stabilize such connections.

Biophysical vision model and learning paradigms about vision: review

A learning paradigm of a new biophysical vision model (BVM) is presented. It incorporates anatomical and physiological evidence from micro- and macroscopic research on vision as reported in the literature during the past five years. Anatomical and physiological vision research tends to drift away from the technological foundations of encoding and reproducing size-defined images of real ongoing life scenarios. White and color light waves reflecting life scenarios are converted by the retina to encoded electrical train pulses with attached real information to be decoded by cortical vision neurons. The BVM paradigm is based on the ideas that: (1) cinema technology reproduces real-life scenes just as the human eye sees them; (2) virtual reality and robotics are computerized replications of categorized human vision faculties in operation. We believe that vision-related technology may extend our knowledge about vision and direct vision research into new horizons. The biophysical vision model has three prerequisites: (1) The faculties of human vision must be categorized. (2) Logic circuits of the 'hardware' of neuronal vision must be present. (3) Vision faculties are operated by self-induced 'software'. Vision research may be enhanced with devices constructed according to BVM that would enable biophysical vision experiments in both humans and animals.