Behaviourally meaningful representations from normalisation and context-guided denoising

Many existing independent component analysis algorithms include a preprocessing stage where the inputs are sphered. This amounts to normalising the data such that all correlations between the variables are removed. In this work, I show that sphering allows very weak contextual modulation to steer the development of meaningful features. Context-biased competition has been proposed as a model of covert attention and I propose that sphering-like normalisation also allows weaker top-down bias to guide attention

Emotion based attentional priority for storage in visual short-term memory

A plethora of research demonstrates that the processing of emotional faces is prioritised over non-emotive stimuli when cognitive resources are limited (this is known as ‘emotional superiority’). However, there is debate as to whether competition for processing resources results in emotional superiority per se, or more specifically, threat superiority. Therefore, to investigate prioritisation of emotional stimuli for storage in visual short-term memory (VSTM), we devised an original VSTM report procedure using schematic (angry, happy, neutral) faces in which processing competition was manipulated. In Experiment 1, display exposure time was manipulated to create competition between stimuli. Participants (n = 20) had to recall a probed stimulus from a set size of four under high (150 ms array exposure duration) and low (400 ms array exposure duration) perceptual processing competition. For the high competition condition (i.e. 150 ms exposure), results revealed an emotional superiority effect per se. In Experiment 2 (n = 20), we increased competition by manipulating set size (three versus five stimuli), whilst maintaining a constrained array exposure duration of 150 ms. Here, for the five-stimulus set size (i.e. maximal competition) only threat superiority emerged. These findings demonstrate attentional prioritisation for storage in VSTM for emotional faces. We argue that task demands modulated the availability of processing resources and consequently the relative magnitude of the emotional/threat superiority effect, with only threatening stimuli prioritised for storage in VSTM under more demanding processing conditions. Our results are discussed in light of models and theories of visual selection, and not only combine the two strands of research (i.e. visual selection and emotion), but highlight a critical factor in the processing of emotional stimuli is availability of processing resources, which is further constrained by task demands

Review: Object vision in a structured world

In natural vision, objects appear at typical locations, both with respect to visual space (e.g., an airplane in the upper part of a scene) and other objects (e.g., a lamp above a table). Recent studies have shown that object vision is strongly adapted to such positional regularities. In this review we synthesize these developments, highlighting that adaptations to positional regularities facilitate object detection and recognition, and sharpen the representations of objects in visual cortex. These effects are pervasive across various types of high-level content. We posit that adaptations to real-world structure collectively support optimal usage of limited cortical processing resources. Taking positional regularities into account will thus be essential for understanding efficient object vision in the real world

A feedback model of visual attention

Feedback connections are a prominent feature of cortical anatomy and are likely to have significant functional role in neural information processing. We present a neural network model of cortical feedback that successfully simulates neurophysiological data associated with attention. In this domain our model can be considered a more detailed, and biologically plausible, implementation of the biased competition model of attention. However, our model is more general as it can also explain a variety of other top-down processes in vision, such as figure/ground segmentation and contextual cueing. This model thus suggests that a common mechanism, involving cortical feedback pathways, is responsible for a range of phenomena and provides a unified account of currently disparate areas of research

Distributed Hypothesis Testing, Attention Shifts and Transmitter Dynatmics During the Self-Organization of Brain Recognition Codes

BP (89-A-1204); Defense Advanced Research Projects Agency (90-0083); National Science Foundation (IRI-90-00530); Air Force Office of Scientific Research (90-0175, 90-0128); Army Research Office (DAAL-03-88-K0088

How gut sampling and microbial invasiveness/noninvasiveness provides mucosal immunity with a nonmolecular pattern means to distinguish commensals from pathogens: A review

Mucosal immunity distinguishes not only different microbial antigens but also separates those of pathogens from those of commensals. How this is done is unknown. The present view is that the pathogen/commensal determination of antigens depends upon as yet to be discovered molecular patterns. Here I review the biological feasibility that it also involves the detection of the invasive differences in their motility towards the gut wall when they are sampled by differently biased methods. 

By their nature, pathogens and commensals have different motility – invasive and noninvasive – in regard to the epithelium. The immune system is in a position to detect such motility differences. This biological opportunity arises since different microbe sampling methods can “catch” different groups of microbes depending upon how their motility interacts with the epithelium. A biological method biased to sample those with invasive motility—pathogens—could be achieved by ‘honey pot traps’ that preferentially (but not exclusively) sample microbes that have a taxis to breaches in the epithelium. A biological method biased to sample those that are noninvasive—commensals—could be done by capturing microbes that are passively and stably residing in the biofilm “offshore” of the epithelium. Such differential sampling strategies would seem to relate to those carried out respectively by (i) M-cells (working with subepithelial dome dendritic cells), and (ii) sub- and intraepithelial dendritic cells.

The interactions of antigen presentation can be arranged so that the immune system links antigens from biased microbial sampling with pathogenic or commensal appropriate immune responses. Such immune classification could feasibly occur biologically through a winner-take-all competition between inhibiting and activating antigen presentation. Winner-takes-all types of processing classification are already known to underlie the biologically interactions between neurons that classify sensory inputs making it also plausible that they are exploited by the immune system. In pathogen identification, M-cell antigens would be activating and biofilm antigens inhibitory, and vise versa for commensal identification. This winner-take-all competition between antigen presentation would act to amplify small statistical biases in the two samples linked to invasiveness/noninvasiveness into a reliable pathogen/commensal distinction. This process would both complement, and acts as independent guarantor, upon the alternative pathogenicity/commensality recognition provided by molecular pattern recognition. 

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A neural network model of adaptively timed reinforcement learning and hippocampal dynamics

A neural model is described of how adaptively timed reinforcement learning occurs. The adaptive timing circuit is suggested to exist in the hippocampus, and to involve convergence of dentate granule cells on CA3 pyramidal cells, and NMDA receptors. This circuit forms part of a model neural system for the coordinated control of recognition learning, reinforcement learning, and motor learning, whose properties clarify how an animal can learn to acquire a delayed reward. Behavioral and neural data are summarized in support of each processing stage of the system. The relevant anatomical sites are in thalamus, neocortex, hippocampus, hypothalamus, amygdala, and cerebellum. Cerebellar influences on motor learning are distinguished from hippocampal influences on adaptive timing of reinforcement learning. The model simulates how damage to the hippocampal formation disrupts adaptive timing, eliminates attentional blocking, and causes symptoms of medial temporal amnesia. It suggests how normal acquisition of subcortical emotional conditioning can occur after cortical ablation, even though extinction of emotional conditioning is retarded by cortical ablation. The model simulates how increasing the duration of an unconditioned stimulus increases the amplitude of emotional conditioning, but does not change adaptive timing; and how an increase in the intensity of a conditioned stimulus "speeds up the clock", but an increase in the intensity of an unconditioned stimulus does not. Computer simulations of the model fit parametric conditioning data, including a Weber law property and an inverted U property. Both primary and secondary adaptively timed conditioning are simulated, as are data concerning conditioning using multiple interstimulus intervals (ISIs), gradually or abruptly changing ISis, partial reinforcement, and multiple stimuli that lead to time-averaging of responses. Neurobiologically testable predictions are made to facilitate further tests of the model.Air Force Office of Scientific Research (90-0175, 90-0128); Defense Advanced Research Projects Agency (90-0083); National Science Foundation (IRI-87-16960); Office of Naval Research (N00014-91-J-4100

Memory and information processing in neuromorphic systems

A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multi-neuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed neuromorphic computing platforms and system

A self-adjusted Monte Carlo simulation as model of financial markets with central regulation

Properties of the self-adjusted Monte Carlo algorithm applied to 2d Ising ferromagnet are studied numerically. The endogenous feedback form expressed in terms of the instant running averages is suggested in order to generate a biased random walk of the temperature that converges to criticality without an external tuning. The robustness of a stationary regime with respect to partial accessibility of the information is demonstrated. Several statistical and scaling aspects have been identified which allow to establish an alternative spin lattice model of the financial market. It turns out that our model alike model suggested by S. Bornholdt, Int. J. Mod. Phys. C {\bf 12} (2001) 667, may be described by L\'evy-type stationary distribution of feedback variations with unique exponent $\alpha_1 \sim 3.3$. However, the differences reflected by Hurst exponents suggest that resemblances between the studied models seem to be nontrivial.Comment: 19 pages, 9 figures, 30 reference