Luminance contrast has little influence on the spread of object-based attention

AbstractWe direct our attention to those visual stimuli that are relevant to our behavioral goals. Some of the visual stimuli that surround us are represented more strongly, because they have a higher luminance contrast. However, selective attention also boosts the representation of visual stimuli. It is not yet well understood how attention and contrast interact. Some previous theories proposed that attentional effects are strongest at low contrast, others that they are strongest at high contrast and yet others that the effects of selective attention are largely independent of contrast. In the present study, we investigated the interaction between selective attention and luminance contrast with a contour-grouping task that provides a sensitive measure of the spread of object-based attention, with delays of several hundreds of milliseconds. We find that the spread of object-based attention is largely independent of contrast, and that subjects experience little difficulty in grouping low-contrast contour elements in the presence of other contour elements with a much higher contrast. The results imply that object-based attention and contrast have largely independent effects on visual processing

A neuronal device for the control of multi-step computations

We describe the operation of a neuronal device which embodies the computational principles of the `paper-and-pencil' machine envisioned by Alan Turing. The network is based on principles of cortical organization. We develop a plausible solution to implement pointers and investigate how neuronal circuits may instantiate the basic operations involved in assigning a value to a variable (i.e., x=5), in determining whether two variables have the same value and in retrieving the value of a given variable to be accessible to other nodes of the network. We exemplify the collective function of the network in simplified arithmetic and problem solving (blocks-world) tasks.Fil: Zylberberg, Ariel Dario. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación. Laboratorio de Inteligencia Artificial y Aplicaciones; ArgentinaFil: Paz, Luciano. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física. Laboratorio de Neurociencia Integrativa; ArgentinaFil: Roelfsema, Pieter R.. Commissariat A Energie Atomique; FranciaFil: Dehaene, Stanislas. Commissariat A Energie Atomique; FranciaFil: Sigman, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires; Argentin

Flexible Working Memory Through Selective Gating and Attentional Tagging

Working memory is essential: it serves to guide intelligent behavior of humans and nonhuman primates when task-relevant stimuli are no longer present to the senses. Moreover, complex tasks often require that multiple working memory representations can be flexibly and independently maintained, prioritized, and updated according to changing task demands. Thus far, neural network models of working memory have been unable to offer an integrative account of how such control mechanisms can be acquired in a biologically plausible manner. Here, we present WorkMATe, a neural network architecture that models cognitive control over working memory content and learns the appropriate control operations needed to solve complex working memory tasks. Key components of the model include a gated memory circuit that is controlled by internal actions, encoding sensory information through untrained connections, and a neural circuit that matches sensory inputs to memory content. The network is trained by means of a biologically plausible reinforcement learning rule that relies on attentional feedback and reward prediction errors to guide synaptic updates. We demonstrate that the model successfully acquires policies to solve classical working memory tasks, such as delayed recognition and delayed pro-saccade/anti-saccade tasks. In addition, the model solves much more complex tasks, including the hierarchical 12-AX task or the ABAB ordered recognition task, both of which demand an agent to independently store and updated multiple items separately in memory. Furthermore, the control strategies that the model acquires for these tasks subsequently generalize to new task contexts with novel stimuli, thus bringing symbolic production rule qualities to a neural network architecture. As such, WorkMATe provides a new solution for the neural implementation of flexible memory control

Noise Correlations Have Little Influence on the Coding of Selective Attention in Area V1

Neurons in the visual primary cortex (area V1) do not only code simple features but also whether image elements are attended or not. These attentional signals are weaker than the feature-selective responses, and their reliability may therefore be limited by the noisiness of neuronal responses. Here we show that it is possible to decode the locus of attention on a single trial from the activity of a small population of neurons in area V1. Previous studies suggested that correlations between the activities of neurons that are part of a population limit the information gain, but here we report that the impact of these noise correlations depends on the relative position of the neurons' receptive fields. Correlations reduce the benefit of pooling neuronal responses evoked by the same object but actually enhance the advantage of pooling responses evoked by different objects. These opposing effects cancelled each other at the population level, so that the net effect of the noise correlations was negligible and attention could be decoded reliably. Our results suggest that noise correlations are caused by large-scale fluctuations in cortical excitability, which can be removed by a comparison of the response strengths evoked by different objects

Interactions between higher and lower visual areas improve shape selectivity of higher level neurons-explaining crowding phenomena

Recent theories of visual perception propose that feedforward cortical processing enables rapid and automatic object categorizations, yet incorporates a limited amount of detail. Subsequent feedback processing highlights high-resolution representations in early visual areas and provides spatial detail. To verify this hypothesis, we separate the contributions of feedforward and feedback signals to the selectivity of cortical neurons in a neural network simulation that is modeled after the hierarchical feedforward-feedback organization of cortical areas. We find that in such a network the responses of high-level neurons can initially distinguish between low-resolution aspects of objects but are 'blind' to differences in detail. After several feedback-feedforward cycles of processing, however, they can also distinguish between objects that differ in detail. Moreover, we find that our model captures recent paradoxical results of crowding phenomena, showing that spatial detail that is lost in visual crowding is nevertheless able to evoke specific adaptation effects. Our results thus provide an existence proof of the feasibility of novel theoretical models and provide a mechanism to explain various psychophysical and physiological results. Introduction Within 40 ms after the light of an image hits the retina, cells in the primary visual cortex (V1) start to fire. The very first spikes already express orientation and spatial frequency selectivity. The same applies to cells in extrastriate areas that, only 10 ms later, instantaneously code for color, motion, stereo depth, etc. Even the highest levels of selectivity, such as face versus nonface in inferotemporal cortex, appear to be already expressed some 80-120 ms after the image is presente

Mouse visual cortex contains a region of enhanced spatial resolution.

The representation of space in mouse visual cortex was thought to be relatively uniform. Here we reveal, using population receptive-field (pRF) mapping techniques, that mouse visual cortex contains a region in which pRFs are considerably smaller. This region, the "focea," represents a location in space in front of, and slightly above, the mouse. Using two-photon imaging we show that the smaller pRFs are due to lower scatter of receptive-fields at the focea and an over-representation of binocular regions of space. We show that receptive-fields of single-neurons in areas LM and AL are smaller at the focea and that mice have improved visual resolution in this region of space. Furthermore, freely moving mice make compensatory eye-movements to hold this region in front of them. Our results indicate that mice have spatial biases in their visual processing, a finding that has important implications for the use of the mouse model of vision

An integrative, multiscale view on neural theories of consciousness.

How is conscious experience related to material brain processes? A variety of theories aiming to answer this age-old question have emerged from the recent surge in consciousness research, and some are now hotly debated. Although most researchers have so far focused on the development and validation of their preferred theory in relative isolation, this article, written by a group of scientists representing different theories, takes an alternative approach. Noting that various theories often try to explain different aspects or mechanistic levels of consciousness, we argue that the theories do not necessarily contradict each other. Instead, several of them may converge on fundamental neuronal mechanisms and be partly compatible and complementary, so that multiple theories can simultaneously contribute to our understanding. Here, we consider unifying, integration-oriented approaches that have so far been largely neglected, seeking to combine valuable elements from various theories