A new approach to solving the feature-binding problem in primate vision

The hidden structure of consciousness

According to Loorits, if we want consciousness to be explained in terms of natural sciences, we should be able to analyze its seemingly non-structural aspects, like qualia, in structural terms. However, the studies conducted over the last three decades do not seem to be able to bridge the explanatory gap between physical phenomena and phenomenal experience. One possible way to bridge the explanatory gap is to seek the structure of consciousness within consciousness itself, through a phenomenal analysis of the qualitative aspects of experience. First, this analysis leads us to identify the explanandum concerning the simplest forms of experience not in qualia but in the unitary set of qualities found in early vision. Second, it leads us to hypothesize that consciousness is also made up of non-apparent parts, and that there exists a hidden structure of consciousness. This structure, corresponding to a simple early visual experience, is constituted by a Hierarchy of Spatial Belongings nested within each other. Each individual Spatial Belonging is formed by a primary content and a primary space. The primary content can be traced in the perceptibility of the contents we can distinguish in the phenomenal field. The primary space is responsible for the perceptibility of the content and is not perceptible in itself. However, the phenomenon I refer to as subtraction of visibility allows us to characterize it as phenomenally negative. The hierarchical relationships between Spatial Belongings can ensure the qualitative nature of components of perceptual organization, such as object, background, and detail. The hidden structure of consciousness presents aspects that are decidedly counterintuitive compared to our idea of phenomenal experience. However, on the one hand, the Hierarchy of Spatial Belongings can explain the qualities of early vision and their appearance as a unitary whole, while on the other hand, it might be more easily explicable in terms of brain organization. In other words, the hidden structure of consciousness can be considered a bridge structure which, placing itself at an intermediate level between experience and physical properties, can contribute to bridging the explanatory gap.

Simple and complex cells revisited: toward a selectivity-invariance model of object recognition

This paper presents a theoretical perspective on modeling ventral stream processing by revisiting the computational abstraction of simple and complex cells. In parallel to David Marr's vision theory, we organize the new perspective into three levels. At the computational level, we abstract simple and complex cells into space partitioning and composition in a topological space based on the redundancy exploitation hypothesis of Horace Barlow. At the algorithmic level, we present a hierarchical extension of sparse coding by exploiting the manifold constraint in high-dimensional space (i.e., the blessing of dimensionality). The resulting over-parameterized models for object recognition differ from existing hierarchical models by disentangling the objectives of selectivity and invariance computation. It is possible to interpret our hierarchical construction as a computational implementation of cortically local subspace untangling for object recognition and face representation, which are closely related to exemplar-based and axis-based coding in the medial temporal lobe. At the implementation level, we briefly discuss two possible implementations based on asymmetric sparse autoencoders and divergent spiking neural networks.

On temporal scale-free non-periodic stimulation and its mechanisms as an infinite improbability drive of the brain's functional connectogram

Rationalized development of electrical stimulation (ES) therapy is of paramount importance. Not only it will foster new techniques and technologies with increased levels of safety, efficacy, and efficiency, but it will also facilitate the translation from basic research to clinical practice. For such endeavor, design of new technologies must dialogue with state-of-the-art neuroscientific knowledge. By its turn, neuroscience is transitioning-a movement started a couple of decades earlier-into adopting a new conceptual framework for brain architecture, in which time and thus temporal patterns plays a central role in the neuronal representation of sampled data from the world. This article discusses how neuroscience has evolved to understand the importance of brain rhythms in the overall functional architecture of the nervous system and, consequently, that neuromodulation research should embrace this new conceptual framework. Based on such support, we revisit the literature on standard (fixed-frequency pulsatile stimuli) and mostly non-standard patterns of ES to put forward our own rationale on how temporally complex stimulation schemes may impact neuromodulation strategies. We then proceed to present a low frequency, on average (thus low energy), scale-free temporally randomized ES pattern for the treatment of experimental epilepsy, devised by our group and termed NPS (Non-periodic Stimulation). The approach has been shown to have robust anticonvulsant effects in different animal models of acute and chronic seizures (displaying dysfunctional hyperexcitable tissue), while also preserving neural function. In our understanding, accumulated mechanistic evidence suggests such a beneficial mechanism of action may be due to the natural-like characteristic of a scale-free temporal pattern that may robustly compete with aberrant epileptiform activity for the recruitment of neural circuits. Delivering temporally patterned or random stimuli within specific phases of the underlying oscillations (i.e., those involved in the communication within and across brain regions) could both potentiate and disrupt the formation of neuronal assemblies with random probability. The usage of infinite improbability drive here is obviously a reference to the "The Hitchhiker's Guide to the Galaxy" comedy science fiction classic, written by Douglas Adams. The parallel is that dynamically driving brain functional connectogram, through neuromodulation, in a manner that would not favor any specific neuronal assembly and/or circuit, could re-stabilize a system that is transitioning to fall under the control of a single attractor. We conclude by discussing future avenues of investigation and their potentially disruptive impact on neurotechnology, with a particular interest in NPS implications in neural plasticity, motor rehabilitation, and its potential for clinical translation.

Quiet Trajectories as Neural Building Blocks

Our concept of the neural mechanisms of working memory has recently undergone an upheaval, because of two transformative concepts: multivariate neural state trajectories and the activity-silent hypothesis. I will argue that putting these concepts together raises the difficult problem of "quiet trajectories," where future neural activity is not fully determined by current activity. However, this also promises new building blocks for neural computation.

Oscillations in the central brain of Drosophila are phase locked to attended visual features

Object-based attention describes the brain's capacity to prioritize one set of stimuli while ignoring others. Human research suggests that the binding of diverse stimuli into one attended percept requires phase-locked oscillatory activity in the brain. Even insects display oscillatory brain activity during visual attention tasks, but it is unclear if neural oscillations in insects are selectively correlated to different features of attended objects. We addressed this question by recording local field potentials in the Drosophila central complex, a brain structure involved in visual navigation and decision making. We found that attention selectively increased the neural gain of visual features associated with attended objects and that attention could be redirected to unattended objects by activation of a reward circuit. Attention was associated with increased beta (20- to 30-Hz) oscillations that selectively locked onto temporal features of the attended visual objects. Our results suggest a conserved function for the beta frequency range in regulating selective attention to salient visual features.

Self-organising coordinate transformation with peaked and monotonic gain modulation in the primate dorsal visual pathway

We study a self-organising neural network model of how visual representations in the primate dorsal visual pathway are transformed from an eye-centred to head-centred frame of reference. The model has previously been shown to robustly develop head-centred output neurons with a standard trace learning rule, but only under limited conditions. Specifically it fails when incorporating visual input neurons with monotonic gain modulation by eye-position. Since eye-centred neurons with monotonic gain modulation are so common in the dorsal visual pathway, it is an important challenge to show how efferent synaptic connections from these neurons may self-organise to produce head-centred responses in a subpopulation of postsynaptic neurons. We show for the first time how a variety of modified, yet still biologically plausible, versions of the standard trace learning rule enable the model to perform a coordinate transformation from eye-centred to head-centred reference frames when the visual input neurons have monotonic gain modulation by eye-position.

A speed-accurate self-sustaining head direction cell path integration model without recurrent excitation

The head direction (HD) system signals HD in an allocentric frame of reference. The system is able to update firing based on internally derived information about self-motion, a process known as path integration. Of particular interest is how path integration might maintain concordance between true HD and internally represented HD. Here we present a self-sustaining two-layer model, capable of self-organizing, which produces extremely accurate path integration. The implications of this work for future investigations of HD system path integration are discussed.