A Model of the Ventral Visual System Based on Temporal Stability and Local Memory

The cerebral cortex is a remarkably homogeneous structure suggesting a rather generic computational machinery. Indeed, under a variety of conditions, functions attributed to specialized areas can be supported by other regions. However, a host of studies have laid out an ever more detailed map of functional cortical areas. This leaves us with the puzzle of whether different cortical areas are intrinsically specialized, or whether they differ mostly by their position in the processing hierarchy and their inputs but apply the same computational principles. Here we show that the computational principle of optimal stability of sensory representations combined with local memory gives rise to a hierarchy of processing stages resembling the ventral visual pathway when it is exposed to continuous natural stimuli. Early processing stages show receptive fields similar to those observed in the primary visual cortex. Subsequent stages are selective for increasingly complex configurations of local features, as observed in higher visual areas. The last stage of the model displays place fields as observed in entorhinal cortex and hippocampus. The results suggest that functionally heterogeneous cortical areas can be generated by only a few computational principles and highlight the importance of the variability of the input signals in forming functional specialization

Spectral Modes of Network Dynamics Reveal Increased Informational Complexity Near Criticality

What does the informational complexity of dynamical networked systems tell us about intrinsic mechanisms and functions of these complex systems? Recent complexity measures such as integrated information have sought to operationalize this problem taking a whole-versus-parts perspective, wherein one explicitly computes the amount of information generated by a network as a whole over and above that generated by the sum of its parts during state transitions. While several numerical schemes for estimating network integrated information exist, it is instructive to pursue an analytic approach that computes integrated information as a function of network weights. Our formulation of integrated information uses a Kullback-Leibler divergence between the multi-variate distribution on the set of network states versus the corresponding factorized distribution over its parts. Implementing stochastic Gaussian dynamics, we perform computations for several prototypical network topologies. Our findings show increased informational complexity near criticality, which remains consistent across network topologies. Spectral decomposition of the system's dynamics reveals how informational complexity is governed by eigenmodes of both, the network's covariance and adjacency matrices. We find that as the dynamics of the system approach criticality, high integrated information is exclusively driven by the eigenmode corresponding to the leading eigenvalue of the covariance matrix, while sub-leading modes get suppressed. The implication of this result is that it might be favorable for complex dynamical networked systems such as the human brain or communication systems to operate near criticality so that efficient information integration might be achieved

Local and global gating of synaptic plasticity

Mechanisms influencing learning in neural networks are usually investigated on either a local or a global scale. The former relates to synaptic processes, the latter to unspecific modulatory systems. Here we study the interaction of a local learning rule that evaluates coincidences of pre- and postsynaptic action potentials and a global modulatory mechanism, such as the action of the basal forebrain onto cortical neurons. The simulations demonstrate that the interaction of these mechanisms leads to a learning rule supporting fast learning rates, stability, and flexibility. Furthermore, the simulations generate two experimentally testable predictions on the dependence of backpropagating action potential on basal forebrain activity and the relative timing of the activity of inhibitory and excitatory neurons in the neocortex.We are grateful to Konrad Körding and Mike Merzenich for valuable discussions of the previous work on the learning rule and the experimental data and Daniel Kiper for comments on a previous version of the manuscript. We are happy to acknowledge the support of SPP Neuroinformatics (grants 5002–44888/2&3 to P. F. M. J. V.), SNF (grant 31-51059.97, awarded to P. K.), and an FPU grant from MEC (M. A. S.-M., Spain)

Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework

In this paper, we argue that the future of Artificial Intelligence research resides in two keywords: integration and embodiment. We support this claim by analyzing the recent advances of the field. Regarding integration, we note that the most impactful recent contributions have been made possible through the integration of recent Machine Learning methods (based in particular on Deep Learning and Recurrent Neural Networks) with more traditional ones (e.g. Monte-Carlo tree search, goal babbling exploration or addressable memory systems). Regarding embodiment, we note that the traditional benchmark tasks (e.g. visual classification or board games) are becoming obsolete as state-of-the-art learning algorithms approach or even surpass human performance in most of them, having recently encouraged the development of first-person 3D game platforms embedding realistic physics. Building upon this analysis, we first propose an embodied cognitive architecture integrating heterogenous sub-fields of Artificial Intelligence into a unified framework. We demonstrate the utility of our approach by showing how major contributions of the field can be expressed within the proposed framework. We then claim that benchmarking environments need to reproduce ecologically-valid conditions for bootstrapping the acquisition of increasingly complex cognitive skills through the concept of a cognitive arms race between embodied agents.Comment: Updated version of the paper accepted to the ICDL-Epirob 2017 conference (Lisbon, Portugal

Towards sample-efficient policy learning with DAC-ML

The sample-inefficiency problem in Artificial Intelligence refers to the inability of current Deep Reinforcement Learning models to optimize action policies within a small number of episodes. Recent studies have tried to overcome this limitation by adding memory systems and architectural biases to improve learning speed, such as in Episodic Reinforcement Learning. However, despite achieving incremental improvements, their performance is still not comparable to how humans learn behavioral policies. In this paper, we capitalize on the design principles of the Distributed Adaptive Control (DAC) theory of mind and brain to build a novel cognitive architecture (DAC-ML) that, by incorporating a hippocampus-inspired sequential memory system, can rapidly converge to effective action policies that maximize reward acquisition in a challenging foraging task

Virtual reality based upper extremity rehabilitation following stroke: a review

In the last decade there have been major developments in the creation of interactive virtual scenarios for the rehabilitation of motor deficits following stroke. Virtual reality technology is arising as a promising tool to diagnose, monitor and induce functional recovery after lesions to the nervous system. This evidence has grown in the last few years, as effort has been made to develop virtual scenarios that are built on the knowledge of mechanisms of recovery. In this paper we review the state of the art virtual reality techniques for rehabilitation of functionality of the upper extremities following stroke. We refer to some of the main systems that have been developed within different rehabilitative approaches such as learning by imitation, reinforced feedback, haptic feedback, augmented practice and repetition, video capture virtual reality, exoskeletons, mental practice, action observation and execution, and others. The major findings of these studies show that virtual reality technologies will become a more and more essential ingredient in the treatment of stroke and other disorders of the nervous system.info:eu-repo/semantics/publishedVersio

The relationship between corporate governance mechanisms and firm value: evidence from the largest Australian firms

The mixed findings in the literature pertaining to the relationship between corporate governance mechanisms and firm value have resulted in the endogeneity issue of the former becoming central to discussions in corporate governance and corporate finance studies. As endogeneity can be in the form of reverse causality and/or in a dynamic sense, this thesis examines the relationships between corporate governance mechanisms that are proxied by ownership concentration and debt and firm value in the largest Australian firms from 1997 to 2008. The study investigates this issue through three different tests. First, the study examines whether there are any causal relationships between ownership concentration, debt and firm value. Second, the study investigates whether ownership concentration, debt and firm value are best treated as a group in order to assess their influence on each other. Therefore, the study assesses their substitutability or complementarity. Third, the study examines whether there are any non-linear relationships between ownership concentration and firm value on the one hand and debt on the other hand, as well as between debt and firm value. In investigating the dynamic endogeneity issue through these tests, the study employs two methodologies: two-way fixed effects (FE) and the two-step system generalised method of moments (GMM). In the first test, the study finds a causal relationship between ownership concentration and firm value as well as between debt and firm value. The causality is found to run from firm value to ownership concentration in a negative direction and from debt to firm value also in a negative direction. No causal relationship is found between ownership concentration and debt. However, further investigation by using sub-samples of ownership concentration reveals that there is causality between these two corporate governance mechanisms. It is found that causality runs from ownership concentration to debt in a negative direction. This test finds that firm value causes ownership concentration, thus providing evidence that endogeneity in the form of reverse causality exists. However, in the dynamic sense, it is found that dynamic endogeneity is not an issue in this test. The second test discovers that there is no evidence that ownership concentration, debt and firm value are effective as a group. Therefore, the study fails to identify their substitutability or complementarity. Furthermore, this test finds that dynamic endogeneity is not an issue in influencing ownership concentration, debt and firm value when they are tested as a group. In the final test, the study finds that there is a non-linear relationship between ownership concentration and firm value. This non-linear association is found to have an influence on the non-linearity between ownership concentration and debt. Further, the study also finds that debt and firm value are non-linear. It is found that the dynamic endogeneity issue does influence the non-linearity functions of ownership concentration but not the non-linearity functions of debt. The thesis concludes that dynamic endogeneity is not a serious issue in influencing the relationship between corporate governance mechanisms and firm value in the largest Australian firms

Virtual reality based rehabilitation speeds up functional recovery of the upper extremities after stroke: a randomized controlled pilot study in the acute phase of stroke using the rehabilitation gaming system

Given the incidence of stroke, the need has arisen to consider more self-managed rehabilitation approaches. A promising technology is Virtual Reality (VR). Thus far, however, it is not clear what the benefits of VR systems are when compared to conventional methods. Here we investigated the clinical impact of one such system, the Rehabilitation Gaming System (RGS), on the recovery time course of acute stroke. RGS combines concepts of action execution and observation with an automatic individualization of training. METHODS. Acute stroke patients (n = 8) used the RGS during 12 weeks in addition to conventional therapy. A control group (n = 8) performed a time matched alternative treatment, which consisted of intense occupational therapy or non-specific interactive games. RESULTS. At the end of the treatment, between-group comparisons showed that the RGS group displayed significantly improved performance in paretic arm speed that was matched by better performance in the arm subpart of the Fugl-Meyer Assessment Test and the Chedoke Arm and Hand Activity Inventory. In addition, the RGS group presented a significantly faster improvement over time for all the clinical scales during the treatment period. CONCLUSIONS. Our results suggest that rehabilitation with the RGS facilitates the functional recovery of the upper extremities and that this system is therefore a promising tool for stroke neurorehabilitation.info:eu-repo/semantics/publishedVersio

Differential neural mechanisms for early and late prediction error detection

Emerging evidence indicates that prediction, instantiated at different perceptual levels, facilitate visual processing and enable prompt and appropriate reactions. Until now, the mechanisms underlying the effect of predictive coding at different stages of visual processing have still remained unclear. Here, we aimed to investigate early and late processing of spatial prediction violation by performing combined recordings of saccadic eye movements and fast event-related fMRI during a continuous visual detection task. Psychophysical reverse correlation analysis revealed that the degree of mismatch between current perceptual input and prior expectations is mainly processed at late rather than early stage, which is instead responsible for fast but general prediction error detection. Furthermore, our results suggest that conscious late detection of deviant stimuli is elicited by the assessment of prediction error’s extent more than by prediction error per se. Functional MRI and functional connectivity data analyses indicated that higher-level brain systems interactions modulate conscious detection of prediction error through top-down processes for the analysis of its representational content, and possibly regulate subsequent adaptation of predictivemodels. Overall, our experimental paradigm allowed to dissect explicit from implicit behavioral and neural responses to deviant stimuli in terms of their reliance on predictive models