1,721,119 research outputs found
Bridging structure and function: A model of sequence learning and prediction in primary visual cortex
No full textCriticality meets learning : criticality signatures in a self-organizing recurrent neural network
No full textMany experiments have suggested that the brain operates close to a critical state, based on signatures of criticality such as power-law distributed neuronal avalanches. In neural network models, criticality is a dynamical state that maximizes information processing capacities, e.g. sensitivity to input, dynamical range and storage capacity, which makes it a favorable candidate state for brain function. Although models that self-organize towards a critical state have been proposed, the relation between criticality signatures and learning is still unclear. Here, we investigate signatures of criticality in a self-organizing recurrent neural network (SORN). Investigating criticality in the SORN is of particular interest because it has not been developed to show criticality. Instead, the SORN has been shown to exhibit spatio-temporal pattern learning through a combination of neural plasticity mechanisms and it reproduces a number of biological findings on neural variability and the statistics and fluctuations of synaptic efficacies. We show that, after a transient, the SORN spontaneously self-organizes into a dynamical state that shows criticality signatures comparable to those found in experiments. The plasticity mechanisms are necessary to attain that dynamical state, but not to maintain it. Furthermore, onset of external input transiently changes the slope of the avalanche distributions – matching recent experimental findings. Interestingly, the membrane noise level necessary for the occurrence of the criticality signatures reduces the model’s performance in simple learning tasks. Overall, our work shows that the biologically inspired plasticity and homeostasis mechanisms responsible for the SORN’s spatio-temporal learning abilities can give rise to criticality signatures in its activity when driven by random input, but these break down under the structured input of short repeating sequences
Pseudo-random Number Generation Using Binary Recurrent Neural Networks
No full text39 p.Pseudo-random number generators are deterministic functions that map, in most
cases, a state x to a new state x˙ using some update function in order to generate
pseudo-random data. Use of these numbers is an integral part of computer science,
stochastic physical & statistical simulation, and cryptography. Because of the deterministic
nature of these functions, it is impossible to speak of the resultant numbers
as truly random. Therefore, the primary goal of pseudo-random number generation
is to create values that are statistically identical to truly random numbers.
To this end, a few specific characteristics are desirable. First, there should be
a way to extract some value from the function (such as a single bit) at discrete
intervals that cannot be guessed with probability greater than 50% if only f, the
update function, is known. Additionally, functions with long limit cycles and whose
cycle lengths grow exponentially in the size of one or more system variables are among
those well-suited for random number generation. Finally, the values generated should
be independently and identically distributed over the given output interval.
Many different, well-established methods exist for generating pseudo-random
numbers. Each of these methods has advantages and disadvantages related to its efficiency
and effectiveness. In this project, we present the results of a unique pseudorandom
number generator created using binary recurrent neural networks trained
with two types of neuronal plasticity, anti-spike-timing dependent plasticity (anti-
STDP) and intrinsic plasticity (IP). We subject our results to industry-standard
random number generator test suites, in addition to performing empirical analysis
on the dynamics of our simulated networks. We show that the interaction of these
types of plasticity creates network dynamics well-suited for pseudo-random number
generation.Materials made available to the public 3/20/2014 with consent of the author.Frankfurt Institute for Advanced StudiesJohann-Wolfgang-Goethe Universität, Frankfurt, GermanyHoward Hughes Medical InstituteUniversität Frankfurt am MainHoward Hughes Medical Institut
Young children's understanding of perception and false belief: Hiding objects from others
No full text
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Learning visual object detection and localisation using icVision
No full textBuilding artificial agents and robots that can act in an intelligent way is one of the main research goals in artificial intelligence and robotics. Yet it is still hard to integrate functional cognitive processes into these systems. We present a framework combining computer vision and machine learning for the learning of object recognition in humanoid robots. A biologically inspired, bottom-up architecture is introduced to facilitate visual perception and cognitive robotics research. It aims to mimic processes in the human brain performing visual cognition tasks.\ud
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A number of experiments with this icVision framework are described. We showcase both detection and identification in the image plane (2D), using machine learning. In addition we show how a biologically inspired attention mechanism allows for fully autonomous learning of visual object representations. Furthermore localising the detected objects in 3D space is presented, which in turn can be used to create a model of the environment
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Cognitive development in context: Learning to pay attention
No full textA developing system must be able to learn new things without forgetting what it has learned before. It should be capable of reacting in different ways to the same stimuli in different contexts. Context sensitive reinforcement learning, which parallels some of the functions of the basal ganglia, is a learning algorithm that fulfills this requirement when the context is explicitly given. Here, we extend the algorithm with the ability to identify the relevant features of the environment that defines the context. It is suggested that this is a critical component of an architecture for cognitive development and we present simulation results that illustrate the operation of the system
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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