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Structured Regularizer for Neural Higher
Structured Regularizer for Neural Higher

... x, i.e. f m−n (yt−n+1:t ; t, x) = [1(yt−n+1:t = a1 ) gm (x, t), . . .]T where gm (x, t) is an arbitrary function. This function maps an input sub-sequence into a new feature space. In this work, we choose to use MLP networks for this function being able to model complex interactions among the variab ...
A Model of Prefrontal Cortical Mechanisms for Goal-directed Behavior Michael E. Hasselmo Abstract
A Model of Prefrontal Cortical Mechanisms for Goal-directed Behavior Michael E. Hasselmo Abstract

... in the ‘‘Reward’’ minicolumn to the input population gi in the ‘‘East’’ state minicolumn. These connections were strengthened during previous exploration of the environment (as described in the Methods section below), allowing units in go to activate a unit in gi. The activity spreads over internal ...
Tree-structured Representation of Melodies for Comparison
Tree-structured Representation of Melodies for Comparison

... The success of the Internet has filled the net with lots of symbolic representations of music works. Two kinds of problems arise to the user: the search for information based on content and the identification of similar works. Both belong to the pattern recognition domain. The applications of variat ...
PDF file
PDF file

... that they are not robust against wide variations in object surface properties and lighting conditions [10]. The network learning approaches in category (3) do not require a match between the left and right elements. Instead, the binocular stimuli with a specific disparity are matched with binocular ...
Location-based Activity Recognition
Location-based Activity Recognition

... other (10m in our implementation). However, it might be desirable to associate GPS traces to a street map, for example, in order to relate locations to addresses in the map. To jointly estimate the GPS to street association and trace segmentation, we construct an RMN that takes into account the spat ...
A Neural Mass Model to Simulate Different Rhythms in a Cortical
A Neural Mass Model to Simulate Different Rhythms in a Cortical

... negative self-loop; that is, they not only inhibit pyramidal neurons (as in previous model) but also inhibit themselves. This idea agrees with the observation that basket cells in the hippocampus and cortex are highly interconnected and a chain of fast inhibitory interneurons can induce γ activity p ...
Neuro-fuzzy systems
Neuro-fuzzy systems

... The weighted inputs xi o wi, where o is a t-norm and tconorm, can be general fuzzy relations too, not just simple products as in standard neurons The transfer function g can be a non-linear such as a sigmoid ...
Using Machine Learning Techniques for Stylometry
Using Machine Learning Techniques for Stylometry

... interdisciplinary research area that integrates literary stylistics, statistics and computer science in the study of the “style” or the “feel” (of a document). The style of a document is typically based on a lot of parameters – its genre or the topic (which is used in text categorization), its conte ...
Visual Categorization: How the Monkey Brain Does It
Visual Categorization: How the Monkey Brain Does It

... cat/dog categorization task. In particular, the monkeys had to perform a delayed match-to-category task where the first stimulus was shown for 600ms, followed by a 1s delay and the second, test, stimulus. In the following, we restrict our analysis to the neurons that showed stimulus selectivity by a ...
Visual Categorization: How the Monkey Brain Does It
Visual Categorization: How the Monkey Brain Does It

... cat/dog categorization task. In particular, the monkeys had to perform a delayed match-to-category task where the first stimulus was shown for 600ms, followed by a 1s delay and the second, test, stimulus. In the following, we restrict our analysis to the neurons that showed stimulus selectivity by a ...
Canonical Microcircuits for Predictive Coding
Canonical Microcircuits for Predictive Coding

... for intrinsic connections among neuronal populations. By deriving canonical forms for these computations, one can associate specific neuronal populations with specific computational roles. This analysis discloses a remarkable correspondence between the microcircuitry of the cortical column and the c ...
Learning with Hierarchical-Deep Models
Learning with Hierarchical-Deep Models

... models. In particular, we show how we can learn a hierarchical Dirichlet process (HDP) prior over the activities of the top-level features in a DBM, coming to represent both a layered hierarchy of increasingly abstract features and a tree-structured hierarchy of classes. Our model depends minimally ...
Neural Networks and Its Application in Engineering
Neural Networks and Its Application in Engineering

... Whenever we talk about a neural network, we should more properly say "artificial neural network" (ANN), because that is what we mean most of the time. Artificial neural networks are computers whose architecture is modeled after the brain. They typically consist of many hundreds of simple processing ...
PDF file
PDF file

... lobe component analysis (CCI LCA), modeling some major mechanisms within a cortical area, is suited for high-dimensional incremental self-organization of such a rich representation, due to coarse-to-fine competition imbedded by its nearly optimal statistical efficiency (i.e. minimum error in the est ...
On real-world temporal pattern recognition using Liquid State
On real-world temporal pattern recognition using Liquid State

... virtually anything in the form, shape and values of that data we’re dealing with. Also, there are many types of data, ranging from nicely constrained statistical information gathered via questionnaires or supermarket customer cards to raw sensory input acquired directly from the real world. As often ...
Scaling self-organizing maps to model large cortical networks
Scaling self-organizing maps to model large cortical networks

... The scaling equations and GLISSOM are based on the RFLISSOM (Receptive-Field Laterally Interconnected Synergetically Self-Organizing Map) computational model of cortical maps. RF-LISSOM has been successfully used to model the development of ocular dominance and orientation maps, as well as low-level ...
From spike frequency to free recall:
From spike frequency to free recall:

... method of hypothesis presentation results in an incomplete and distorted perception of the experimental data, as it is filtered through the multiple associations that each individual has with these verbal terms. Even the terms used in the title of this chapter, such as episodic memory and spatial na ...
Abstract:
Abstract:

... Stylometry – the measure of style – is a burgeoning interdisciplinary research area that integrates literary stylistics, statistics and computer science in the study of the “style” or the “feel” (of a document). The style of a document is typically based on a lot of parameters – its genre or the top ...
A Bayesian network primer
A Bayesian network primer

... (causes). The descriptors of the local models give the model parameters. The multifaceted nature of Bayesian networks follows from the fact that this representation addresses jointly three autonomous levels of the domain: the causal model, the probabilistic dependency-independency structure, and the ...
The pattern of ocular dominance columns in macaque visual cortex
The pattern of ocular dominance columns in macaque visual cortex

... Because the dark bands were similar in width to the bands of terminal degeneration which have been shown to result from single-layer lesions of the lateral geniculate body, i t seemed possible that they corresponded to ocular dominance columns. To test this idea, the boundaries of ocular dominance c ...
Self-adaptive genotype-phenotype maps: neural networks as a meta-representation
Self-adaptive genotype-phenotype maps: neural networks as a meta-representation

... steps, from its initial state, according to the encoded rule table. ...
2. Adversarial Sequence Prediction
2. Adversarial Sequence Prediction

... But, as defined in the proof of Lemma 6.2, this elegant predictor requires too much computing time to be implemented in our universe. So this still leaves open the question of whether there exist sequence predictors efficient enough to be implemented in this universe and that can learn to predict an ...
Document
Document

... classify instances into two classes The predictors can encode 2^5=32 possible distinct patterns. Assume all patterns are equally probable. Hence the chances of the two cases having different predictive patterns are 31/32=97%. Thus in 97% of our samples of size two, the five variables are sufficient ...
Predictive Coding as a Model of Biased Competition in Visual
Predictive Coding as a Model of Biased Competition in Visual

... expectation and sensory-driven analysis. Rather than passively responding to the output activity generated by preceding stages of cortical processing, PC proposes that higher levels of cortex actively predict the input they expect to receive. Furthermore, it is proposed that cortical feedback connec ...
Down - 서울대 Biointelligence lab
Down - 서울대 Biointelligence lab

... The high-order mental abilities  An emerging property of specialized neural networks  The number of neurons in the central nervous systems 1012  We aim to understand the principal organization of neuron-like elements and how such structures can support and enable particular mental processes. ...
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Hierarchical temporal memory



Hierarchical temporal memory (HTM) is an online machine learning model developed by Jeff Hawkins and Dileep George of Numenta, Inc. that models some of the structural and algorithmic properties of the neocortex. HTM is a biomimetic model based on the memory-prediction theory of brain function described by Jeff Hawkins in his book On Intelligence. HTM is a method for discovering and inferring the high-level causes of observed input patterns and sequences, thus building an increasingly complex model of the world.Jeff Hawkins states that HTM does not present any new idea or theory, but combines existing ideas to mimic the neocortex with a simple design that provides a large range of capabilities. HTM combines and extends approaches used in Sparse distributed memory, Bayesian networks, spatial and temporal clustering algorithms, while using a tree-shaped hierarchy of nodes that is common in neural networks.
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