A scientific theory of ars memoriae: spatial view cells in a continuous
... The architecture of a continuous attractor neural network (CANN). The architecture is the same as that of a discrete attractor neural network. During learning, external inputs ei with Gaussian spatial fields force the output neurons to fire with rates ri, the recurrent collaterals produce the same r ...
... The architecture of a continuous attractor neural network (CANN). The architecture is the same as that of a discrete attractor neural network. During learning, external inputs ei with Gaussian spatial fields force the output neurons to fire with rates ri, the recurrent collaterals produce the same r ...
A hybrid case-based reasoning and neural network approach to
... related applications [1]. Traditionally, case-based reasoning (CBR) [2-9] has been successfully applied to fault diagnosis for customer service support or help desk. CBR systems rely on building a large repository of diagnostic cases (or past service reports) in order to circumvent the difficult tas ...
... related applications [1]. Traditionally, case-based reasoning (CBR) [2-9] has been successfully applied to fault diagnosis for customer service support or help desk. CBR systems rely on building a large repository of diagnostic cases (or past service reports) in order to circumvent the difficult tas ...
A simulation of parahippocampal and hippocampal structures guiding spatial navigation of
... To reduce computing time, the initial encoding of the environment occurs with the virtual rat traversing a pre-coded trajectory through the T-maze. This brings the rat up the stem of the maze, into the left arm, back to the stem, up into the right arm and back to the original starting position in th ...
... To reduce computing time, the initial encoding of the environment occurs with the virtual rat traversing a pre-coded trajectory through the T-maze. This brings the rat up the stem of the maze, into the left arm, back to the stem, up into the right arm and back to the original starting position in th ...
A Case for a Situationally Adaptive Many
... programs are also easily parallelizable, which is expected to be helpful for problems involving larger complexity. Hardware support for MLP-like systems is also showing promise, with IBM’s Truenorth chip being an example. This is just one prototype implementation for SAS, we expect to evaluate other ...
... programs are also easily parallelizable, which is expected to be helpful for problems involving larger complexity. Hardware support for MLP-like systems is also showing promise, with IBM’s Truenorth chip being an example. This is just one prototype implementation for SAS, we expect to evaluate other ...
Mininw Mlrltivzarid-e Time C&w
... those methods, without the need for any optimization. The basic idea is that each iteration selects the candidate unit (or basis function, in our case) U whose outputs u covary the most with the current error residuals e. Falhman (FL90) proposed using the standard covariance definition, to give a si ...
... those methods, without the need for any optimization. The basic idea is that each iteration selects the candidate unit (or basis function, in our case) U whose outputs u covary the most with the current error residuals e. Falhman (FL90) proposed using the standard covariance definition, to give a si ...
Application of Neural Networks for Intelligent Video
... follow (Bourg, p. 212). Consider, for instance, an enemy in a combat game. Suppose we decide that the enemy will be idle most of the time except when the player is within eyesight and has a weapon equipped. Thus, we have defined a set of rules that dictate the enemy character. This technique is simp ...
... follow (Bourg, p. 212). Consider, for instance, an enemy in a combat game. Suppose we decide that the enemy will be idle most of the time except when the player is within eyesight and has a weapon equipped. Thus, we have defined a set of rules that dictate the enemy character. This technique is simp ...
Statistical mechanics of neocortical interactions: Constraints on 40
... retention of 7 ± 2 items [19]. This is true even for apparently exceptional memory performers who, while they may be capable of more efficient encoding and retrieval of STM, and while they may be more efficient in ‘‘chunking’’ larger patterns of information into single items, nevertheless are limite ...
... retention of 7 ± 2 items [19]. This is true even for apparently exceptional memory performers who, while they may be capable of more efficient encoding and retrieval of STM, and while they may be more efficient in ‘‘chunking’’ larger patterns of information into single items, nevertheless are limite ...
(G5AIAI) - 2001/02
... In this part of the answer I am looking for the “classic” description of The Turing Test (i.e an interrogator asks a computer and a human questions without knowing which is which and than has to decide which is the human and which is the computer). If the computer can fool the interrogator then it h ...
... In this part of the answer I am looking for the “classic” description of The Turing Test (i.e an interrogator asks a computer and a human questions without knowing which is which and than has to decide which is the human and which is the computer). If the computer can fool the interrogator then it h ...
Computational Intelligence: Neural Networks and
... ANN [15]. First, it can perform more complex tasks than any of its components (i.e., individual ANNs in the ensemble). Second, it can make the overall system easier to understand and modify. Finally, it is more robust than a monolithic ANN, and can show graceful performance degradation in situations ...
... ANN [15]. First, it can perform more complex tasks than any of its components (i.e., individual ANNs in the ensemble). Second, it can make the overall system easier to understand and modify. Finally, it is more robust than a monolithic ANN, and can show graceful performance degradation in situations ...
sai-avatar1.doc
... conversation, and derives an architecture for implementing these features through automation. First the thesis describes the process of face-to-face conversation and what nonverbal behaviors contribute to its success. It then presents a theoretical framework that explains how a text message can be a ...
... conversation, and derives an architecture for implementing these features through automation. First the thesis describes the process of face-to-face conversation and what nonverbal behaviors contribute to its success. It then presents a theoretical framework that explains how a text message can be a ...
Learning place cells, grid cells and invariances: A unifying model
... the target rate everywhere (Supplementary Online Material, SOM). From this homogeneous state, a small potentiation of one excitatory weight leads to an increased firing rate of the output neuron at the location of the associated place field (highlighted red curve in Fig. 1e). To bring the output neu ...
... the target rate everywhere (Supplementary Online Material, SOM). From this homogeneous state, a small potentiation of one excitatory weight leads to an increased firing rate of the output neuron at the location of the associated place field (highlighted red curve in Fig. 1e). To bring the output neu ...
The Involvement of Recurrent Connections in Area CA3 in
... neurons, but their activity during locomotion changes periodically at the theta frequency. We ignore this temporal variation, as well as the diversity of interneurons and patterns of connectivity, and include in the model a single global inhibitory neuron, which fosters competition between stored pa ...
... neurons, but their activity during locomotion changes periodically at the theta frequency. We ignore this temporal variation, as well as the diversity of interneurons and patterns of connectivity, and include in the model a single global inhibitory neuron, which fosters competition between stored pa ...
Classification using sparse representations
... King’s College London, Department of Informatics, London. UK. Abstract Representing signals as linear combinations of basis vectors sparsely selected from an overcomplete dictionary has proven to be advantageous for many applications in pattern recognition, machine learning, signal processing, and c ...
... King’s College London, Department of Informatics, London. UK. Abstract Representing signals as linear combinations of basis vectors sparsely selected from an overcomplete dictionary has proven to be advantageous for many applications in pattern recognition, machine learning, signal processing, and c ...
Intrusion Detection using Fuzzy Clustering and Artificial Neural
... clustering and neural networks for an Intrusion Detection System (IDS). While neural networks are effective in capturing the non-linearity in data provided, it also has certain limitations including the requirement of high computational resources. By clustering the data, each ANN is trained on a par ...
... clustering and neural networks for an Intrusion Detection System (IDS). While neural networks are effective in capturing the non-linearity in data provided, it also has certain limitations including the requirement of high computational resources. By clustering the data, each ANN is trained on a par ...
Down - 서울대 : Biointelligence lab
... Dynamics of such maps modeled as dynamic neural field theory Discussion of such competitive dynamics in a variety of examples in different parts of the brain Formal discussions of population coding and some extensions of the basic models including dynamic updates of represented features with changin ...
... Dynamics of such maps modeled as dynamic neural field theory Discussion of such competitive dynamics in a variety of examples in different parts of the brain Formal discussions of population coding and some extensions of the basic models including dynamic updates of represented features with changin ...
Disjunctive Temporal Planning with Uncertainty
... (SC) if there exists a decision that, combined with any realisation, satisfies the constraints. In other words, there is a way to assign values to the decision variables such that, given any values for the parameters, at least one disjunct on each constraint is satisfied. Note this means that a DTPU ...
... (SC) if there exists a decision that, combined with any realisation, satisfies the constraints. In other words, there is a way to assign values to the decision variables such that, given any values for the parameters, at least one disjunct on each constraint is satisfied. Note this means that a DTPU ...
Finally, the peak firing rate within any one place field of a single cell
... answer this question. At an abstract level, well-known computational algorithms exist which create sparse, pattern-separated representations from distributed input patterns. A simple example is the competitive-learning pattern classification device described by Rumelhart and Zipser (1986), which has ...
... answer this question. At an abstract level, well-known computational algorithms exist which create sparse, pattern-separated representations from distributed input patterns. A simple example is the competitive-learning pattern classification device described by Rumelhart and Zipser (1986), which has ...
Unsupervised Many-to-Many Object Matching for Relational Data
... ReMatch can find the shared groups, and perform crossdomain recommendations without common user/item identifiers. In multi-lingual corpus analysis, ReMatch can be used for discovering shared topics across languages by applying it to multi-lingual document-word networks without alignments. Most exist ...
... ReMatch can find the shared groups, and perform crossdomain recommendations without common user/item identifiers. In multi-lingual corpus analysis, ReMatch can be used for discovering shared topics across languages by applying it to multi-lingual document-word networks without alignments. Most exist ...
On Theoretical Properties of Sum
... probabilistic model and showed impressive results for image completion [Poon and Domingos, 2011, Dennis and Ventura, 2012, Peharz et al., 2013], computer vision [Amer and Todorovic, 2012], classification [Gens and Domingos, 2012] and speech/language modeling [Peharz et al., 2014, Cheng et al., 2014] ...
... probabilistic model and showed impressive results for image completion [Poon and Domingos, 2011, Dennis and Ventura, 2012, Peharz et al., 2013], computer vision [Amer and Todorovic, 2012], classification [Gens and Domingos, 2012] and speech/language modeling [Peharz et al., 2014, Cheng et al., 2014] ...
An Overview of Some Recent Developments in Bayesian Problem
... Bayesian network to model existing vehicle capabilities and the uncertainty regarding the state of those capabilities. It selects from the available alternatives the best response to the unanticipated event with the aim of maximizing the overall achievement of mission objectives. Bayesian Ship Self ...
... Bayesian network to model existing vehicle capabilities and the uncertainty regarding the state of those capabilities. It selects from the available alternatives the best response to the unanticipated event with the aim of maximizing the overall achievement of mission objectives. Bayesian Ship Self ...
Artificial Intelligence Chapter 7 - Computer Science
... • Artificial Neural Networks are powerful computational systems consisting of many simple processing elements connected together to perform tasks analogously to biological brains. • They are massively parallel, which makes them efficient, robust, fault tolerant and noise tolerant. ...
... • Artificial Neural Networks are powerful computational systems consisting of many simple processing elements connected together to perform tasks analogously to biological brains. • They are massively parallel, which makes them efficient, robust, fault tolerant and noise tolerant. ...
A Theory of Cerebral Cortex - Temporal Dynamics of Learning Center
... arbitrarily (and permanently) assigned to represent exactly one unique English word of the 10,000 most frequently seen words in a 1.4 billion word corpus of general proper English text (e.g., news stories). Concatenating the sentences of the corpus into one long string of words with punctuation (and ...
... arbitrarily (and permanently) assigned to represent exactly one unique English word of the 10,000 most frequently seen words in a 1.4 billion word corpus of general proper English text (e.g., news stories). Concatenating the sentences of the corpus into one long string of words with punctuation (and ...
Diagnosis of Pulmonary Embolism Using Fuzzy Inference System
... is divided into 5 classes. What you see here is the tweaking that has to be given to all the classes in order to implement the PIOPED criteria to its best fit. ...
... is divided into 5 classes. What you see here is the tweaking that has to be given to all the classes in order to implement the PIOPED criteria to its best fit. ...
A Multistrategy Approach to Classifier Learning from Time
... statistical property: namely, how closely a particular type of stochastic process fits (i.e., can generate) observed data. Our objective is to identify the predominant process type to select an appropriate learning architecture. The memory form, as defined by Mozer (1994), is a property of a time se ...
... statistical property: namely, how closely a particular type of stochastic process fits (i.e., can generate) observed data. Our objective is to identify the predominant process type to select an appropriate learning architecture. The memory form, as defined by Mozer (1994), is a property of a time se ...
Learning Optimal Bayesian Networks Using A
... The basic idea of our algorithm is to formulate learning optimal Bayesian networks as a shortest path finding problem. We use the order graph in Figure 1 as the search graph. We let the top-most node that contains no variables be the start state and the bottom-most node with all variables be the goa ...
... The basic idea of our algorithm is to formulate learning optimal Bayesian networks as a shortest path finding problem. We use the order graph in Figure 1 as the search graph. We let the top-most node that contains no variables be the start state and the bottom-most node with all variables be the goa ...
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.