Tracking the Emergence of Conceptual Knowledge during Human
... conceptual knowledge in a context quite different from the original learning situation. Importantly, in a separate follow-up behavioral experiment, where participants provided verbal descriptions of the conceptual structure of the task after each learning block (see Supplemental Results and Suppleme ...
... conceptual knowledge in a context quite different from the original learning situation. Importantly, in a separate follow-up behavioral experiment, where participants provided verbal descriptions of the conceptual structure of the task after each learning block (see Supplemental Results and Suppleme ...
Epilepsy in Small
... necessary to allow enough time for the entire network to be activated before the first cells recovered. The refractory period can be generated by many mechanisms, either presynaptic or postsynaptic, and we leave this undetermined. The important factor is that the refractory time is on the order of t ...
... necessary to allow enough time for the entire network to be activated before the first cells recovered. The refractory period can be generated by many mechanisms, either presynaptic or postsynaptic, and we leave this undetermined. The important factor is that the refractory time is on the order of t ...
eref Saglroglu Intelligent Systems Research Group, Contra
... meaningful to robots. Recently there has been increasing interest in upgrading robot intelligence by using multiple sensors as shown in Fig.3. [104,106]. In the figure, for a specific task, cameras provide locational information about the objects. The colour camera helps selecting a particular obje ...
... meaningful to robots. Recently there has been increasing interest in upgrading robot intelligence by using multiple sensors as shown in Fig.3. [104,106]. In the figure, for a specific task, cameras provide locational information about the objects. The colour camera helps selecting a particular obje ...
PDF
... cannot actually influence it. Indeed, although RL is primarily concerned with situations in which action selection is germane, such predictions play a major role in assessing the effects of different actions, and thereby in optimizing policies. In Pavlovian conditioning, the predictions also lead to ...
... cannot actually influence it. Indeed, although RL is primarily concerned with situations in which action selection is germane, such predictions play a major role in assessing the effects of different actions, and thereby in optimizing policies. In Pavlovian conditioning, the predictions also lead to ...
Chemical Analogies: Two Kinds of Explanation
... with weights equal to a parameter i are formed to connect all units that represent alternative mappings for the same element. In addition to the units representing mapping hypotheses, the network includes two special units. The semantic unit is used to convey information about the system's prior as ...
... with weights equal to a parameter i are formed to connect all units that represent alternative mappings for the same element. In addition to the units representing mapping hypotheses, the network includes two special units. The semantic unit is used to convey information about the system's prior as ...
Document
... – Concept formation (e.g., what are patterns of genomic instability as measured by array CGH that constitute molecular subtypes of lung cancer capable of guiding development of new treatments?); – Feature construction (e.g., how can mass-spectrometry signals be decomposed into individual variables t ...
... – Concept formation (e.g., what are patterns of genomic instability as measured by array CGH that constitute molecular subtypes of lung cancer capable of guiding development of new treatments?); – Feature construction (e.g., how can mass-spectrometry signals be decomposed into individual variables t ...
Maximum likelihood bounded tree-width Markov networks
... both hardness results and provable guarantees of approximation accuracy. In 1968, Chow and Liu [6] provided a rigorous analysis for finding maximum likelihood Markov trees (distributions in which the dependencies follow a tree structure, with only pairwise direct dependencies and no loops), casting ...
... both hardness results and provable guarantees of approximation accuracy. In 1968, Chow and Liu [6] provided a rigorous analysis for finding maximum likelihood Markov trees (distributions in which the dependencies follow a tree structure, with only pairwise direct dependencies and no loops), casting ...
Learning in multi-agent systems
... On-line (or incremental) learning algorithms, such as backpropagating neural networks or (in some way) reinforcement learning, have been used to compute new hypotheses incrementally as soon as a new training example becomes available. On the other hand, off-line learning methods induce a hypothesis ...
... On-line (or incremental) learning algorithms, such as backpropagating neural networks or (in some way) reinforcement learning, have been used to compute new hypotheses incrementally as soon as a new training example becomes available. On the other hand, off-line learning methods induce a hypothesis ...
Visual pathway class..
... Ventral Pathway: Summary • Receives input from both M and P subcortical pathways, but mostly P inputs. • Progressive increase in the complexity of spatial selectivity. Neurons are ultimately responsive to holistic objects in IT. • IT neurons also exhibit well-known scale, shift, rotation invariance ...
... Ventral Pathway: Summary • Receives input from both M and P subcortical pathways, but mostly P inputs. • Progressive increase in the complexity of spatial selectivity. Neurons are ultimately responsive to holistic objects in IT. • IT neurons also exhibit well-known scale, shift, rotation invariance ...
Associative memory properties of multiple cortical modules
... in the superior temporal sulcus are activated by the sight of the lips moving, and neuronal responses to auditory stimuli have been found in the same region (Baylis et al 1987, Hasselmo et al 1989). The architecture in figure 1 may also be relevant to several different situations apart from the ones ...
... in the superior temporal sulcus are activated by the sight of the lips moving, and neuronal responses to auditory stimuli have been found in the same region (Baylis et al 1987, Hasselmo et al 1989). The architecture in figure 1 may also be relevant to several different situations apart from the ones ...
Learning Vector Representations for Sentences
... 2.4 The role of the hidden layer in a two-layer feed-forward neural network is to project the data onto another vector space in which they are now linearly separable. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.5 A four-layer neural network for face recognition. Higher layers (i.e. ...
... 2.4 The role of the hidden layer in a two-layer feed-forward neural network is to project the data onto another vector space in which they are now linearly separable. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.5 A four-layer neural network for face recognition. Higher layers (i.e. ...
A neural implementation of Bayesian inference based on predictive
... of free parameters, W, and references to the “synaptic weights” refer to the elements of W. Here, as in previous work with PC/BC-DIM only non-negative weights, inputs, and activations are used. Initially the values of y were all set to zero, although random initialisation of the prediction node acti ...
... of free parameters, W, and references to the “synaptic weights” refer to the elements of W. Here, as in previous work with PC/BC-DIM only non-negative weights, inputs, and activations are used. Initially the values of y were all set to zero, although random initialisation of the prediction node acti ...
A Differential Approach to Inference in Bayesian Networks Adnan
... dence e: they only contain one type of variables cor responding to network parameters, with no variables corresponding to evidence indicators. In fact, these polynomials correspond to our canonical polynomi als when evidence indicators are fixed to a particular value. Hence, they do not represent ...
... dence e: they only contain one type of variables cor responding to network parameters, with no variables corresponding to evidence indicators. In fact, these polynomials correspond to our canonical polynomi als when evidence indicators are fixed to a particular value. Hence, they do not represent ...
Coefficient of Variation (CV) vs Mean Interspike Interval (ISI) curves
... D. R. Smith and G. K. Smith, A statistical analysis of the continuous activity of single cortical neurons in the cat unanesthetized isolated forebrain, Biophys. J. 5 (1965) 47-74. ...
... D. R. Smith and G. K. Smith, A statistical analysis of the continuous activity of single cortical neurons in the cat unanesthetized isolated forebrain, Biophys. J. 5 (1965) 47-74. ...
lecture slides
... the same, but the kernel is now the co-training kernel. ● Note! The co-training kernel involves matrix inverses. It cannot be computed element-by-element separately: it can only be computed as part of a full-sized square matrix. ● This means that the kernel elements for the training samples depend o ...
... the same, but the kernel is now the co-training kernel. ● Note! The co-training kernel involves matrix inverses. It cannot be computed element-by-element separately: it can only be computed as part of a full-sized square matrix. ● This means that the kernel elements for the training samples depend o ...
A Neural Network Based Navigation for Intelligent Autonomous
... networks are the wishes to understand principles leading in some manner to the comprehension of the human brain functions and to build machines that are able to perform complex tasks requiring massively parallel computation. Neural Networks deal with cognitive tasks such as learning, adaptation gene ...
... networks are the wishes to understand principles leading in some manner to the comprehension of the human brain functions and to build machines that are able to perform complex tasks requiring massively parallel computation. Neural Networks deal with cognitive tasks such as learning, adaptation gene ...
Diagnosis of Pulmonary Embolism Using Fuzzy Inference System
... • Despite its name Fuzzy Logic is not nebulous, cloudy or vague. • It provides a very precise approach for dealing with uncertainty which is derived from complex human behavior. • Fuzzy Logic is so powerful, mainly because it does not require a deep understanding of a system or exact and precise num ...
... • Despite its name Fuzzy Logic is not nebulous, cloudy or vague. • It provides a very precise approach for dealing with uncertainty which is derived from complex human behavior. • Fuzzy Logic is so powerful, mainly because it does not require a deep understanding of a system or exact and precise num ...
Learning sensory maps with real-world stimuli in real time using a
... In the real world events do not occur in isolation but are combined in a variety of ways. In the first experiment we assess whether our model is able to develop specific and stable representations under these circumstances. The initial weights of the synapses from thalamic neurons to cortical excita ...
... In the real world events do not occur in isolation but are combined in a variety of ways. In the first experiment we assess whether our model is able to develop specific and stable representations under these circumstances. The initial weights of the synapses from thalamic neurons to cortical excita ...
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 ...
Decoding a Temporal Population Code
... of cells in the former group, given this input, is reduced while that of the latter is increased. The synapses evolve according to a simplified version of the learning rule proposed in Maass et al. (2002) and Auer, Burgsteiner, and Maass (2001), the main difference being that the clear margin term h ...
... of cells in the former group, given this input, is reduced while that of the latter is increased. The synapses evolve according to a simplified version of the learning rule proposed in Maass et al. (2002) and Auer, Burgsteiner, and Maass (2001), the main difference being that the clear margin term h ...
Catastrophic interference
Catastrophic Interference, also known as catastrophic forgetting, is the tendency of a artificial neural network to completely and abruptly forget previously learned information upon learning new information. Neural networks are an important part of the network approach and connectionist approach to cognitive science. These networks use computer simulations to try and model human behaviours, such as memory and learning. Catastrophic interference is an important issue to consider when creating connectionist models of memory. It was originally brought to the attention of the scientific community by research from McCloskey and Cohen (1989), and Ractcliff (1990). It is a radical manifestation of the ‘sensitivity-stability’ dilemma or the ‘stability-plasticity’ dilemma. Specifically, these problems refer to the issue of being able to make an artificial neural network that is sensitive to, but not disrupted by, new information. Lookup tables and connectionist networks lie on the opposite sides of the stability plasticity spectrum. The former remains completely stable in the presence of new information but lacks the ability to generalize, i.e. infer general principles, from new inputs. On the other hand, connectionst networks like the standard backpropagation network are very sensitive to new information and can generalize on new inputs. Backpropagation models can be considered good models of human memory insofar as they mirror the human ability to generalize but these networks often exhibit less stability than human memory. Notably, these backpropagation networks are susceptible to catastrophic interference. This is considered an issue when attempting to model human memory because, unlike these networks, humans typically do not show catastrophic forgetting. Thus, the issue of catastrophic interference must be eradicated from these backpropagation models in order to enhance the plausibility as models of human memory.