Cell Assembly Sequences Arising from Spike
... period before making a choice (left or right) for the next run through a T maze. Action potentials for pyramidal cells were recorded together with local field potentials. The pattern of sequential activation of simultaneously recorded neurons for a given trial type (Fig. 1 A, B) was reliable across ...
... period before making a choice (left or right) for the next run through a T maze. Action potentials for pyramidal cells were recorded together with local field potentials. The pattern of sequential activation of simultaneously recorded neurons for a given trial type (Fig. 1 A, B) was reliable across ...
High-Level Information Fusion with Bayesian - CEUR
... Most languages for expressing ontologies, such as the most popular variant of the W3C Recommendation OWL [5], are based on Description Logic [6]. Other ontology languages such as KIF2 and the ISO Standard Common Logic3 , are based on first-order logic. Classical logic has no standard means to repres ...
... Most languages for expressing ontologies, such as the most popular variant of the W3C Recommendation OWL [5], are based on Description Logic [6]. Other ontology languages such as KIF2 and the ISO Standard Common Logic3 , are based on first-order logic. Classical logic has no standard means to repres ...
Prediction of lower extremities` movement by angle–angle diagrams
... the value of human movement. As an approach to selecting data for training artificial neural network learning, we chose the calculated values of angles in the joints. With each presentation the output of the neural network was compared to the desired output and an error was computed. This error was ...
... the value of human movement. As an approach to selecting data for training artificial neural network learning, we chose the calculated values of angles in the joints. With each presentation the output of the neural network was compared to the desired output and an error was computed. This error was ...
A Computer Simulation of Olfactory Cortex with Functional
... Synaptic Properties and Modification Rules. In the model, each synaptic connection has an associated weight which determines the peak amplitude of the conductance change induced in the postsynaptic cell following presynaptic activity [2.0]. To study learning in the model, synaptic weights associated ...
... Synaptic Properties and Modification Rules. In the model, each synaptic connection has an associated weight which determines the peak amplitude of the conductance change induced in the postsynaptic cell following presynaptic activity [2.0]. To study learning in the model, synaptic weights associated ...
Design of A Fuzzy Expert System And A Multi
... training raw data in a learning process and they use inductive reasoning for inference. Considering that the knowledge is clear for expert systems, it is also easy evaluation and validation. But the knowledge for neural network is not clear, so it would be difficult for the user to understand and ch ...
... training raw data in a learning process and they use inductive reasoning for inference. Considering that the knowledge is clear for expert systems, it is also easy evaluation and validation. But the knowledge for neural network is not clear, so it would be difficult for the user to understand and ch ...
Modelling fast stimulus-response association learning along the
... and the second driven by actual or possibly mentally simulated practice. The first uses a network of PFC, PM and PPC areas (Ruge and Wolfensteller, 2009; Cole et al, 2010; Brass et al., 2009). The second seems to involve additional more rostral prefrontal areas (Cole et al., 2010), early visual area ...
... and the second driven by actual or possibly mentally simulated practice. The first uses a network of PFC, PM and PPC areas (Ruge and Wolfensteller, 2009; Cole et al, 2010; Brass et al., 2009). The second seems to involve additional more rostral prefrontal areas (Cole et al., 2010), early visual area ...
Introduction - The MIT Press
... are being successfully integrated with powerful performance systems; and practical applications based on the more established techniques have already made their presence felt . Recent successes in machine learning include decision tree induction applied to industrial process control (based on Quinla ...
... are being successfully integrated with powerful performance systems; and practical applications based on the more established techniques have already made their presence felt . Recent successes in machine learning include decision tree induction applied to industrial process control (based on Quinla ...
slides
... Dialogue Agent is trained on conversation sets Each conversation set is one “context unit” (CU) Agent database contains many CUs But not all of them have to be processed at all times Some of them could be deactivated when not needed (forgetting) and reactivated again when needed (recalling) ...
... Dialogue Agent is trained on conversation sets Each conversation set is one “context unit” (CU) Agent database contains many CUs But not all of them have to be processed at all times Some of them could be deactivated when not needed (forgetting) and reactivated again when needed (recalling) ...
4-CPG1
... can generate a stereotyped rhythmic movement without sensory afference or somatic feedback. • It can be activated/sustained by a triggering stimulus (either tonic or phasic), but requires no modulation of the input to generate the basic pattern. • Lundberg and Grillner had a nasty argument on whethe ...
... can generate a stereotyped rhythmic movement without sensory afference or somatic feedback. • It can be activated/sustained by a triggering stimulus (either tonic or phasic), but requires no modulation of the input to generate the basic pattern. • Lundberg and Grillner had a nasty argument on whethe ...
Flexible sequence learning in a SOM model of the mirror system
... in another (see Fig. 2 for an example of two sequences). Two sets of ordinal nodes are used to learn these two sequences. Learning is achieved during manual activation of the map by clamping the relevant ordinal node to an active state and then using simple Hebbian learning to train weights between ...
... in another (see Fig. 2 for an example of two sequences). Two sets of ordinal nodes are used to learn these two sequences. Learning is achieved during manual activation of the map by clamping the relevant ordinal node to an active state and then using simple Hebbian learning to train weights between ...
Narrow versus wide tuning curves: What`s best for a population code?
... Therefore, in this case, narrow tuning curves are better, in the sense that they transmit more information about the presentation angle. Note that using a center-of-mass estimator (dashed line) to compute the MDC leads to the opposite conclusion: that wide tuning curves are better. This is a compell ...
... Therefore, in this case, narrow tuning curves are better, in the sense that they transmit more information about the presentation angle. Note that using a center-of-mass estimator (dashed line) to compute the MDC leads to the opposite conclusion: that wide tuning curves are better. This is a compell ...
Narrow versus wide tuning curves: What`s best for a population code?
... Therefore, in this case, narrow tuning curves are better, in the sense that they transmit more information about the presentation angle. Note that using a center-of-mass estimator (dashed line) to compute the MDC leads to the opposite conclusion: that wide tuning curves are better. This is a compell ...
... Therefore, in this case, narrow tuning curves are better, in the sense that they transmit more information about the presentation angle. Note that using a center-of-mass estimator (dashed line) to compute the MDC leads to the opposite conclusion: that wide tuning curves are better. This is a compell ...
The Importance of Chaos Theory in the Development of Artificial
... Neural networks are a relatively new development in computer science, having survived a brush with the exclusive-or problem while the field was still in its teens in the 1960s and recovered for a renaissance in the 1980s. Chaos is a new mathematical theory, dating back to perhaps the 1960s at the ea ...
... Neural networks are a relatively new development in computer science, having survived a brush with the exclusive-or problem while the field was still in its teens in the 1960s and recovered for a renaissance in the 1980s. Chaos is a new mathematical theory, dating back to perhaps the 1960s at the ea ...
Handwritten Gregg Shorthand Recognition
... device to move. That movement can span an infinite number of very precise motions. These networks do indeed want to smooth their inputs which, due to limitations of sensors, come in non-continuous bursts, say thirty times a second. To do that, they might accept these inputs, sum that data, and then ...
... device to move. That movement can span an infinite number of very precise motions. These networks do indeed want to smooth their inputs which, due to limitations of sensors, come in non-continuous bursts, say thirty times a second. To do that, they might accept these inputs, sum that data, and then ...
Dynamic computation in a recurrent network of heterogeneous
... of the clusters are unconstrained and have an equal likelihood of existing at any position. Therefore, clusters in a homogeneous network move in a random walk, constrained only by their interactions with nearby clusters [1]. In contrast, networks with heterogeneous neurons tend to bias the locations ...
... of the clusters are unconstrained and have an equal likelihood of existing at any position. Therefore, clusters in a homogeneous network move in a random walk, constrained only by their interactions with nearby clusters [1]. In contrast, networks with heterogeneous neurons tend to bias the locations ...
Learning with Hierarchical-Deep Models
... categories into several supercategories, sharing a similarity metric within these classes. They, however, did not attempt to address transfer learning problem, and primarily focused on large-scale image retrieval tasks. Finally, Fei-Fei et al. [11] used an HB approach, with a prior on the parameters ...
... categories into several supercategories, sharing a similarity metric within these classes. They, however, did not attempt to address transfer learning problem, and primarily focused on large-scale image retrieval tasks. Finally, Fei-Fei et al. [11] used an HB approach, with a prior on the parameters ...
Learning
... e.g., the learning of arbitrary lists of words or lists of associations experimentally probed by free recall vs. cued recall (cued is easier) ...
... e.g., the learning of arbitrary lists of words or lists of associations experimentally probed by free recall vs. cued recall (cued is easier) ...
all publications as Word document
... Symposium on Computer Science in Sports, Loughborough, UK, pp.119-127, ISBN: 978-3-319-24560-7. DOI: 10.1007/978-3-319-24560-7_15. Chung, P, Soltoggio, A, Dawson, C, Meng, Q, Pain, M (ed) (2015) Proceedings of the 10th International Symposium on Computer Science in Sports (ISCSS), Springer, ISBN: 97 ...
... Symposium on Computer Science in Sports, Loughborough, UK, pp.119-127, ISBN: 978-3-319-24560-7. DOI: 10.1007/978-3-319-24560-7_15. Chung, P, Soltoggio, A, Dawson, C, Meng, Q, Pain, M (ed) (2015) Proceedings of the 10th International Symposium on Computer Science in Sports (ISCSS), Springer, ISBN: 97 ...
Bayesian Retrieval In Associative Memories With Storage Errors
... efficient associative memory models in terms of information stored per bit of memory. However, it has not been so widely used since its performance degrades significantly if there are errors in the initial patterns presented or if there are errors in the synaptic weight matrix [12]. Both sorts of er ...
... efficient associative memory models in terms of information stored per bit of memory. However, it has not been so widely used since its performance degrades significantly if there are errors in the initial patterns presented or if there are errors in the synaptic weight matrix [12]. Both sorts of er ...
Using ADP to Understand and Replicate Brain Intelligence: the Next
... foundation of decision theory [6], risk analysis, modern investment analysis, and dynamic programming, among others. Before Von Neumann, most economists believed that (consumer) values or preferences are strictly an ordinal relationship; for example, you can prove by experiment that most people pref ...
... foundation of decision theory [6], risk analysis, modern investment analysis, and dynamic programming, among others. Before Von Neumann, most economists believed that (consumer) values or preferences are strictly an ordinal relationship; for example, you can prove by experiment that most people pref ...
MS PowerPoint format - KDD
... – Vertices (37): findings (e.g., esophageal intubation), intermediates, observables – K2: found BBN different in only 1 edge from gold standard (elicited from expert) ...
... – Vertices (37): findings (e.g., esophageal intubation), intermediates, observables – K2: found BBN different in only 1 edge from gold standard (elicited from expert) ...
Forecasting Generation Waste Using Artificial Neural Networks 1
... assess the waste generation (WG) rates; the most instrumental of them are load-count analysis, weight-volume analysis and materials-balance analysis. Nevertheless, these are the central strategies for estimating the figures of generated waste, but there are some disadvantages. For instance, the load ...
... assess the waste generation (WG) rates; the most instrumental of them are load-count analysis, weight-volume analysis and materials-balance analysis. Nevertheless, these are the central strategies for estimating the figures of generated waste, but there are some disadvantages. For instance, the load ...
Computational approaches to sensorimotor transformations
... using basis functions. Next we describe how these transformations can be learned using biologically plausible algorithms. Finally, we explain how to implement short-term spatial memory and updating of motor plans using these representations. In each case, we examine the extent to which models relyin ...
... using basis functions. Next we describe how these transformations can be learned using biologically plausible algorithms. Finally, we explain how to implement short-term spatial memory and updating of motor plans using these representations. In each case, we examine the extent to which models relyin ...
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.