Structure-function relationship in hierarchical model of brain networks
... the dynamics of the network, using generic oscillators (periodic or chaotic) as the nodes of typical network models like small-world and scale-free networks, globally or sparsely connected with random architectures [15, 23, 70]. The ability of the network to achieve rather idealized complete synchro ...
... the dynamics of the network, using generic oscillators (periodic or chaotic) as the nodes of typical network models like small-world and scale-free networks, globally or sparsely connected with random architectures [15, 23, 70]. The ability of the network to achieve rather idealized complete synchro ...
Classification using sparse representations
... is equal to the transpose of the V and has each row normalised to sum to one. Hence, the feedforward and feedback weights are simply rescaled versions of each other. Initially the values of y are all set to zero, although random initialisation of the prediction node activations can also be used with ...
... is equal to the transpose of the V and has each row normalised to sum to one. Hence, the feedforward and feedback weights are simply rescaled versions of each other. Initially the values of y are all set to zero, although random initialisation of the prediction node activations can also be used with ...
Neural realisation of the SP theory
... the system is exactly the same as before except that row 0 contains the encoded pattern and each symbol in that pattern is aligned with matching symbols in the rows below. The original sentence has, in effect, been recreated because the alignment contains the words of the sentence in their correct o ...
... the system is exactly the same as before except that row 0 contains the encoded pattern and each symbol in that pattern is aligned with matching symbols in the rows below. The original sentence has, in effect, been recreated because the alignment contains the words of the sentence in their correct o ...
One Computer Scientist`s (Deep) Superior Colliculus
... I am modeling the superior colliculus, a structure in the vertebrate midbrain which receives and integrates multi-sensory information to localize objects and events in the world and generate orienting actions. To model the superior colliculus, I use artificial neural networks. The goal is to provide ...
... I am modeling the superior colliculus, a structure in the vertebrate midbrain which receives and integrates multi-sensory information to localize objects and events in the world and generate orienting actions. To model the superior colliculus, I use artificial neural networks. The goal is to provide ...
A distributed problem-solving approach to rule induction
... systems. It can be in the form of data exchange, knowledge transfer, or heuristics migration, where the learning mechanisms involved are relatively simple. It can also be done by extending machine learning techniques developed for single-agent systems, such as explanation-based learning, case-based ...
... systems. It can be in the form of data exchange, knowledge transfer, or heuristics migration, where the learning mechanisms involved are relatively simple. It can also be done by extending machine learning techniques developed for single-agent systems, such as explanation-based learning, case-based ...
discrete variational autoencoders
... Unsupervised learning of probabilistic models is a powerful technique, facilitating tasks such as denoising and inpainting, and regularizing supervised tasks such as classification (Hinton et al., 2006; Salakhutdinov & Hinton, 2009; Rasmus et al., 2015). Many datasets of practical interest are proje ...
... Unsupervised learning of probabilistic models is a powerful technique, facilitating tasks such as denoising and inpainting, and regularizing supervised tasks such as classification (Hinton et al., 2006; Salakhutdinov & Hinton, 2009; Rasmus et al., 2015). Many datasets of practical interest are proje ...
Funkcionisanje AMRES veza prema Internetu u proteklom i
... cflow - caida’s netflow collector and visualization perl tool adapted to support additional requests: more configurable options – traffic patters ! dynamic time frame based on RRD graphs for subnets, protocols and services simple web interface for row data dump ...
... cflow - caida’s netflow collector and visualization perl tool adapted to support additional requests: more configurable options – traffic patters ! dynamic time frame based on RRD graphs for subnets, protocols and services simple web interface for row data dump ...
Meinongian Semantics and Artificial Intelligence
... As William A. Woods (1975: 44) puts it, The major characteristic of the semantic networks that distinguishes them from other candidates [for knowledge representation systems] is the characteristic notion of a link or pointer [an arc, in the above terminology] which connects individual facts into a t ...
... As William A. Woods (1975: 44) puts it, The major characteristic of the semantic networks that distinguishes them from other candidates [for knowledge representation systems] is the characteristic notion of a link or pointer [an arc, in the above terminology] which connects individual facts into a t ...
The Control of Rate and Timing of Spikes in the Deep Cerebellar
... An intermediate level of input synchronization consisted of 100 groups of 4 synchronized elements. In the condition without input synchronization, all 400 input elements were activated independently. A second parameter that we manipulated was the total amplitude of Gin plus Gex by multiplying both G ...
... An intermediate level of input synchronization consisted of 100 groups of 4 synchronized elements. In the condition without input synchronization, all 400 input elements were activated independently. A second parameter that we manipulated was the total amplitude of Gin plus Gex by multiplying both G ...
Statistical learning as a domain-general mechanism of entrenchment
... the foundation for future research investigating the close relationship between frequent cooccurrence and the strength and automaticity of recall at various levels of linguistic analysis. From the beginning, research on implicit learning related to language was focused on the way(s) in which units o ...
... the foundation for future research investigating the close relationship between frequent cooccurrence and the strength and automaticity of recall at various levels of linguistic analysis. From the beginning, research on implicit learning related to language was focused on the way(s) in which units o ...
the resonate-and-fire neuron: time dependent and frequency
... This Masters Thesis is brought to you for free and open access by the Student Theses at Bucknell Digital Commons. It has been accepted for inclusion in Master’s Theses by an authorized administrator of Bucknell Digital Commons. For more information, please contact [email protected]. ...
... This Masters Thesis is brought to you for free and open access by the Student Theses at Bucknell Digital Commons. It has been accepted for inclusion in Master’s Theses by an authorized administrator of Bucknell Digital Commons. For more information, please contact [email protected]. ...
all BAMI book sections in pdf
... the brain, but for the foreseeable future it is easiest to understand these principles in the context of the brain because the brain continues to offer suggestions and constraints on the solutions to many open issues. This approach to machine intelligence is different than that taken by classic AI a ...
... the brain, but for the foreseeable future it is easiest to understand these principles in the context of the brain because the brain continues to offer suggestions and constraints on the solutions to many open issues. This approach to machine intelligence is different than that taken by classic AI a ...
Learning to classify complex patterns using a VLSI network of
... The learning and classification of natural stimuli are accomplished by biological organisms with remarkable ease, even when the input is noisy or incomplete. Such real-time classification of complex patterns of spike trains is a difficult computational problem that artificial neural networks are con ...
... The learning and classification of natural stimuli are accomplished by biological organisms with remarkable ease, even when the input is noisy or incomplete. Such real-time classification of complex patterns of spike trains is a difficult computational problem that artificial neural networks are con ...
An Action Selection Calculus
... All argue that the majority of observed and apparently intelligent behavior may be ascribed to an innate, pre-programmed, stimulus response mechanism available to the individual. Innate intelligence is not, however, defined by degree. Complex, essentially reactive, models have been developed to comp ...
... All argue that the majority of observed and apparently intelligent behavior may be ascribed to an innate, pre-programmed, stimulus response mechanism available to the individual. Innate intelligence is not, however, defined by degree. Complex, essentially reactive, models have been developed to comp ...
The role of spiking nonlinearity in contrast gain control
... & Meister, 1997), and network interactions (Victor, 1987). It was also suggested that the active ionic channels inside the spiking generation (Kim & Rieke, 2001; Sanchez-Vives et al., 2000) might play an important role in controlling the changing of the transfer function. In order to clarify the con ...
... & Meister, 1997), and network interactions (Victor, 1987). It was also suggested that the active ionic channels inside the spiking generation (Kim & Rieke, 2001; Sanchez-Vives et al., 2000) might play an important role in controlling the changing of the transfer function. In order to clarify the con ...
Strong items get suppressed, weak items do not: The role of item
... fact that, according to strength dependence, both strong and weak items should suffer from retrieval-induced impairment. Indeed, since the recall of items is assumed to increase these items’ associations to the common cue, the likelihood of recall of the still-to-be-remembered items should be relati ...
... fact that, according to strength dependence, both strong and weak items should suffer from retrieval-induced impairment. Indeed, since the recall of items is assumed to increase these items’ associations to the common cue, the likelihood of recall of the still-to-be-remembered items should be relati ...
The Sensitivity of Belief Networks to Imprecise Probabilities: An
... will be of little practical value. But, if a BN's performance turns out to be insensitive to probable errors, we can allay concerns about the reliability of subjective probability assessments. We performed two experiments to examine the sensitivity of BNs to the expert probabilities. In each experim ...
... will be of little practical value. But, if a BN's performance turns out to be insensitive to probable errors, we can allay concerns about the reliability of subjective probability assessments. We performed two experiments to examine the sensitivity of BNs to the expert probabilities. In each experim ...
Representation, Computation, and Observer
... it, this is the view that “a necessary condition on any physical process counting as a computation is that it possesses representational content” [1, p. 260]. He clarifies that what he has in mind is a very minimal form of representation, involving no more than “a basic notion of aboutness or refere ...
... it, this is the view that “a necessary condition on any physical process counting as a computation is that it possesses representational content” [1, p. 260]. He clarifies that what he has in mind is a very minimal form of representation, involving no more than “a basic notion of aboutness or refere ...
Neurodynamical modeling of arbitrary visuomotor tasks
... the behavioral data of visuomotor experiments. To our knowledge, this is the first cross-study model in visuomotor mappings, which tries to address two or more experiments at the same time. We compare evidence from experiments conducted by two groups and show that they are inconsistent. In addition, ...
... the behavioral data of visuomotor experiments. To our knowledge, this is the first cross-study model in visuomotor mappings, which tries to address two or more experiments at the same time. We compare evidence from experiments conducted by two groups and show that they are inconsistent. In addition, ...
A computational account for the ontogeny of mirror neurons via
... Here, α is the learning rate, which determines how quickly the synaptic weights change. The brackets h and i denote that ∆wj,i is the average of all synaptic changes over a particular time. This articulates the idea that synaptic modification is a slow process. The rationale behind the formula is th ...
... Here, α is the learning rate, which determines how quickly the synaptic weights change. The brackets h and i denote that ∆wj,i is the average of all synaptic changes over a particular time. This articulates the idea that synaptic modification is a slow process. The rationale behind the formula is th ...
Dynamical Properties of Neuronal Systems with
... patterns of this multiscale architecture and thus, it has been increasingly applied in neuroscientific research [196, 247]. Advancing neuroimaging techniques make it possible to map the brain’s wiring structure with increasing detail. Ultimately, this information could be organized into a database co ...
... patterns of this multiscale architecture and thus, it has been increasingly applied in neuroscientific research [196, 247]. Advancing neuroimaging techniques make it possible to map the brain’s wiring structure with increasing detail. Ultimately, this information could be organized into a database co ...
Preference Learning: An Introduction
... a while in fields such as economic decision theory [37]. Needless to say, however, the acquisition of preferences is not always an easy task. Therefore, not only are modeling languages and representation formalisms needed, but also methods for the automatic learning, discovery and adaptation of pref ...
... a while in fields such as economic decision theory [37]. Needless to say, however, the acquisition of preferences is not always an easy task. Therefore, not only are modeling languages and representation formalisms needed, but also methods for the automatic learning, discovery and adaptation of pref ...
Bayesian Network Classifiers
... share the same structure G, and if Θ satisfies Equation 5, then LL(B|D) ≥ LL(B 0 |D). Thus, given a network structure, there is a closed form solution for the parameters that maximize the log likelihood score, namely, Equation 5. Moreover, since the first term of Equation 2 does not depend on the ch ...
... share the same structure G, and if Θ satisfies Equation 5, then LL(B|D) ≥ LL(B 0 |D). Thus, given a network structure, there is a closed form solution for the parameters that maximize the log likelihood score, namely, Equation 5. Moreover, since the first term of Equation 2 does not depend on the ch ...
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.