PDF file
... sample the entire input area as a single vector. An architecture figure for WWN-3 is shown in Fig. 1. We initialized WWN-3 to use retinal images of total size 38×38, having foregrounds sized roughly 19 × 19 placed on them, with foreground contours based on the object’s contours. V4 had 20 × 20 × 3 n ...
... sample the entire input area as a single vector. An architecture figure for WWN-3 is shown in Fig. 1. We initialized WWN-3 to use retinal images of total size 38×38, having foregrounds sized roughly 19 × 19 placed on them, with foreground contours based on the object’s contours. V4 had 20 × 20 × 3 n ...
neural models of head-direction cells
... Redish et al. (1996) modelled the relationship between ATN and PSc. Each area was represented by a CANN, consisting of an excitatory and an inhibitory layer of nodes to maintain a single hill of activation. They based this on observations that PSc represents current heading, whereas ATN represents f ...
... Redish et al. (1996) modelled the relationship between ATN and PSc. Each area was represented by a CANN, consisting of an excitatory and an inhibitory layer of nodes to maintain a single hill of activation. They based this on observations that PSc represents current heading, whereas ATN represents f ...
PDF file
... development of disparity tuning in toy objects data using an artificial neural network based on back-propagation and reinforcement learning. They reported 90% correct recognition rate for 11 classes of disparity. In Solgi & Weng 2008 [13], a multilayer in-place learning network was used to detect bi ...
... development of disparity tuning in toy objects data using an artificial neural network based on back-propagation and reinforcement learning. They reported 90% correct recognition rate for 11 classes of disparity. In Solgi & Weng 2008 [13], a multilayer in-place learning network was used to detect bi ...
Decision Sum-Product-Max Networks
... graphical models for multi-stage decision problems. IDs extend Bayesian Networks with decision and utility nodes. As the case with most probabilistic graphical models, solving IDs is NP-hard even in networks with bounded treewidth (Mauá, de Campos, and Zaffalon 2012). SumProduct Networks (SPNs) (Po ...
... graphical models for multi-stage decision problems. IDs extend Bayesian Networks with decision and utility nodes. As the case with most probabilistic graphical models, solving IDs is NP-hard even in networks with bounded treewidth (Mauá, de Campos, and Zaffalon 2012). SumProduct Networks (SPNs) (Po ...
Introduction to Artificial Neural Networks (ANNs)
... When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells, such that A’s efficiency as one of the cells firing B, is increased. Hebb Rule 4wi,j = λ oi oj Instrumental in Bi ...
... When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells, such that A’s efficiency as one of the cells firing B, is increased. Hebb Rule 4wi,j = λ oi oj Instrumental in Bi ...
Instrumental Conditioning Driven by Apparently Neutral Stimuli: A
... presentations. This correlation is not static: for instance, when an animal learns to associate a conditioned stimulus (CS) with an unconditioned stimulus (US), the DA burst starts to be triggered by the appearance of the CS instead of the US. This switch in the occurrence of DA activation is consid ...
... presentations. This correlation is not static: for instance, when an animal learns to associate a conditioned stimulus (CS) with an unconditioned stimulus (US), the DA burst starts to be triggered by the appearance of the CS instead of the US. This switch in the occurrence of DA activation is consid ...
Automatic Derivation of Substructures-1993
... Table 1: The Protein Structures used in this work. The columns contain the following information: Narne: The name of the protein as assigned by the Broolchaven database. Chains: If the protem contains multiple ch.un.s, the chain used is indicated. Residues: The number of residues in the sequence, as ...
... Table 1: The Protein Structures used in this work. The columns contain the following information: Narne: The name of the protein as assigned by the Broolchaven database. Chains: If the protem contains multiple ch.un.s, the chain used is indicated. Residues: The number of residues in the sequence, as ...
A visual processing task: Retina and V1
... stages in the brain. Our retina sends some 106 axons, with maybe some 10 bits/sec each. Yet, consciously we process much less information (estimate are about 100 bits/sec). These properties of the input are so called second order statistics, as the calculation of the correlation uses terms such as ⟨ ...
... stages in the brain. Our retina sends some 106 axons, with maybe some 10 bits/sec each. Yet, consciously we process much less information (estimate are about 100 bits/sec). These properties of the input are so called second order statistics, as the calculation of the correlation uses terms such as ⟨ ...
Neurons
... A strong stimulus can trigger more neurons to fire, and to fire more often, but all action potentials are of the ...
... A strong stimulus can trigger more neurons to fire, and to fire more often, but all action potentials are of the ...
Cortical region interactions and the functional role of apical
... and the post-synaptic activity of both dendrites. In this case, the stronger the apical input, the more the basal weights are modified. Furthermore, weak apical input can change the sign of learning and cause the node to move its receptive field away from the current stimulus. The same learning rul ...
... and the post-synaptic activity of both dendrites. In this case, the stronger the apical input, the more the basal weights are modified. Furthermore, weak apical input can change the sign of learning and cause the node to move its receptive field away from the current stimulus. The same learning rul ...
Financial time series forecasting with machine learning techniques
... Stock index prediction is an important challenge in financial time series prediction. The stock market is subject to large price volatility which translates to high risks for holders of common shares. Portfolio diversification permits the reduction of company specific risk, but the 2007/2008 financi ...
... Stock index prediction is an important challenge in financial time series prediction. The stock market is subject to large price volatility which translates to high risks for holders of common shares. Portfolio diversification permits the reduction of company specific risk, but the 2007/2008 financi ...
Prediction of pedal cyclists and pedestrian fatalities from
... reliable way of representing the non-linear relations between the inputs and outputs of a system. ANN can consider the complex relations inside the data, and it tries to generalize. A typical ANN model can be implemented for various problem types such as pattern recognition, classification, predicti ...
... reliable way of representing the non-linear relations between the inputs and outputs of a system. ANN can consider the complex relations inside the data, and it tries to generalize. A typical ANN model can be implemented for various problem types such as pattern recognition, classification, predicti ...
regional difference in stainability with calcium
... zone near the olfactory bulb were also stained. Regions with low staining scores include Ammon’s horn CA1–CA3 pyramidal cell layer, the basolateral amygdala, and the caudate putamen. The CA1–CA3 pyramidal cell layer, except for CA3c, exhibited the worst stainability, and often, no cells were stained ...
... zone near the olfactory bulb were also stained. Regions with low staining scores include Ammon’s horn CA1–CA3 pyramidal cell layer, the basolateral amygdala, and the caudate putamen. The CA1–CA3 pyramidal cell layer, except for CA3c, exhibited the worst stainability, and often, no cells were stained ...
Artificial Brain Organization
... Uses distributed processing in sparsely connected neurons Uses spatio-temporal associative learning Uses feedback for input prediction and screening input information for novelty Develops an internal value system to evaluate its state in environment using reinforcement learning Plans output actions ...
... Uses distributed processing in sparsely connected neurons Uses spatio-temporal associative learning Uses feedback for input prediction and screening input information for novelty Develops an internal value system to evaluate its state in environment using reinforcement learning Plans output actions ...
Lecture 22 clustering (3)
... • Biological motivations: Different regions of a brain (cerebral cortex) seem to tune into different tasks. Particular location of the neural response of the "map" often directly corresponds to specific modality and quality of sensory signal. • SOM is an unsupervised clustering algorithm which creat ...
... • Biological motivations: Different regions of a brain (cerebral cortex) seem to tune into different tasks. Particular location of the neural response of the "map" often directly corresponds to specific modality and quality of sensory signal. • SOM is an unsupervised clustering algorithm which creat ...
The History of Artificial Intelligence
... • Artificial Intelligence has come a long way from its early roots, driven by dedicated researchers. The beginnings of AI reach back before electronics, to philosophers and mathematicians such as Boole and others theorizing on principles that were used as the foundation of AI Logic. AI really began ...
... • Artificial Intelligence has come a long way from its early roots, driven by dedicated researchers. The beginnings of AI reach back before electronics, to philosophers and mathematicians such as Boole and others theorizing on principles that were used as the foundation of AI Logic. AI really began ...
An Application of Ant Colony Optimization to Image Clustering
... are based on specific and different visual cues, representing various aspects of the content. The aim for the machine is to learn associations between complex combinations of lowlevel features and semantic concepts. In the literature, the problem of the selection of important features is solved by f ...
... are based on specific and different visual cues, representing various aspects of the content. The aim for the machine is to learn associations between complex combinations of lowlevel features and semantic concepts. In the literature, the problem of the selection of important features is solved by f ...
The Neural Basis of the Object Concept in Ambiguous and
... For the first series of experiments two feature dimensions were used: color and orientation. In order to investigate the binding capabilities of the network, two types of stimuli were tested (Fig. 3a–d). The first contained a horizontal and a vertical bar that overlap in the center. When both bars s ...
... For the first series of experiments two feature dimensions were used: color and orientation. In order to investigate the binding capabilities of the network, two types of stimuli were tested (Fig. 3a–d). The first contained a horizontal and a vertical bar that overlap in the center. When both bars s ...
Introduction - Knowledge Based Systems Group
... When the architecture is totally defined, the two highest layers will correspond to the bot’s brain. The Formatting Data Layer is actually the only layer that depends on the flight simulator specificities. It does not include any sort of intelligence, whereas the two highest layers deal with artific ...
... When the architecture is totally defined, the two highest layers will correspond to the bot’s brain. The Formatting Data Layer is actually the only layer that depends on the flight simulator specificities. It does not include any sort of intelligence, whereas the two highest layers deal with artific ...
CSE 5290: Artificial Intelligence
... are sometimes called NP complete problems or for which an algorithmic solution is too complex to be found) and are often well suited to problems that people are good at solving, but for which traditional methods are not. ...
... are sometimes called NP complete problems or for which an algorithmic solution is too complex to be found) and are often well suited to problems that people are good at solving, but for which traditional methods are not. ...
A Computational Model of Human Planning in the Traveling Salesman... Simone Cutini () Andrea Di Ferdinando ()
... map is pre-activated by the corresponding unit of the saliency map. The Nearest Neighbor (NN) is implemented as a Gaussian-shaped hill of activity centered on the last visited city, whereas Direction Right (DR) and Direction Down (DD) saliency maps consist of linear gradients that cover the entire v ...
... map is pre-activated by the corresponding unit of the saliency map. The Nearest Neighbor (NN) is implemented as a Gaussian-shaped hill of activity centered on the last visited city, whereas Direction Right (DR) and Direction Down (DD) saliency maps consist of linear gradients that cover the entire v ...
Spatial Spread Measurements in HR 90K users
... • Ideally, in multi-channel cochlear implants (CI), each electrode should selectively activate a distinct group of neurons in the cochlea. • In reality, the same neurons are stimulated by multiple electrodes because the injected current spreads through the cochlear tissue. If two or more electrodes ...
... • Ideally, in multi-channel cochlear implants (CI), each electrode should selectively activate a distinct group of neurons in the cochlea. • In reality, the same neurons are stimulated by multiple electrodes because the injected current spreads through the cochlear tissue. If two or more electrodes ...
Evolutionary Connectionism and Mind/Brain Modularity - laral
... properties of the neural network are hardwired by the researcher in the neural network but it is necessary to actually simulate the evolutionary process that results in these genetically inherited properties or constraints. Artificial Life simulations differ from the usual connectionist simulations ...
... properties of the neural network are hardwired by the researcher in the neural network but it is necessary to actually simulate the evolutionary process that results in these genetically inherited properties or constraints. Artificial Life simulations differ from the usual connectionist simulations ...
Evolutionary Connectionism and Mind/Brain Modularity - laral
... Computational cognitive science tends to be strongly modularistic. The computational mind is made up of distinct modules which specialize in processing distinct types of information, have specialized functions, and are closed to interference from other types of information and functions (Chomsky, 19 ...
... Computational cognitive science tends to be strongly modularistic. The computational mind is made up of distinct modules which specialize in processing distinct types of information, have specialized functions, and are closed to interference from other types of information and functions (Chomsky, 19 ...
2320lecture22
... Neural Correlates of Selection • Results: Neurons in visual system respond vigorously to certain stimuli but are then sharply suppressed if a different stimulus is selected by attention • Interpretation: this selection might be a neural correlate of the perceptual suppression of unattended informat ...
... Neural Correlates of Selection • Results: Neurons in visual system respond vigorously to certain stimuli but are then sharply suppressed if a different stimulus is selected by attention • Interpretation: this selection might be a neural correlate of the perceptual suppression of unattended informat ...