Second cause of hidden hearing loss identified
... When the ear is exposed to loud noises over time, synapses connecting hair cells with the neurons in Patients who complain they can't hear their friends the inner ear are lost. This loss of synapses has at a noisy restaurant, but pass a hearing test in previously been shown as a mechanism leading to ...
... When the ear is exposed to loud noises over time, synapses connecting hair cells with the neurons in Patients who complain they can't hear their friends the inner ear are lost. This loss of synapses has at a noisy restaurant, but pass a hearing test in previously been shown as a mechanism leading to ...
![arXiv:1604.00289v3 [cs.AI] 2 Nov 2016](http://s1.studyres.com/store/data/003558001_1-ebbf1f5b4b7daca920a4ae5f6fdbbe61-300x300.png)
arXiv:1604.00289v3 [cs.AI] 2 Nov 2016
... These accomplishments have helped neural networks regain their status as a leading paradigm in machine learning, much as they were in the late 1980s and early 1990s. The recent success of neural networks has captured attention beyond academia. In industry, companies such as Google and Facebook have ...
... These accomplishments have helped neural networks regain their status as a leading paradigm in machine learning, much as they were in the late 1980s and early 1990s. The recent success of neural networks has captured attention beyond academia. In industry, companies such as Google and Facebook have ...
Building Machines That Learn and Think Like People
... input layer that presents the data (e.g, an image), hidden layers that transform the data into intermediate representations, and an output layer that produces a response (e.g., a label or an action). Recurrent connections are also popular when processing sequential data. Deep learning: A neural netw ...
... input layer that presents the data (e.g, an image), hidden layers that transform the data into intermediate representations, and an output layer that produces a response (e.g., a label or an action). Recurrent connections are also popular when processing sequential data. Deep learning: A neural netw ...
disparity detection from stereo
... connections that carry supervisory disparity information (e.g., when a monkey reaches an apple) enable neurons to self-organize according to not only bottom-up input, but also supervised disparity information. Consequently, the neurons that are tuned to similar disparities are grouped in nearby area ...
... connections that carry supervisory disparity information (e.g., when a monkey reaches an apple) enable neurons to self-organize according to not only bottom-up input, but also supervised disparity information. Consequently, the neurons that are tuned to similar disparities are grouped in nearby area ...
A. Azzini "A New Genetic Approach for Neural Network Design and
... Moreover, several parameters of an ANN can affect, during the design, how easy a solution is to find. Some of these parameters are related to the architecture design of the neural network, concerning the number of layers and nodes, and the connection weights. Some others consider the selection of da ...
... Moreover, several parameters of an ANN can affect, during the design, how easy a solution is to find. Some of these parameters are related to the architecture design of the neural network, concerning the number of layers and nodes, and the connection weights. Some others consider the selection of da ...
A First Study of Fuzzy Cognitive Maps Learning Using Particle
... been used. This function is selected since the values Ai of the concepts, by definition, must lie within [0, 1]. The interaction of the FCM results after a few iterations in a steady state, i.e. the values of the concepts are not modified further. Desired values of the output concepts of the FCM gua ...
... been used. This function is selected since the values Ai of the concepts, by definition, must lie within [0, 1]. The interaction of the FCM results after a few iterations in a steady state, i.e. the values of the concepts are not modified further. Desired values of the output concepts of the FCM gua ...
Integrate-and-Fire Neurons and Networks
... neighbors which is easily recognizable by an external observer as a travelling wave of activity. Let us now keep the connections between the same neurons as before but move all neurons to a new random location on the two-dimensional sheet. Apart from the fact that connection lines are longer, nothin ...
... neighbors which is easily recognizable by an external observer as a travelling wave of activity. Let us now keep the connections between the same neurons as before but move all neurons to a new random location on the two-dimensional sheet. Apart from the fact that connection lines are longer, nothin ...
TagSpace: Semantic Embeddings from Hashtags
... consider, with the notable exception of Ding et al. (2012), which uses an unsupervised method. As mentioned in Section 1, many approaches learn unsupervised word embeddings. In our experiments we use word2vec (Mikolov et al., 2013) as a representative scalable model for unsupervised embeddings. W SA ...
... consider, with the notable exception of Ding et al. (2012), which uses an unsupervised method. As mentioned in Section 1, many approaches learn unsupervised word embeddings. In our experiments we use word2vec (Mikolov et al., 2013) as a representative scalable model for unsupervised embeddings. W SA ...
ling411-13-FunctionalWebs - OWL-Space
... “…activation of the web, so to speak, completes itself as a result of the strong web-internal links. If the web of neurons is considered a memory representation of an object and each neuron to represent one particular feature of this object memory, the full ignition would be the neuronal correlate o ...
... “…activation of the web, so to speak, completes itself as a result of the strong web-internal links. If the web of neurons is considered a memory representation of an object and each neuron to represent one particular feature of this object memory, the full ignition would be the neuronal correlate o ...
Editing Statistical Records by Neural Networks
... The learning functions of the BP algorithm for the two-layer model are generalizations of the single-layer functions described above. They can be presented as ? [j? = o[j?*(1-o[j?)*(t[j?-o[j?) for adjusting the second matrix. and ? [j? = o[j?*(1-o[j?)*?? ?w[j,k?*? [k? for adjusting the first matrix. ...
... The learning functions of the BP algorithm for the two-layer model are generalizations of the single-layer functions described above. They can be presented as ? [j? = o[j?*(1-o[j?)*(t[j?-o[j?) for adjusting the second matrix. and ? [j? = o[j?*(1-o[j?)*?? ?w[j,k?*? [k? for adjusting the first matrix. ...
Text S1.
... An 8 by 8 grid of electrodes with 333 µm inter-electrode spacing was included. The inter-electrode spacing, which was larger than the inter-electrode spacing of 200 µm in MEAs, was selected so that the distance from each peripheral electrode to the edge of the network were also the inter-electrode s ...
... An 8 by 8 grid of electrodes with 333 µm inter-electrode spacing was included. The inter-electrode spacing, which was larger than the inter-electrode spacing of 200 µm in MEAs, was selected so that the distance from each peripheral electrode to the edge of the network were also the inter-electrode s ...
PDF file
... takes place inside an internal hidden area and, thus, their states are hidden from the external world, not directly observable and interactively teachable by the external world. This results in their lack of effective mechanism for state equivalence and skill transfer (discussed below). The theory h ...
... takes place inside an internal hidden area and, thus, their states are hidden from the external world, not directly observable and interactively teachable by the external world. This results in their lack of effective mechanism for state equivalence and skill transfer (discussed below). The theory h ...
Uncertainty Handling for Sensor Location Estimation in Wireless
... than the range-based ones, they are more economical and provide simpler estimates. In a range-free proximity-based localization algorithm, introduced by Bulusu et al., the anchor nodes broadcast their positions within the network and each sensor node computes its position as a centroid of the positi ...
... than the range-based ones, they are more economical and provide simpler estimates. In a range-free proximity-based localization algorithm, introduced by Bulusu et al., the anchor nodes broadcast their positions within the network and each sensor node computes its position as a centroid of the positi ...
The Basal Ganglia and Chunking of Action Repertoires
... (MI) hand cortex represention overlap with one another, whereas they do not overlap with input from cortex of the contralateral MI hand representations (Flaherty & Graybiel, 1993). We have suggested that systems of input modules (called matrisomes) could serve as templates for forming new associatio ...
... (MI) hand cortex represention overlap with one another, whereas they do not overlap with input from cortex of the contralateral MI hand representations (Flaherty & Graybiel, 1993). We have suggested that systems of input modules (called matrisomes) could serve as templates for forming new associatio ...
On Theoretical Properties of Sum
... weights: SPNs have been defined to have nonnegative parameters (sum weights), where most work so far has additionally assumed locally normalized parameters, i.e. that for each sum node the associated weights sum up to 1. This assures that the SPN distribution is readily correctly normalized. Up to n ...
... weights: SPNs have been defined to have nonnegative parameters (sum weights), where most work so far has additionally assumed locally normalized parameters, i.e. that for each sum node the associated weights sum up to 1. This assures that the SPN distribution is readily correctly normalized. Up to n ...
A Computational Intelligence Approach to Modelling Interstate Conflict
... failure of statistical methods might be attributed to the fact that the interstate variables related to MID are non-linear, highly interdependent and context dependent. This means conflict modelling requires more suitable techniques. Neural networks, particularly multilayer perceptrons (MLPs), have ...
... failure of statistical methods might be attributed to the fact that the interstate variables related to MID are non-linear, highly interdependent and context dependent. This means conflict modelling requires more suitable techniques. Neural networks, particularly multilayer perceptrons (MLPs), have ...
Stable propagation of synchronous spiking in cortical neural networks
... target neuron (Fig. 1a). These transients, in turn, result in well timed response spikes in target neurons. Neurons that share a large enough pool of simultaneously discharging input cells tend to align their action potentials15 (Fig. 1b). By repeating this arrangement, a group of neurons can reprod ...
... target neuron (Fig. 1a). These transients, in turn, result in well timed response spikes in target neurons. Neurons that share a large enough pool of simultaneously discharging input cells tend to align their action potentials15 (Fig. 1b). By repeating this arrangement, a group of neurons can reprod ...
Titel van de presentatie - Faculteit der Sociale Wetenschappen
... points for which the classification accuracy was above a given statistical threshold define “Reactivations” as patterns producing correct classifier outputs at reactivation times ...
... points for which the classification accuracy was above a given statistical threshold define “Reactivations” as patterns producing correct classifier outputs at reactivation times ...
Hardware: Input, Processing, and Output Devices
... Build models of the real world Use models to make predictions Genetic Algorithms: Typically uses an existing model (Fitness Function) Searches for a good (or optimal) solution to the model. ...
... Build models of the real world Use models to make predictions Genetic Algorithms: Typically uses an existing model (Fitness Function) Searches for a good (or optimal) solution to the model. ...
The 25 International Joint Conference on Artificial Intelligence
... link diagram grows or otherwise changes, the set of relevant entities and relationships change, which affects the space of information that S TRIDER will decide is relevant enough to present to the analyst. • S TRIDER influences analyst decisions by presenting relevant information. S TRIDER may ther ...
... link diagram grows or otherwise changes, the set of relevant entities and relationships change, which affects the space of information that S TRIDER will decide is relevant enough to present to the analyst. • S TRIDER influences analyst decisions by presenting relevant information. S TRIDER may ther ...
A"computational"approach"towards"the"ontogeny"of" mirror"neurons
... learning could be responsible for the ontogeny of predictive mirror neurons (Keysers and Gazzola, 2014). Here, we have shown that a variation of Oja’s rule (an implementation of Hebbian learning) is sufficient to explain the emergence of mirror neurons. An artificial neural network that simulates th ...
... learning could be responsible for the ontogeny of predictive mirror neurons (Keysers and Gazzola, 2014). Here, we have shown that a variation of Oja’s rule (an implementation of Hebbian learning) is sufficient to explain the emergence of mirror neurons. An artificial neural network that simulates th ...
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.