The Basal Ganglia Anatomy, Physiology, etc. Overview
... result is focused facilitation and surrounding inhibition of thalamocortical and brainstem targets neurons that are involved in the generation of motor patterns. ...
... result is focused facilitation and surrounding inhibition of thalamocortical and brainstem targets neurons that are involved in the generation of motor patterns. ...
A Case for a Situationally Adaptive Many
... while some scale to larger thread counts, depending on the available parallelism. Due to lack of knowledge of scalability patterns, a naive parallelization would try to use the maximum number of threads available in the multicore processor, and hence not perform optimally. A scheduler is thus requir ...
... while some scale to larger thread counts, depending on the available parallelism. Due to lack of knowledge of scalability patterns, a naive parallelization would try to use the maximum number of threads available in the multicore processor, and hence not perform optimally. A scheduler is thus requir ...
Computing with Spiking Neuron Networks
... non-exhaustive outline, a neuron can generate an action potential – the spike – at the soma, the cell body of the neuron. This brief electric pulse (1 or 2ms duration) then travels along the neuron’s axon, that in turn is linked up to the receiving end of other neurons, the dendrites (see Figure 1, ...
... non-exhaustive outline, a neuron can generate an action potential – the spike – at the soma, the cell body of the neuron. This brief electric pulse (1 or 2ms duration) then travels along the neuron’s axon, that in turn is linked up to the receiving end of other neurons, the dendrites (see Figure 1, ...
neural_networks
... Each synaptic connection scales its axon’s value and then the postsynaptic neuron adds up these scaled values. Each postsynaptic neuron has its own distinct summation process, which converts the current set of active synapses into a scalar value termed the internal excitation or net input excitation ...
... Each synaptic connection scales its axon’s value and then the postsynaptic neuron adds up these scaled values. Each postsynaptic neuron has its own distinct summation process, which converts the current set of active synapses into a scalar value termed the internal excitation or net input excitation ...
PDF
... the teaching signal was further enriched to better fit the pertaining biological data on the responses of DA neurons to novel stimuli. The actor in these models was comprised of one layer of neurons, each representing a specific action. It learned stimulus-action pairs based on the prediction error ...
... the teaching signal was further enriched to better fit the pertaining biological data on the responses of DA neurons to novel stimuli. The actor in these models was comprised of one layer of neurons, each representing a specific action. It learned stimulus-action pairs based on the prediction error ...
Communication as an emergent metaphor for neuronal operation
... weights according to some rule, adjustment in a given time step being a function of a training example. Weight updates are successively aggregated until the network reaches an equilibrium in which no adjustments are made (or alternatively stopping before the equilibrium, if designed to avoid overfit ...
... weights according to some rule, adjustment in a given time step being a function of a training example. Weight updates are successively aggregated until the network reaches an equilibrium in which no adjustments are made (or alternatively stopping before the equilibrium, if designed to avoid overfit ...
Baseball Prediction Using Ensemble Learning by Arlo Lyle (Under
... While much work has been done over the past ten years in the area of baseball prediction, due to competition between companies and the importance of providing the best predictions, very little information is provided about how those companies actually calculate their predictions. Currently the most ...
... While much work has been done over the past ten years in the area of baseball prediction, due to competition between companies and the importance of providing the best predictions, very little information is provided about how those companies actually calculate their predictions. Currently the most ...
Applying Transcranial Alternating Current Stimulation to the Study of Spike Timing Dependent Plasticity in Neural Networks
... networks. Developing in silico neural networks with which to test the efficacy of tACS stands to reduce the cost and time associated with the research needed to translate this therapy from the lab to the clinic. The present study created such a micro-network composed of modified FitzHugh-Nagumo neur ...
... networks. Developing in silico neural networks with which to test the efficacy of tACS stands to reduce the cost and time associated with the research needed to translate this therapy from the lab to the clinic. The present study created such a micro-network composed of modified FitzHugh-Nagumo neur ...
Canonical computations of cerebral cortex
... This was first established in cat V1, in which cortex, and thus intracortical input, was silenced either by cooling [49] or by electrical shock (which evoked massive inhibition) [50], leaving only thalamic input. These manipulations did not change the tuning for stimulus orientation of the membrane ...
... This was first established in cat V1, in which cortex, and thus intracortical input, was silenced either by cooling [49] or by electrical shock (which evoked massive inhibition) [50], leaving only thalamic input. These manipulations did not change the tuning for stimulus orientation of the membrane ...
Edge of chaos and prediction of computational performance for
... An analysis of the temporal evolution of state differences that result from fairly large input differences cannot identify those parameter values in the map of Fig. 1(b) that yield circuits which have (in conjunction with a linear readout) large computational performance (Maass et al., 2005). The re ...
... An analysis of the temporal evolution of state differences that result from fairly large input differences cannot identify those parameter values in the map of Fig. 1(b) that yield circuits which have (in conjunction with a linear readout) large computational performance (Maass et al., 2005). The re ...
PVLV: The Primary Value and Learned Value
... Barto, 1981), which captures the core principle that learning should be based on the discrepancy between predictions and actual outcomes: ␦ t ⫽ rt ⫺ r̂t, ...
... Barto, 1981), which captures the core principle that learning should be based on the discrepancy between predictions and actual outcomes: ␦ t ⫽ rt ⫺ r̂t, ...
Licence Plate Localization And Recognition Using
... Artificial neural networks are statistical models of real world systems which are built by tuning a set of parameters. These parameters, known as weights, describe a model which forms a mapping from a set of given values known as inputs to an associated set of values, the outputs. The process of tun ...
... Artificial neural networks are statistical models of real world systems which are built by tuning a set of parameters. These parameters, known as weights, describe a model which forms a mapping from a set of given values known as inputs to an associated set of values, the outputs. The process of tun ...
Improving CNN Performance with Min-Max Objective
... In this experiment, the CNN “quick” model from the Caffe package2 (named Quick-CNN) is selected as the baseline model. This model consists of 3 convolution layers and 1 fully connected layers. Experimental results of test error rates on the CIFAR-10 test set are shown in Table 1. In this Table, Min- ...
... In this experiment, the CNN “quick” model from the Caffe package2 (named Quick-CNN) is selected as the baseline model. This model consists of 3 convolution layers and 1 fully connected layers. Experimental results of test error rates on the CIFAR-10 test set are shown in Table 1. In this Table, Min- ...
(Full text - MSWord file 171K)
... enriched to better fit the pertaining biological data on the responses of DA neurons to novel stimuli. ...
... enriched to better fit the pertaining biological data on the responses of DA neurons to novel stimuli. ...
Associative learning signals in the brain
... exhibited enhanced responses to the US a full day before the first day animals expressed learning of the CS–US pairing. While the behavioral conditioned responses remained asymptotic on the 2 days following learning, the enhanced neural responses to the CS and US declined back to control levels in th ...
... exhibited enhanced responses to the US a full day before the first day animals expressed learning of the CS–US pairing. While the behavioral conditioned responses remained asymptotic on the 2 days following learning, the enhanced neural responses to the CS and US declined back to control levels in th ...
A Self-Organizing Neural Network for Contour Integration through Synchronized Firing
... dense enough visual input for such connections to form during development, the connections become diffuse, resulting in weaker integration. Statistics of images projected on the retina indeed support this hypothesis. Reinagel & Zador (1999) showed that human gaze most often falls upon areas with hig ...
... dense enough visual input for such connections to form during development, the connections become diffuse, resulting in weaker integration. Statistics of images projected on the retina indeed support this hypothesis. Reinagel & Zador (1999) showed that human gaze most often falls upon areas with hig ...
A Neural Network of Adaptively Timed Reinforcement
... duration. Sections 10-15 interpret the adaptive timing mechanism in terms of interactions between dentate granule cells and CA3 pyramidal cells in the hippocampus, notably at NMDA receptors. Neurobiological data in support of this hypothesis are summarized and new predictions made. Sections 16-22 su ...
... duration. Sections 10-15 interpret the adaptive timing mechanism in terms of interactions between dentate granule cells and CA3 pyramidal cells in the hippocampus, notably at NMDA receptors. Neurobiological data in support of this hypothesis are summarized and new predictions made. Sections 16-22 su ...
Artificial Neural Network Channel Estimation for OFDM
... single layer could be called the “output layer” (OL). Each node in the OL is called an output neuron. This structure can be extended to a multi-layer FF ANN by adding one or more layers to the existing network. These additional layers thenbecome “hidden layers” (HL). Each node in the HL is called a ...
... single layer could be called the “output layer” (OL). Each node in the OL is called an output neuron. This structure can be extended to a multi-layer FF ANN by adding one or more layers to the existing network. These additional layers thenbecome “hidden layers” (HL). Each node in the HL is called a ...
Transfer Learning using Computational Intelligence
... multi-task learning, robust learning, and concept drift are all terms which have been used to handle the related scenarios. More specifically, when the method aims to optimize the performance on multiple tasks or domains simultaneously, it is considered to be multi-task learning. If it optimizes pe ...
... multi-task learning, robust learning, and concept drift are all terms which have been used to handle the related scenarios. More specifically, when the method aims to optimize the performance on multiple tasks or domains simultaneously, it is considered to be multi-task learning. If it optimizes pe ...
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.