November 2008_Neural_Computing_Systems.SupervisedBackProp
... Backpropagation (BP) is amongst the ‘most popular algorithms for ANNs’: it has been estimated by Paul Werbos, the person who first worked on the algorithm in the 1970’s, that between 40% and 90% of the real world ANN applications use the BP algorithm. Werbos traces the algorithm to the psychologist ...
... Backpropagation (BP) is amongst the ‘most popular algorithms for ANNs’: it has been estimated by Paul Werbos, the person who first worked on the algorithm in the 1970’s, that between 40% and 90% of the real world ANN applications use the BP algorithm. Werbos traces the algorithm to the psychologist ...
Brain-implantable biomimetic electronics as the next era in neural
... allowing a solution path to be defined and followed in an incremental manner. Outlined in this report is our collective progress to date in developing the underlying science and technology that will enable the functions of specific brain regions to be replaced by multichip modules consisting of nove ...
... allowing a solution path to be defined and followed in an incremental manner. Outlined in this report is our collective progress to date in developing the underlying science and technology that will enable the functions of specific brain regions to be replaced by multichip modules consisting of nove ...
Music Similarity Estimation with the Mean
... vectors. As only the means depend on hm , this model is called mRBM (Fig. 2a). It is the standard type of RBM to model realvalued inputs. However, independent Gaussian noise does not yield a good generative model for most real-world data. To model pairwise dependencies of visibles gated by hiddens, ...
... vectors. As only the means depend on hm , this model is called mRBM (Fig. 2a). It is the standard type of RBM to model realvalued inputs. However, independent Gaussian noise does not yield a good generative model for most real-world data. To model pairwise dependencies of visibles gated by hiddens, ...
5. hierarchical multimodal language modeling
... This happens, for example, when auditory input enters area A1. First the ‘_start’ representation in area A1 gets activated indicating the beginning of a new spoken sentence. This will activate the corresponding ‘_start’ assembly in area A2 which in turn activates the ‘S’ assembly in the sequence are ...
... This happens, for example, when auditory input enters area A1. First the ‘_start’ representation in area A1 gets activated indicating the beginning of a new spoken sentence. This will activate the corresponding ‘_start’ assembly in area A2 which in turn activates the ‘S’ assembly in the sequence are ...
Reliable prediction of T-cell epitopes using neural networks with
... comparable performance. In the following we will use the Blosum50 matrix when we refer to Blosum sequence encoding. A last encoding scheme is defined in terms of a hidden Markov model. The details of this encoding are described later in section 3.3. The sparse versus the Blosum sequence-encoding sch ...
... comparable performance. In the following we will use the Blosum50 matrix when we refer to Blosum sequence encoding. A last encoding scheme is defined in terms of a hidden Markov model. The details of this encoding are described later in section 3.3. The sparse versus the Blosum sequence-encoding sch ...
Graph Logic Model Framework for Predictive Linguistic Analysis
... described with the help of logic operators (LO) from the basic {OR, AND, XOR}. LO might be chosen to apply to all and every node to define conditions of leaving/arriving. Say, if one applies “XOR” operator for the node leaving condition and chooses links along the graph, we are able to redraw the gr ...
... described with the help of logic operators (LO) from the basic {OR, AND, XOR}. LO might be chosen to apply to all and every node to define conditions of leaving/arriving. Say, if one applies “XOR” operator for the node leaving condition and chooses links along the graph, we are able to redraw the gr ...
the application of artificial intelligence methods in heat - QRC
... tempering curve, and the thermal conductivity for similar new heats in the production or for every new steel which is similar to some previously used one. It is also possible to optimise the steel chemical composition for the desired tool steels tempering resistance, or the thermal conductivity. Thi ...
... tempering curve, and the thermal conductivity for similar new heats in the production or for every new steel which is similar to some previously used one. It is also possible to optimise the steel chemical composition for the desired tool steels tempering resistance, or the thermal conductivity. Thi ...
Computational intelligent strategies to predict energy conservation
... system proposed by Jang and Sun [46]. A typical fuzzy inference system based on subtractive clustering has been trained by both back propagation and hybrid learning methods frequently [47]. In Artificial Neural Networks (ANN), the most commonly used algorithms is Back Propagation (BP). However, othe ...
... system proposed by Jang and Sun [46]. A typical fuzzy inference system based on subtractive clustering has been trained by both back propagation and hybrid learning methods frequently [47]. In Artificial Neural Networks (ANN), the most commonly used algorithms is Back Propagation (BP). However, othe ...
Conditioning: Simple Neural Circuits in the Honeybee
... LTM formation in intact animals. The use of the uncaging technique allowed further identification of the molecular processes underlying the prolonged PKA activation in the AL and thus the mechanisms contributing to induction of LTM in the AL. Nitric oxide, which is required for LTM formation in the ...
... LTM formation in intact animals. The use of the uncaging technique allowed further identification of the molecular processes underlying the prolonged PKA activation in the AL and thus the mechanisms contributing to induction of LTM in the AL. Nitric oxide, which is required for LTM formation in the ...
Dynamic Computation in a Recurrent Network of Heterogeneous
... In contrast, networks with heterogeneous neurons tend to bias the locations where clusters reside. Clusters do not wander Previous attempts to model such networks in software have nta indt h oain htmxmz freely bt asacrificed low-level details that are known to affect cluster their local recurrent fe ...
... In contrast, networks with heterogeneous neurons tend to bias the locations where clusters reside. Clusters do not wander Previous attempts to model such networks in software have nta indt h oain htmxmz freely bt asacrificed low-level details that are known to affect cluster their local recurrent fe ...
File
... 8. What is an atomic event? An atomic event is an assignment of particular values to all variables, in other words, the complete specifications of the state of domain. 9. Define joint probability distribution. Joint probability distribution completely specifies an agent's probability assignments to ...
... 8. What is an atomic event? An atomic event is an assignment of particular values to all variables, in other words, the complete specifications of the state of domain. 9. Define joint probability distribution. Joint probability distribution completely specifies an agent's probability assignments to ...
Enhanced cholinergic suppression of previously strengthened synapses enables the formation of
... Hasselmo & Bower, 1993; Hasselmo et al., 1995; Kohonen, 1984; Marr, 1971; McNaughton & Morris, 1987; Wilson & Bower, 1992). These intrinsic connections give models of the olfactory cortex and the hippocampus the capacity to store distributed patterns (conveyed via afferent input connections) and to ...
... Hasselmo & Bower, 1993; Hasselmo et al., 1995; Kohonen, 1984; Marr, 1971; McNaughton & Morris, 1987; Wilson & Bower, 1992). These intrinsic connections give models of the olfactory cortex and the hippocampus the capacity to store distributed patterns (conveyed via afferent input connections) and to ...
Ramón y Cajal, 19 th century
... Neuronal activity changes the intracellular calcium. Via changes in intra-cellular calcium, neurons change their morphology with respect to their axonal and dendritic shape. This leads to changes in neuronal connectivity which, in turn, adapts neuronal activity. The goal is that by these changes neu ...
... Neuronal activity changes the intracellular calcium. Via changes in intra-cellular calcium, neurons change their morphology with respect to their axonal and dendritic shape. This leads to changes in neuronal connectivity which, in turn, adapts neuronal activity. The goal is that by these changes neu ...
Multi-Instance Learning
... It is worthwhile to note that in several applications of the multiple instance problem, the actual predictions desired are real valued. For example, the binding affinity between a molecule and receptor is quantitative, so a real-valued label of binding strength is preferable. S. Ray & D. Page (2001) ...
... It is worthwhile to note that in several applications of the multiple instance problem, the actual predictions desired are real valued. For example, the binding affinity between a molecule and receptor is quantitative, so a real-valued label of binding strength is preferable. S. Ray & D. Page (2001) ...
PDF file
... The auditory data were collected as follows. 63 persons with a variety of nationalities, including American, Chinese, French, India, Malaysian and Spanish, and ages, from 18 to 50, participated in our speech data collection. Each person made 5 utterances for each of the 10 digits. There is a silence ...
... The auditory data were collected as follows. 63 persons with a variety of nationalities, including American, Chinese, French, India, Malaysian and Spanish, and ages, from 18 to 50, participated in our speech data collection. Each person made 5 utterances for each of the 10 digits. There is a silence ...
Minimal model of strategy switching in the plus
... Experimental studies in the plus maze have shown that rats with inactivated HPC were strongly biased towards response strategies, while they were biased towards place strategies when DLS was inactivated [4, 5]. In other experimental environments a double dissociation between HPC and DLS has been dem ...
... Experimental studies in the plus maze have shown that rats with inactivated HPC were strongly biased towards response strategies, while they were biased towards place strategies when DLS was inactivated [4, 5]. In other experimental environments a double dissociation between HPC and DLS has been dem ...
Spiking Neural Networks: Principles and Challenges
... to the computation have to be expressed in terms of the spikes that spiking neurons communicate with. The challenge is that the nature of the neural code (or neural codes) is an unresolved topic of research in neuroscience. However, based on what is known from biology, a number of neural information ...
... to the computation have to be expressed in terms of the spikes that spiking neurons communicate with. The challenge is that the nature of the neural code (or neural codes) is an unresolved topic of research in neuroscience. However, based on what is known from biology, a number of neural information ...
Proceedings of 2014 BMI the Third International Conference on
... On one hand neuroscience is rich in data and poor in theory. On the other hand, many computer scientists are busy with engineering inspired methods, not motivated by brain in ...
... On one hand neuroscience is rich in data and poor in theory. On the other hand, many computer scientists are busy with engineering inspired methods, not motivated by brain in ...
Adaptive routing in wireless communication networks using swarm
... particular difficulties arising from the dynamic nature of their topology, due to node movement, radio interference, node failures, and new additions. A variety of routing protocols have been offered and the best-performing schemes generally depend on the specific characteristics of the operating en ...
... particular difficulties arising from the dynamic nature of their topology, due to node movement, radio interference, node failures, and new additions. A variety of routing protocols have been offered and the best-performing schemes generally depend on the specific characteristics of the operating en ...
An Artificial Neural Network Model for the Human Heart:
... map the final output value to this associated range. For example, in the case where the final output value is constrained by the activation function to a numerical value between 0 and 1, the value may need to then be mapped or converted to a different range (e.g. 0 and 100, 1 to 9, etc.). A key feat ...
... map the final output value to this associated range. For example, in the case where the final output value is constrained by the activation function to a numerical value between 0 and 1, the value may need to then be mapped or converted to a different range (e.g. 0 and 100, 1 to 9, etc.). A key feat ...
10EI212 NEURAL NETWORKS AND FUZZY LOGIC CONTROL
... Unit IV: Fuzzy Logic Control Membership function – Knowledge base – Decision-making logic – Optimisation of membership function using neural networks – Adaptive fuzzy system – Introduction to genetic algorithm Unit V Application of FLC Fuzzy logic control – Inverted pendulum – Image processing – Hom ...
... Unit IV: Fuzzy Logic Control Membership function – Knowledge base – Decision-making logic – Optimisation of membership function using neural networks – Adaptive fuzzy system – Introduction to genetic algorithm Unit V Application of FLC Fuzzy logic control – Inverted pendulum – Image processing – Hom ...
האוניברסיטה העברית בירושלי - Center for the Study of Rationality
... operant learning in natural environments. First, the computational problem of finding the values is bedeviled by the “curse of dimensionality”: the number of states is exponential with the number of variable, which define a state [1]. Second, when the state of the world is only partially known, (i.e ...
... operant learning in natural environments. First, the computational problem of finding the values is bedeviled by the “curse of dimensionality”: the number of states is exponential with the number of variable, which define a state [1]. Second, when the state of the world is only partially known, (i.e ...
Artificial Intelligence: Modern Approach
... We cover areas that are sometimes underemphasized, including reasoning under uncertainty, learning, neural networks, natural language, vision, robotics, and philosophical foundations. We cover many of the more recent ideas in the field, including simulated annealing, memory-bounded search, global on ...
... We cover areas that are sometimes underemphasized, including reasoning under uncertainty, learning, neural networks, natural language, vision, robotics, and philosophical foundations. We cover many of the more recent ideas in the field, including simulated annealing, memory-bounded search, global on ...
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.