Computation with Spikes in a Winner-Take-All Network
... model will ensure that all membrane potentials are discharged (Vi = 0) at the onset of a stimulus. The network will then select the winning neuron after receiving a predetermined number of input spikes, and this winner will have the first output spike. Even if the conditions above are fulfilled, the n ...
... model will ensure that all membrane potentials are discharged (Vi = 0) at the onset of a stimulus. The network will then select the winning neuron after receiving a predetermined number of input spikes, and this winner will have the first output spike. Even if the conditions above are fulfilled, the n ...
Imitating others by composition of primitive actions: a neuro
... into multiple action primitives such as reaching to a cup, grasping the cup and moving the cup toward one’s mouth. Each action primitive can be reutilized also as a component for other actions e.g. reaching to a cup can be used for another goal such as clearing it away. This idea of decomposition of ...
... into multiple action primitives such as reaching to a cup, grasping the cup and moving the cup toward one’s mouth. Each action primitive can be reutilized also as a component for other actions e.g. reaching to a cup can be used for another goal such as clearing it away. This idea of decomposition of ...
learning motor skills by imitation: a biologically inspired robotic model
... movements is done in the PM module and in the cerebellum module. These two modules are implemented using the dynamical recurrent associative memory architecture (DRAMA) (Billard, 1998; Billard & Hayes, 1999), which allows learning of times series and of spatio-temporal invariance in multimodal input ...
... movements is done in the PM module and in the cerebellum module. These two modules are implemented using the dynamical recurrent associative memory architecture (DRAMA) (Billard, 1998; Billard & Hayes, 1999), which allows learning of times series and of spatio-temporal invariance in multimodal input ...
Cortical Plasticity - Lund University Publications
... Patients with nerve injury in their arm might end up with the nerve fibres completely misconnected after the healing process. To help these patients an urgent question is how to optimize the rehabilitation, to make the cortical maps reorganize as well as possible. One question then, is if there are ...
... Patients with nerve injury in their arm might end up with the nerve fibres completely misconnected after the healing process. To help these patients an urgent question is how to optimize the rehabilitation, to make the cortical maps reorganize as well as possible. One question then, is if there are ...
Using Neural Networks to Improve Behavioural Realism in
... parameters, the inputs and outputs to the neural network required to produce the desired driving behaviour and enable straightforward interfacing with the simulator ...
... parameters, the inputs and outputs to the neural network required to produce the desired driving behaviour and enable straightforward interfacing with the simulator ...
Acquired Equivalence and Distinctiveness of Cues: I. Exploring a
... this case the food unit) within the network depicted in Figure 1. Given these assumptions, the presentation of AB will excite either p or r and, because these units receive two sources of input, they will strongly activate either the food output unit or the no-food output unit, respectively. Given t ...
... this case the food unit) within the network depicted in Figure 1. Given these assumptions, the presentation of AB will excite either p or r and, because these units receive two sources of input, they will strongly activate either the food output unit or the no-food output unit, respectively. Given t ...
Karuza, E. A., Newport, E. L., Aslin, R. N., Starling, S. J., Tivarus
... involved in the computation of statistical regularities both within and across modalities. However, since the previous fMRI studies of segmentation show mixed behavioral evidence of statistical learning, it is challenging to compare across studies. The present experiment addresses gaps in our unders ...
... involved in the computation of statistical regularities both within and across modalities. However, since the previous fMRI studies of segmentation show mixed behavioral evidence of statistical learning, it is challenging to compare across studies. The present experiment addresses gaps in our unders ...
Learning Efficient Markov Networks - Washington
... to variables that decompose the remaining variables into independent subsets, and recurse on these smaller problems until trivial ones are obtained. Our algorithm uses a similar strategy, but at learning time: it recursively attempts to find features (i.e., partial variable assignments) that decompo ...
... to variables that decompose the remaining variables into independent subsets, and recurse on these smaller problems until trivial ones are obtained. Our algorithm uses a similar strategy, but at learning time: it recursively attempts to find features (i.e., partial variable assignments) that decompo ...
Chapter 5 Development
... Synaptic Pruning • Reduces the number of functional synapses • Influenced by neutrophins and functionality of the synapse ...
... Synaptic Pruning • Reduces the number of functional synapses • Influenced by neutrophins and functionality of the synapse ...
Using Sentence-Level LSTM Language Models for Script Inference
... are focused on knowledge induction. These systems are primarily designed to induce collections of co-occurring event types involving the same entities, and their ability to infer held-out events is not their primary intended purpose (Chambers and Jurafsky, 2008; Ferraro and Van Durme, 2016, inter al ...
... are focused on knowledge induction. These systems are primarily designed to induce collections of co-occurring event types involving the same entities, and their ability to infer held-out events is not their primary intended purpose (Chambers and Jurafsky, 2008; Ferraro and Van Durme, 2016, inter al ...
An Introduction to Reinforcement Learning
... Learning Given S, A, γ, and the facility to follow a trajectory by sampling from T and R, how can we find an optimal policy π ∗ ? Various classes of learning methods exist. We will consider a simple one called Q-learning, which is a temporal difference learning algorithm. Let Q be our “guess” of ...
... Learning Given S, A, γ, and the facility to follow a trajectory by sampling from T and R, how can we find an optimal policy π ∗ ? Various classes of learning methods exist. We will consider a simple one called Q-learning, which is a temporal difference learning algorithm. Let Q be our “guess” of ...
Machine learning for information retrieval: Neural networks
... information retrieval and indexing. More recently, information science researchers have turned to other newer artificial-intelligence-based inductive learning techniques including neural networks, symbolic learning, and genetic algorithms. These newer techniques, which are grounded on diverse paradi ...
... information retrieval and indexing. More recently, information science researchers have turned to other newer artificial-intelligence-based inductive learning techniques including neural networks, symbolic learning, and genetic algorithms. These newer techniques, which are grounded on diverse paradi ...
Introduction to Artificial Neural Networks (ANNs)
... 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 Binding of.. pieces of an image words of a song multisensory in ...
... 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 Binding of.. pieces of an image words of a song multisensory in ...
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... efficiency of their efforts. As a result, VP training makes people better able to strategically allocate attention to multiple components of the task to comply with the change in emphases during training. In contrast, in fixed-priority (FP) training, all components are equally weighted, which was fo ...
... efficiency of their efforts. As a result, VP training makes people better able to strategically allocate attention to multiple components of the task to comply with the change in emphases during training. In contrast, in fixed-priority (FP) training, all components are equally weighted, which was fo ...
NEURAL NETWORKS AND FUZZY SYSTEMS
... Two insights about the rate of convergence First,the individual energies decrease nontrivially.the BAM system does not creep arbitrary slowly down the toward the nearest local minimum.the system takes definite hops into the basin of attraction of the fixed point. Second,a synchronous BAM tends to c ...
... Two insights about the rate of convergence First,the individual energies decrease nontrivially.the BAM system does not creep arbitrary slowly down the toward the nearest local minimum.the system takes definite hops into the basin of attraction of the fixed point. Second,a synchronous BAM tends to c ...
Closed-Form Learning of Markov Networks from Dependency
... case, weight learning is a convex optimization problem, but cannot be done in closed form except in very special circumstances, e.g. [18]. Structure learning is even harder, since it typically involves a search over a large number of candidate structures, and weights must be learned for each candida ...
... case, weight learning is a convex optimization problem, but cannot be done in closed form except in very special circumstances, e.g. [18]. Structure learning is even harder, since it typically involves a search over a large number of candidate structures, and weights must be learned for each candida ...
Barnes TD, Kubota Y, Hu D, Jin DZ, Graybiel AM. Activity of striatal
... in habit formation4–8, in rats successively trained on a rewardbased procedural task, given extinction training and then given reacquisition training. The spike activity of striatal output neurons, nodal points in cortico-basal ganglia circuits, changed markedly across multiple dimensions during eac ...
... in habit formation4–8, in rats successively trained on a rewardbased procedural task, given extinction training and then given reacquisition training. The spike activity of striatal output neurons, nodal points in cortico-basal ganglia circuits, changed markedly across multiple dimensions during eac ...
The Emergence of Rule-Use: A Dynamic Neural Field Model of... Aaron Buss ()
... presentation of the target card created “ridges” of activation at the specified feature values (blue, star) that spanned the spatial dimension. Next, activation peaks start to grow due to the locally excitatory/laterally inhibitory interactions in the FWM fields at the locations where the test input ...
... presentation of the target card created “ridges” of activation at the specified feature values (blue, star) that spanned the spatial dimension. Next, activation peaks start to grow due to the locally excitatory/laterally inhibitory interactions in the FWM fields at the locations where the test input ...
Binary neurons and networks
... 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. (1949) Donald Hebb ...
... 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. (1949) Donald Hebb ...
Semantic Networks: Visualizations of Knowledge
... The representations in the circle are alternate external forms. We can add the distinction between external and internal representations adds to this picture. Brachman’s lowest level has the machine concepts of "atoms and pointers", betraying his preference for implementations built using Lisp. Of c ...
... The representations in the circle are alternate external forms. We can add the distinction between external and internal representations adds to this picture. Brachman’s lowest level has the machine concepts of "atoms and pointers", betraying his preference for implementations built using Lisp. Of c ...
Potts Networks – Latching – Correlated patterns
... • In addition, popular neurons affect negatively the general performance (decay of F(x)). • These results show how the current trend in category specific deficits (‘living’ weaker than ‘non living’) could emerge even in a purely homogeneous network. ...
... • In addition, popular neurons affect negatively the general performance (decay of F(x)). • These results show how the current trend in category specific deficits (‘living’ weaker than ‘non living’) could emerge even in a purely homogeneous network. ...
Building BN-Based System Reliability Model by Dual Genetic
... structure, since a large space of possible BN structures could be searched from a small number of nodes. The well-known K2 algorithm aims to search the best BN structure by analyzing a system database[9]. However, the K2 algorithm is based on the assumption that there is a given ordering between the ...
... structure, since a large space of possible BN structures could be searched from a small number of nodes. The well-known K2 algorithm aims to search the best BN structure by analyzing a system database[9]. However, the K2 algorithm is based on the assumption that there is a given ordering between the ...
Research and Development of Granular Neural Networks
... self-learning, association, less manual intervention, high accuracy, and makes use of expert knowledge well. But there are many problems, such as difficult to handle and describe fuzzy information, difficult to take advantage of existing experience knowledge, non-explanatory, and its higher sample r ...
... self-learning, association, less manual intervention, high accuracy, and makes use of expert knowledge well. But there are many problems, such as difficult to handle and describe fuzzy information, difficult to take advantage of existing experience knowledge, non-explanatory, and its higher sample r ...
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.