ALGORITHMICS
... (health status, family, career evolution etc) to be taken into account in order to decide if a given employee is reliable for a bank loan. A bank expert relies on his experience (previous success and failure cases) when he makes a decision ...
... (health status, family, career evolution etc) to be taken into account in order to decide if a given employee is reliable for a bank loan. A bank expert relies on his experience (previous success and failure cases) when he makes a decision ...
IMPROVING THE PERFORMANCES OF ASYNCHRONOUS
... Previous research [8, 13] shows that synchronization is beneficial for some techniques, but increases the costs for others. The evaluation of the performances of the AWCS technique is done using NetLogo. NetLogo is a programming environment with agents that allows the implementation of the asynchron ...
... Previous research [8, 13] shows that synchronization is beneficial for some techniques, but increases the costs for others. The evaluation of the performances of the AWCS technique is done using NetLogo. NetLogo is a programming environment with agents that allows the implementation of the asynchron ...
lecture 2 not ready - Villanova Department of Computing Sciences
... • Compression: The rule is simpler than the data it explains • Outlier detection: Exceptions that are not covered by the rule, e.g., fraud CSC 4510 - M.A. Papalaskari - Villanova University ...
... • Compression: The rule is simpler than the data it explains • Outlier detection: Exceptions that are not covered by the rule, e.g., fraud CSC 4510 - M.A. Papalaskari - Villanova University ...
Neurulation I (Pevny)
... Neural plate is firmly anchored to adjacent tissues at hinge points (to the notochord for MHP an Epidermal ectoderm for the DLHP. 2. Neuroepithelial cell wedging within the hinge-points generates furrowing. 3. Forces for folding are generated lateral to the hinge points by the expanding epidermal ec ...
... Neural plate is firmly anchored to adjacent tissues at hinge points (to the notochord for MHP an Epidermal ectoderm for the DLHP. 2. Neuroepithelial cell wedging within the hinge-points generates furrowing. 3. Forces for folding are generated lateral to the hinge points by the expanding epidermal ec ...
biological learning and artificial intelligence
... personally subscribe to this view in the context of language acquisition, this could certainly be the case in many other situations. Most fixed motor patterns would obviously profit from some degree of adaptation. This, of course, would no longer make them fixed. A system of this kind that has been ...
... personally subscribe to this view in the context of language acquisition, this could certainly be the case in many other situations. Most fixed motor patterns would obviously profit from some degree of adaptation. This, of course, would no longer make them fixed. A system of this kind that has been ...
Testing Promotes Long-Term Learning via Stabilizing Activation
... in the laboratory. Participants had a 30 sec rest period after the thirtieth cued recall trial. During the follow-up test right after the scanning sessions, participants were asked to recall the remembered words. In all further analyses, we considered a word pair to be remembered only if the partici ...
... in the laboratory. Participants had a 30 sec rest period after the thirtieth cued recall trial. During the follow-up test right after the scanning sessions, participants were asked to recall the remembered words. In all further analyses, we considered a word pair to be remembered only if the partici ...
Mapping the manuals of madness: Comparing the ICD
... three on this index, indicating a small world structure, it has a similar average shortest path length, but a higher level of clustering, as compared to a network of the same dimensions, in which the same number of edges is assigned to node pairs completely at random (see Watts and Strogatz, 1998). ...
... three on this index, indicating a small world structure, it has a similar average shortest path length, but a higher level of clustering, as compared to a network of the same dimensions, in which the same number of edges is assigned to node pairs completely at random (see Watts and Strogatz, 1998). ...
View/Open - Minerva Access
... information and how the networks and structures involved are formed. In this thesis, we use theoretical approaches to further our understanding of brain function. First, we investigate how experimentally-based learning rules lead to the formation of different network structures, through unsupervised ...
... information and how the networks and structures involved are formed. In this thesis, we use theoretical approaches to further our understanding of brain function. First, we investigate how experimentally-based learning rules lead to the formation of different network structures, through unsupervised ...
Spike-timing dependent plasticity and the cognitive map
... Department of Physics and Astronomy, University of California Los Angeles, Los Angeles, CA, USA Centre for Computational Neuroscience and Robotics, University of Sussex, Brighton, UK ...
... Department of Physics and Astronomy, University of California Los Angeles, Los Angeles, CA, USA Centre for Computational Neuroscience and Robotics, University of Sussex, Brighton, UK ...
Learning place cells, grid cells and invariances: A unifying model
... synaptic changes during spatial exploration. In principle, the time scale of plasticitybased models can be augmented arbitrarily by increasing the synaptic learning rates. For stable patterns to emerge, however, significant weight changes must occur only after the animal has visited most of the envi ...
... synaptic changes during spatial exploration. In principle, the time scale of plasticitybased models can be augmented arbitrarily by increasing the synaptic learning rates. For stable patterns to emerge, however, significant weight changes must occur only after the animal has visited most of the envi ...
14. Development and Plasticity
... inputs riin. The synaptic efficiency is denoted by wi. the output of the neuron, rout depends on the particular input stimulus. (B) A network of associative nodes. Each component of the input vector, riin, is distributed to each neuron in the network. However, the effect of the input can be differen ...
... inputs riin. The synaptic efficiency is denoted by wi. the output of the neuron, rout depends on the particular input stimulus. (B) A network of associative nodes. Each component of the input vector, riin, is distributed to each neuron in the network. However, the effect of the input can be differen ...
14. Development and Plasticity
... inputs riin. The synaptic efficiency is denoted by wi. the output of the neuron, rout depends on the particular input stimulus. (B) A network of associative nodes. Each component of the input vector, riin, is distributed to each neuron in the network. However, the effect of the input can be differen ...
... inputs riin. The synaptic efficiency is denoted by wi. the output of the neuron, rout depends on the particular input stimulus. (B) A network of associative nodes. Each component of the input vector, riin, is distributed to each neuron in the network. However, the effect of the input can be differen ...
Sten Grillner
... intact animals or the need to use animals under anesthesia. We then could show that subthreshold activation of MLR indeed released reflex discharges similar to that of DOPA and a suppression of other shortlatency responses. We then suggested that the MLR indeed caused locomotion by releasing the spi ...
... intact animals or the need to use animals under anesthesia. We then could show that subthreshold activation of MLR indeed released reflex discharges similar to that of DOPA and a suppression of other shortlatency responses. We then suggested that the MLR indeed caused locomotion by releasing the spi ...
Emergence of new signal-primitives in neural systems
... Combinatoric novelty is a dynamic, creative strategy insofar as it constantly brings into being new combinations of elements. However, such combinatoric realms are inherently limited by their fixed sets of primitive elements. Arguably, all that can happen within such universes are recombinations of ...
... Combinatoric novelty is a dynamic, creative strategy insofar as it constantly brings into being new combinations of elements. However, such combinatoric realms are inherently limited by their fixed sets of primitive elements. Arguably, all that can happen within such universes are recombinations of ...
Towards the integration of neural mechanisms and cognition in
... neural circuits and the robot; it is the control interface and it implements how the neural activity is translated in actuation. The Neural lattice layer is the brain model and it is fairly composed by at least two sublayers: the neural circuits and the cognition. The neural circuits layer contains ...
... neural circuits and the robot; it is the control interface and it implements how the neural activity is translated in actuation. The Neural lattice layer is the brain model and it is fairly composed by at least two sublayers: the neural circuits and the cognition. The neural circuits layer contains ...
Realizing Biological Spiking Network Models in a Configurable
... NE of the challenges in simulating large neural networks in a parallel, distributed manner is to ensure sufficient communication bandwidth between the computation nodes. Depending on the neural connection densites and the actual spike rates, communication can in fact constitute the major bottleneck l ...
... NE of the challenges in simulating large neural networks in a parallel, distributed manner is to ensure sufficient communication bandwidth between the computation nodes. Depending on the neural connection densites and the actual spike rates, communication can in fact constitute the major bottleneck l ...
Dopamine: generalization and bonuses
... stimulus. This is matched by the temporal difference prediction error dðtÞ; which follows the reward signal rðtÞ: (B) In later learning trials, a dopamine cell responds to the delivery of the stimulus, but not the reward. This is again matched by dðtÞ—there is no response at the time of the reward b ...
... stimulus. This is matched by the temporal difference prediction error dðtÞ; which follows the reward signal rðtÞ: (B) In later learning trials, a dopamine cell responds to the delivery of the stimulus, but not the reward. This is again matched by dðtÞ—there is no response at the time of the reward b ...
An Imperfect Dopaminergic Error Signal Can Drive Temporal
... Recently, we proposed the first spiking neuronal network model to implement a complete TD(0) implementation with both prediction and control, and demonstrated that it is able to solve a non-trivial task with sparse rewards [34]. However, in that model each synapse performs its own approximation of t ...
... Recently, we proposed the first spiking neuronal network model to implement a complete TD(0) implementation with both prediction and control, and demonstrated that it is able to solve a non-trivial task with sparse rewards [34]. However, in that model each synapse performs its own approximation of t ...
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.