IV. Model Application: the UAV Autonomous Learning in Unknown
... action is chosen when it has high Q-values than others in a given state. Last but not least, unlike most of the cognitive experiments, which deal with relatively simple decision-making task [1, 4, 8], our model’s application of UAV autonomous learning in 3D environment is more complicated and full o ...
... action is chosen when it has high Q-values than others in a given state. Last but not least, unlike most of the cognitive experiments, which deal with relatively simple decision-making task [1, 4, 8], our model’s application of UAV autonomous learning in 3D environment is more complicated and full o ...
A Parallel Approach to Syntax for Generation 1 Introduction
... That the generation task requires parallelism has not been generally recognized. A historical reason for this might be the pervasive focus on structure rather than process. It is common to start work from a notion of the inputs that a generator must deal with, or from a (typically structuralist) the ...
... That the generation task requires parallelism has not been generally recognized. A historical reason for this might be the pervasive focus on structure rather than process. It is common to start work from a notion of the inputs that a generator must deal with, or from a (typically structuralist) the ...
Irregular persistent activity induced by synaptic excitatory feedback
... Brunel and Wang, 2001), though not very robustly. However, these models do not account for the high irregularity shown in the experiments. While high irregularity can be obtained robustly in the baseline period, provided inhibition is sufficiently strong, because neurons receive synaptic inputs that ...
... Brunel and Wang, 2001), though not very robustly. However, these models do not account for the high irregularity shown in the experiments. While high irregularity can be obtained robustly in the baseline period, provided inhibition is sufficiently strong, because neurons receive synaptic inputs that ...
PTE: Predictive Text Embedding through Large-scale
... the representations and only use the labels to train the classifier after the data is transformed into the learned representation. RNTNs and CNNs incorporate the labels directly into representation learning, so the learned representations are particularly tuned for the classification task. To incorp ...
... the representations and only use the labels to train the classifier after the data is transformed into the learned representation. RNTNs and CNNs incorporate the labels directly into representation learning, so the learned representations are particularly tuned for the classification task. To incorp ...
Inductive Logic Programming: Challenges
... which several future perspectives at that time were shown. Since then, the areas related to Machine Learning and AI have been rapidly growing and changing. Recent trends include learning from big data, from statistical learning to deep learning, integration of neural and symbolic learning, general i ...
... which several future perspectives at that time were shown. Since then, the areas related to Machine Learning and AI have been rapidly growing and changing. Recent trends include learning from big data, from statistical learning to deep learning, integration of neural and symbolic learning, general i ...
Online Adaptable Learning Rates for the Game Connect-4
... to the kernel trick used in support vector ma- (Sec. 3.3). The vector w chines (SVM): The low dimensional board is from all LUTs. It can be a rather big vector, conprojected into a high dimensional sample space taining, e. g., 9 million weights in our standard Connect-4 implementation with 70 8-tupl ...
... to the kernel trick used in support vector ma- (Sec. 3.3). The vector w chines (SVM): The low dimensional board is from all LUTs. It can be a rather big vector, conprojected into a high dimensional sample space taining, e. g., 9 million weights in our standard Connect-4 implementation with 70 8-tupl ...
From spike frequency to free recall:
... In this model, the primary locus for encoding of associations was in region CA3 of the hippocampal formation. Two features of region CA3 make it particularly appealing as the locus for storage of episodic memory. 1.) the convergence of multimodal sensory information on this region means that strengt ...
... In this model, the primary locus for encoding of associations was in region CA3 of the hippocampal formation. Two features of region CA3 make it particularly appealing as the locus for storage of episodic memory. 1.) the convergence of multimodal sensory information on this region means that strengt ...
The C. elegans Connectome Consists of Homogenous Circuits with
... (both in terms of the total number of sets, and in the difference from the shuffled sets; Fig 2D). The interesting feature in these two sets is the bidirectional connection between X and Y neurons. In triad #10, the X and Y neurons synapse one another and both are presynaptic to their mutual Z neuro ...
... (both in terms of the total number of sets, and in the difference from the shuffled sets; Fig 2D). The interesting feature in these two sets is the bidirectional connection between X and Y neurons. In triad #10, the X and Y neurons synapse one another and both are presynaptic to their mutual Z neuro ...
Logical Modes of Attack in Argumentation Networks
... 1. The fact that a node a attacks a node b can attack a node c, (a → b) → c; 2. A node a can attack the attack of a node b on a node c, a → (b → c); and 3. The fact that node a attacks node b attacks the attack from node c to node d (but not any other attack on d), (a → b) → (c → d). Here, there are ...
... 1. The fact that a node a attacks a node b can attack a node c, (a → b) → c; 2. A node a can attack the attack of a node b on a node c, a → (b → c); and 3. The fact that node a attacks node b attacks the attack from node c to node d (but not any other attack on d), (a → b) → (c → d). Here, there are ...
mwr-paper.pdf
... “natural” form (e.g., geographical data or text given in natural language). In general, a formal abstraction of the domain being modeled is created which is simple enough to be processed on a computer, but still produces an adequate model of the original information. By evaluating the shortcomings o ...
... “natural” form (e.g., geographical data or text given in natural language). In general, a formal abstraction of the domain being modeled is created which is simple enough to be processed on a computer, but still produces an adequate model of the original information. By evaluating the shortcomings o ...
Folie 1
... “… I agree with Stemberger that connectionism can make a valuable contribution to cognitive science. The only place that we differ is that, first, he thinks that the contribution will be made by providing a way of *eliminating* symbols, whereas I think that connectionism will make its greatest contr ...
... “… I agree with Stemberger that connectionism can make a valuable contribution to cognitive science. The only place that we differ is that, first, he thinks that the contribution will be made by providing a way of *eliminating* symbols, whereas I think that connectionism will make its greatest contr ...
Seminar Slides - CSE, IIT Bombay
... represent knowledge. Can replace a human for monotonous jobs of answering queries, e.g. E-help desk. ...
... represent knowledge. Can replace a human for monotonous jobs of answering queries, e.g. E-help desk. ...
Learning to represent reward structure: A key to adapting to complex
... and constraints are refined, which we do not attempt here. The numeric prediction construct and its learning signal are at the heart of the formulation, and they are called the value function and TD error, respectively. The value function defines a solution for the balancing problem, while TD error pr ...
... and constraints are refined, which we do not attempt here. The numeric prediction construct and its learning signal are at the heart of the formulation, and they are called the value function and TD error, respectively. The value function defines a solution for the balancing problem, while TD error pr ...
invariant face and object recognition in the visual system
... Until now, research on translation invariance has considered the case in which there is only one object in the visual field. The question then arises of how the visual system operates in a cluttered environment. Do all objects that can activate an inferior temporal neuron do so whenever they are any ...
... Until now, research on translation invariance has considered the case in which there is only one object in the visual field. The question then arises of how the visual system operates in a cluttered environment. Do all objects that can activate an inferior temporal neuron do so whenever they are any ...
Attractor concretion as a mechanism for the formation of context
... number of trials, the CS-reinforcement contingencies were reversed and monkeys had to learn the new contingencies. In the experiments, the CS–US associations were reversed only once. However, in principle, the two contexts defined by the sets of CS–US associations could be alternated multiple times. ...
... number of trials, the CS-reinforcement contingencies were reversed and monkeys had to learn the new contingencies. In the experiments, the CS–US associations were reversed only once. However, in principle, the two contexts defined by the sets of CS–US associations could be alternated multiple times. ...
Identification of a Functional Connectome for Long
... same context, contextual fear memory is inferred from an increase in freezing behavior [15]. The advantage of this task is that a single training episode produces robust memory that is easily-quantifiable and long-lasting [16]. During training wild-type (WT) mice (F1 from a cross between C57B6/N and ...
... same context, contextual fear memory is inferred from an increase in freezing behavior [15]. The advantage of this task is that a single training episode produces robust memory that is easily-quantifiable and long-lasting [16]. During training wild-type (WT) mice (F1 from a cross between C57B6/N and ...
Temporal Sequence Detection with Spiking Neurons: Towards
... active dendrites and dynamic synapses in an integrated model. For a long time, dendrites have been thought to be the structures where complex neuronal computation takes place, but only recently have we begun to understand how they operate. The dendrites do not simply collect and pass synaptic inputs ...
... active dendrites and dynamic synapses in an integrated model. For a long time, dendrites have been thought to be the structures where complex neuronal computation takes place, but only recently have we begun to understand how they operate. The dendrites do not simply collect and pass synaptic inputs ...
Online Adaptable Learning Rates for the Game Connect-4
... ~ t is a function of time t since it will be modified by the TD learning algorithm (Sec. 3.3). The vector w ~ t combines all weights from all LUTs. It can be a rather big vector, containing, e. g., 9 million weights in our standard Connect-4 implementation with 70 8-tuples. It turns out that only 60 ...
... ~ t is a function of time t since it will be modified by the TD learning algorithm (Sec. 3.3). The vector w ~ t combines all weights from all LUTs. It can be a rather big vector, containing, e. g., 9 million weights in our standard Connect-4 implementation with 70 8-tuples. It turns out that only 60 ...
pdf
... Figure 2 | Conjunctiveness and hubness in the hippocampus. (a) Representational similarity analysis (RSA) logic. Left: associative similarity contrast, with expected high regional representational similarity for comparisons of the same association, and low similarity for comparisons of different ass ...
... Figure 2 | Conjunctiveness and hubness in the hippocampus. (a) Representational similarity analysis (RSA) logic. Left: associative similarity contrast, with expected high regional representational similarity for comparisons of the same association, and low similarity for comparisons of different ass ...
Complementary roles of basal ganglia and cerebellum in learning
... LTD of parallel fiber synapses with the error signal provided by the climbing fibers. These authors also showed that the modulation of simple spikes by complex spikes is too weak to be useful for real-time motor control. Kitazawa et al. [56] analyzed the information content of complex spikes in arm- ...
... LTD of parallel fiber synapses with the error signal provided by the climbing fibers. These authors also showed that the modulation of simple spikes by complex spikes is too weak to be useful for real-time motor control. Kitazawa et al. [56] analyzed the information content of complex spikes in arm- ...
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.