Bayesian Networks for Logical Reasoning
... knowledge. In this example the logical axioms have probability 1, but not so the hypotheses. A causal Bayesian network is sometimes just called a causal network . The above example may be called a logical Bayesian network or simply a logical network . As in the causal case, if logical implication is ...
... knowledge. In this example the logical axioms have probability 1, but not so the hypotheses. A causal Bayesian network is sometimes just called a causal network . The above example may be called a logical Bayesian network or simply a logical network . As in the causal case, if logical implication is ...
Mechanisms of Maximum Information Preservation in the Drosophila
... Recent investigations have shown that PNs are broadly tuned to odors, whereas ORNs are narrowly tuned [3,30]. In ORNs, most odor responses cluster in the weak end of their dynamic range. In PNs, however, odor responses are distributed more uniformly throughout their dynamic range. This is a result o ...
... Recent investigations have shown that PNs are broadly tuned to odors, whereas ORNs are narrowly tuned [3,30]. In ORNs, most odor responses cluster in the weak end of their dynamic range. In PNs, however, odor responses are distributed more uniformly throughout their dynamic range. This is a result o ...
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... but limited in tolerance to the object transformations. The histogram-based descriptors, for an example, the SIFT features, show great tolerance to the object transformations but such feature detectors are not complete in the sense that they do not take all useful information while trying to achieve ...
... but limited in tolerance to the object transformations. The histogram-based descriptors, for an example, the SIFT features, show great tolerance to the object transformations but such feature detectors are not complete in the sense that they do not take all useful information while trying to achieve ...
Module 2
... easily handle them. The storage also presents another problem but searching can be achieved by hashing. The number of rules that are used must be minimised and the set can be produced by expressing each rule in as general a form as possible. The representation of games in this way leads to a state s ...
... easily handle them. The storage also presents another problem but searching can be achieved by hashing. The number of rules that are used must be minimised and the set can be produced by expressing each rule in as general a form as possible. The representation of games in this way leads to a state s ...
Does the Conventional Leaky Integrate-and
... Now consider the case in Fig. 4c, where the refractory period is added and compare it with Fig. 4b. In the presence of refractory period the firing pattern is sparser because in the refractory period, a number of incoming spikes are neglected, so the ‘effective’ input to the neuron will be smaller, ...
... Now consider the case in Fig. 4c, where the refractory period is added and compare it with Fig. 4b. In the presence of refractory period the firing pattern is sparser because in the refractory period, a number of incoming spikes are neglected, so the ‘effective’ input to the neuron will be smaller, ...
Perception Processing for General Intelligence
... DeSTIN’s pattern library, so that the pattern library contains not only classic DeSTIN centroids, but also these corresponding ”image grammar” style patterns. Then, when a new input comes into a DeSTIN node, in addition to being compared to the centroids at the node, it can be fed as input to the pr ...
... DeSTIN’s pattern library, so that the pattern library contains not only classic DeSTIN centroids, but also these corresponding ”image grammar” style patterns. Then, when a new input comes into a DeSTIN node, in addition to being compared to the centroids at the node, it can be fed as input to the pr ...
The Involvement of Recurrent Connections in Area CA3 in
... connections per cell is still much lower than in reality, although the degree of connectivity is higher. This does not pose a problem, however, as long as the cells a particular neuron connects to can be considered from a functional point of view as a random sample, the number of connections per neu ...
... connections per cell is still much lower than in reality, although the degree of connectivity is higher. This does not pose a problem, however, as long as the cells a particular neuron connects to can be considered from a functional point of view as a random sample, the number of connections per neu ...
slides
... learn the general task requirement as well as the specific location of the hidden platform Spatial pretraining can separate the two kinds of learning Rats first made familiar with the general task requirements and subsequently trained after receiving NMDAR antagonists could learn the spatial locatio ...
... learn the general task requirement as well as the specific location of the hidden platform Spatial pretraining can separate the two kinds of learning Rats first made familiar with the general task requirements and subsequently trained after receiving NMDAR antagonists could learn the spatial locatio ...
DECODING NEURONAL FIRING AND MODELING NEURAL
... is greatly simplified if the integration time used to define the firing rate is longer than any intrinsic neuronal time scale affecting firing, as discussed in section 13. In this case, measured and calculated static properties can be used to construct a dynamic model. Although firing-rate models ar ...
... is greatly simplified if the integration time used to define the firing rate is longer than any intrinsic neuronal time scale affecting firing, as discussed in section 13. In this case, measured and calculated static properties can be used to construct a dynamic model. Although firing-rate models ar ...
Deep Learning for Artificial General Intelligence
... Note that many of the xt may refer to different, time-varying activations of the same unit in sequence-processing RNNs (“unfolding in time”). During an episode, the same weight may get reused over and over again in topology-dependent ways, e.g., in RNNs, or in convolutional NNs. This is called weigh ...
... Note that many of the xt may refer to different, time-varying activations of the same unit in sequence-processing RNNs (“unfolding in time”). During an episode, the same weight may get reused over and over again in topology-dependent ways, e.g., in RNNs, or in convolutional NNs. This is called weigh ...
Article
... In this model, there is no explicit or linear measure of time like the tics of an oscillator or a continuously ramping firing rate (see Discussion; Durstewitz, 2003). Instead, time is implicitly encoded in the state of the network— defined not only by which neurons are spiking, but also by the prope ...
... In this model, there is no explicit or linear measure of time like the tics of an oscillator or a continuously ramping firing rate (see Discussion; Durstewitz, 2003). Instead, time is implicitly encoded in the state of the network— defined not only by which neurons are spiking, but also by the prope ...
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... such as growing a network from small to large [36], and the nonstationarity of the development process [35]. The term “connectionist” has been misleading, diverting attention to only network styles of computation that do not address how the internal representations emerge without human programmer’s ...
... such as growing a network from small to large [36], and the nonstationarity of the development process [35]. The term “connectionist” has been misleading, diverting attention to only network styles of computation that do not address how the internal representations emerge without human programmer’s ...
Network Self-Organization Explains the Statistics and
... To estimate the probability distribution governing excitatory-toexcitatory synaptic strengths we bin connection strengths and divide the number of occurrences in each bin by the bin size. The bin sizes are uniform on the log scale. To mimic experimental procedures [15], very small synapses (v0:01) a ...
... To estimate the probability distribution governing excitatory-toexcitatory synaptic strengths we bin connection strengths and divide the number of occurrences in each bin by the bin size. The bin sizes are uniform on the log scale. To mimic experimental procedures [15], very small synapses (v0:01) a ...
Evolving a Roving Eye for Go - Neural Network Research Group
... games, such as Othello and chess, machines cannot come close to master level performance in Go. Not only are there generally more moves possible in Go than other two-player, complete information, zero-sum, games, but it is also difficult to formulate an accurate evaluation function for board positio ...
... games, such as Othello and chess, machines cannot come close to master level performance in Go. Not only are there generally more moves possible in Go than other two-player, complete information, zero-sum, games, but it is also difficult to formulate an accurate evaluation function for board positio ...
paper - Gatsby Computational Neuroscience Unit
... The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there ex ...
... The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there ex ...
Learning - TU Chemnitz
... Of several responses made to the same situation, those which are accompanied or closely followed by satisfaction to the animal will, other things being equal, be more firmly connected with the situation, so that, when it recurs, they will be more likely to recur; those which are accompanied or close ...
... Of several responses made to the same situation, those which are accompanied or closely followed by satisfaction to the animal will, other things being equal, be more firmly connected with the situation, so that, when it recurs, they will be more likely to recur; those which are accompanied or close ...
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... features of each neuron are developed, instead of hand-crafted, so that the limited resource is optimally used. This approach helps us learn more about the biological stereo vision, and also yields results superior to those of traditional computer vision approaches, e.g., under weak textures. Develo ...
... features of each neuron are developed, instead of hand-crafted, so that the limited resource is optimally used. This approach helps us learn more about the biological stereo vision, and also yields results superior to those of traditional computer vision approaches, e.g., under weak textures. Develo ...
Motor learning in man: A review of functional and clinical studies
... 2.2.2. Premotor cortex Activation in the lateral premotor cortex (PMC) during the early stages of skill learning has been observed bilaterally (Deiber et al., 1997; Inoue et al., 1997) and has been reported to be prominent in the right side (Deiber et al., 1997; Jenkins et al., 1994; Inoue et al., 1 ...
... 2.2.2. Premotor cortex Activation in the lateral premotor cortex (PMC) during the early stages of skill learning has been observed bilaterally (Deiber et al., 1997; Inoue et al., 1997) and has been reported to be prominent in the right side (Deiber et al., 1997; Jenkins et al., 1994; Inoue et al., 1 ...
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.