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... in bursts when stimuli predicting future primary reinforcement occur, through DA-dependent strengthening of corticostriatal synapses. After learning, the presentation of a reward-predicting stimulus would lead to DA burst firing as a result of indirect excitation from the striosomes. The arrival of ...
... in bursts when stimuli predicting future primary reinforcement occur, through DA-dependent strengthening of corticostriatal synapses. After learning, the presentation of a reward-predicting stimulus would lead to DA burst firing as a result of indirect excitation from the striosomes. The arrival of ...
Sensory Receptors, Neuronal Circuits for Processing Information
... extending through its core. Surrounding this are multiple concentric capsule layers, so that compression anywhere on the outside of the corpuscle will elongate, indent, or otherwise deform the central fiber. Now study Figure 46–3, which shows only the central fiber of the pacinian corpuscle after al ...
... extending through its core. Surrounding this are multiple concentric capsule layers, so that compression anywhere on the outside of the corpuscle will elongate, indent, or otherwise deform the central fiber. Now study Figure 46–3, which shows only the central fiber of the pacinian corpuscle after al ...
Measuring the degree of Synonymy between Words using
... the WordNet as described in the next paragraph, and |T | denotes the number of word pairs in T . Intuitively, Equation 3 compares the semantic relations that exist between A and B (expressed using lexical patterns), against the semantic relations that typically exist between synonymous words. If the ...
... the WordNet as described in the next paragraph, and |T | denotes the number of word pairs in T . Intuitively, Equation 3 compares the semantic relations that exist between A and B (expressed using lexical patterns), against the semantic relations that typically exist between synonymous words. If the ...
Modeling Human Communication Dynamics
... labels [4]. It is a significant change from previous approaches that only examined individual modalities, ignoring the synergy between speech and gestures. The task of the LDCRF model is to learn a map ping between a sequence of observations x 5 5 x1, x2, c, xm 6 and a sequence of labels y 5 5 y1, y ...
... labels [4]. It is a significant change from previous approaches that only examined individual modalities, ignoring the synergy between speech and gestures. The task of the LDCRF model is to learn a map ping between a sequence of observations x 5 5 x1, x2, c, xm 6 and a sequence of labels y 5 5 y1, y ...
The Neural Basis of the Object Concept in Ambiguous and
... activated by the same bar is synchronized as well. Finally, if the bars have different colors, the oscillations are de-synchronized. Applying the hypothesis that an object is represented by synchronous oscillation, the patterns of synchrony in the first eigenmodes of the four stimulations are to be ...
... activated by the same bar is synchronized as well. Finally, if the bars have different colors, the oscillations are de-synchronized. Applying the hypothesis that an object is represented by synchronous oscillation, the patterns of synchrony in the first eigenmodes of the four stimulations are to be ...
Synchronization and coordination of sequences in two neural
... 关Eqs. 共2a兲 and 共2b兲 with gi = 0兴. Although a detailed analysis is possible for N = 3 关9兴, this is not the case for more complex networks. When N ⬎ 3, the system may have several different heteroclinic orbits according to the strength of inhibition. This has been demonstrated to be a powerful strateg ...
... 关Eqs. 共2a兲 and 共2b兲 with gi = 0兴. Although a detailed analysis is possible for N = 3 关9兴, this is not the case for more complex networks. When N ⬎ 3, the system may have several different heteroclinic orbits according to the strength of inhibition. This has been demonstrated to be a powerful strateg ...
Revised October 2009
... h(D, σ), Ih , It i where h(D, σ), Ih i and h(D, σ), It i are classical L-structures such that Ih ⊆ It . Thus we can think of a here-and-there structure M as similar to a first-order classical model, but having two parts, or components, h and t that correspond to two different points or “worlds”, ‘he ...
... h(D, σ), Ih , It i where h(D, σ), Ih i and h(D, σ), It i are classical L-structures such that Ih ⊆ It . Thus we can think of a here-and-there structure M as similar to a first-order classical model, but having two parts, or components, h and t that correspond to two different points or “worlds”, ‘he ...
Industry characteristics and operations efficiency of joint
... certainly answer the question whether industry models improve predictability compared to “general” models. Independently on the chosen bankruptcy prediction method, the set of variables and the industry of the study, the evaluation of the tool predictability – classifier – is an essential part. Acco ...
... certainly answer the question whether industry models improve predictability compared to “general” models. Independently on the chosen bankruptcy prediction method, the set of variables and the industry of the study, the evaluation of the tool predictability – classifier – is an essential part. Acco ...
Teacher Guide
... will differ from the multimeter used to measure voltage drops across the individual components. The current registered on the ammeter should be the exact same value that is listed as flowing through each segment of the axon. The voltage across the battery is _____ V. (Using a standard DC power sour ...
... will differ from the multimeter used to measure voltage drops across the individual components. The current registered on the ammeter should be the exact same value that is listed as flowing through each segment of the axon. The voltage across the battery is _____ V. (Using a standard DC power sour ...
CHAPTER TWO
... neural networks learning; supervised learning and unsupervised learning. Also this chapter discusses some advanced neural networks learning and problems using neural networks. ...
... neural networks learning; supervised learning and unsupervised learning. Also this chapter discusses some advanced neural networks learning and problems using neural networks. ...
Computational physics: Neural networks
... mann Machines [9]. We will study important properties of such networks such as transients, equilibrium, ergodicity and periodicity in section 2.3. An exact description of transient and stationary behavior for stochastic neural networks is not possible in general. In some special cases, however, one ...
... mann Machines [9]. We will study important properties of such networks such as transients, equilibrium, ergodicity and periodicity in section 2.3. An exact description of transient and stationary behavior for stochastic neural networks is not possible in general. In some special cases, however, one ...
Neural and Computational Mechanisms of Action Processing
... The observation of view-dependence fits nicely with example-based visual recognition mechanisms (see the Section ‘Theoretical models’). However, it has to be noted that the same population of neurons can be simultaneously tuned to multiple parameters, e.g. to the view and different grip types. In ad ...
... The observation of view-dependence fits nicely with example-based visual recognition mechanisms (see the Section ‘Theoretical models’). However, it has to be noted that the same population of neurons can be simultaneously tuned to multiple parameters, e.g. to the view and different grip types. In ad ...
Interfacing Real-Time Spiking I/O with the SpiNNaker neuromimetic
... silicon retina: it can be seen that the most active neurons are those along the line edges (column 7 and 11) as the robot moves forward. The rest of the input layer is subject to random noise. Fig.6b shows the mean activity in the sub-sampler layer: as the WTA mechanism takes place, we can see that ...
... silicon retina: it can be seen that the most active neurons are those along the line edges (column 7 and 11) as the robot moves forward. The rest of the input layer is subject to random noise. Fig.6b shows the mean activity in the sub-sampler layer: as the WTA mechanism takes place, we can see that ...
Extended Liquid Computing in Networks of Spiking Neurons
... Many algorithms have been developed over the years, but we will not give more details about them (see [4]). Indeed, computations on attractors networks like RNNs show a certain number of limitations. First, a great number of attractor, i.e. a system of very high-dimensionality is needed to store inf ...
... Many algorithms have been developed over the years, but we will not give more details about them (see [4]). Indeed, computations on attractors networks like RNNs show a certain number of limitations. First, a great number of attractor, i.e. a system of very high-dimensionality is needed to store inf ...