The control of rostrocaudal pattern in the developing spinal cord
... the demonstration that the expression of LIM homeodomain (HD) proteins defines each of the columnar subclasses of chick MNs, prior to target innervation (Tsuchida et al., 1994). Moreover, genetic analyses have shown that LIM-HD proteins control neuronal differentiation and axonal pathfinding in both ...
... the demonstration that the expression of LIM homeodomain (HD) proteins defines each of the columnar subclasses of chick MNs, prior to target innervation (Tsuchida et al., 1994). Moreover, genetic analyses have shown that LIM-HD proteins control neuronal differentiation and axonal pathfinding in both ...
Text - ETH E
... reinforcement learning problems with delayed reinforcement (Barto, Sutton, & Anderson, 1983; Sutton & Barto, 1998; Tesauro, 1994). Examples for tasks with delayed rewards are board games such as backgammon. In such games the TD reward prediction signal codes for the chance to win and serves as the v ...
... reinforcement learning problems with delayed reinforcement (Barto, Sutton, & Anderson, 1983; Sutton & Barto, 1998; Tesauro, 1994). Examples for tasks with delayed rewards are board games such as backgammon. In such games the TD reward prediction signal codes for the chance to win and serves as the v ...
Classification using sparse representations
... Representing signals as linear combinations of basis vectors sparsely selected from an overcomplete dictionary has proven to be advantageous for many applications in pattern recognition, machine learning, signal processing, and computer vision. While this approach was originally inspired by insights ...
... Representing signals as linear combinations of basis vectors sparsely selected from an overcomplete dictionary has proven to be advantageous for many applications in pattern recognition, machine learning, signal processing, and computer vision. While this approach was originally inspired by insights ...
Computation with Spikes in a Winner-Take-All Network
... Case 3 happens only under certain initial conditions, for example, when Vk Vj or when neuron j initially receives an external input spike frequency that is higher than that to neuron k. A leaky integrate-and-fire model will ensure that all membrane potentials are discharged (Vi = 0) at the onset of ...
... Case 3 happens only under certain initial conditions, for example, when Vk Vj or when neuron j initially receives an external input spike frequency that is higher than that to neuron k. A leaky integrate-and-fire model will ensure that all membrane potentials are discharged (Vi = 0) at the onset of ...
Mapping Function Onto Neuronal Morphology
... where and are the position of the peak and the half-width of the function, respectively. The functions for the branching asymmetry, a(l), and angle, (l), were analogous to Eq. 4. As in biological neurons, the dendritic diameter was not allowed to go ⬍0.2 m. The size of the soma was fixed at 20 ...
... where and are the position of the peak and the half-width of the function, respectively. The functions for the branching asymmetry, a(l), and angle, (l), were analogous to Eq. 4. As in biological neurons, the dendritic diameter was not allowed to go ⬍0.2 m. The size of the soma was fixed at 20 ...
Association of type I neurons positive for NADPH
... morphology, these neurons exhibit distinct heterogeneity. In one subpopulation, the cell body is narrowly attenuated (7–10 μm in width). These have bipolar dendrites, extending 300–800 μm from the cell body. One or both of the dendrites is often closely associated with blood vessels and tends to be ...
... morphology, these neurons exhibit distinct heterogeneity. In one subpopulation, the cell body is narrowly attenuated (7–10 μm in width). These have bipolar dendrites, extending 300–800 μm from the cell body. One or both of the dendrites is often closely associated with blood vessels and tends to be ...
what distinguishes conscious experience from unconscious processes
... Yet a person experiences a single coherent conscious experience, and we remain wondering how these separate experience producing areas build that. If the different areas of the brain can conjure the stuff of experience, the qualia, independently, it might imply that there is something central to whi ...
... Yet a person experiences a single coherent conscious experience, and we remain wondering how these separate experience producing areas build that. If the different areas of the brain can conjure the stuff of experience, the qualia, independently, it might imply that there is something central to whi ...
Artificial neural network
In machine learning and cognitive science, artificial neural networks (ANNs) are a family of statistical learning models inspired by biological neural networks (the central nervous systems of animals, in particular the brain) and are used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown. Artificial neural networks are generally presented as systems of interconnected ""neurons"" which exchange messages between each other. The connections have numeric weights that can be tuned based on experience, making neural nets adaptive to inputs and capable of learning.For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. After being weighted and transformed by a function (determined by the network's designer), the activations of these neurons are then passed on to other neurons. This process is repeated until finally, an output neuron is activated. This determines which character was read.Like other machine learning methods - systems that learn from data - neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition.