Knockdown of the Dyslexia-Associated Gene
... that was rescued by expression of exogenous Kiaa0319 (Paracchini et al. 2006). Control transfection animals received a scrambled sequence control of Kiaa0319 shRNA, also previously used, that contained 6 bases in the sequence scrambled to render the shRNA inactive in terms of reducing Kiaa0319 expre ...
... that was rescued by expression of exogenous Kiaa0319 (Paracchini et al. 2006). Control transfection animals received a scrambled sequence control of Kiaa0319 shRNA, also previously used, that contained 6 bases in the sequence scrambled to render the shRNA inactive in terms of reducing Kiaa0319 expre ...
Knockdown of the Dyslexia-Associated Gene
... that was rescued by expression of exogenous Kiaa0319 (Paracchini et al. 2006). Control transfection animals received a scrambled sequence control of Kiaa0319 shRNA, also previously used, that contained 6 bases in the sequence scrambled to render the shRNA inactive in terms of reducing Kiaa0319 expre ...
... that was rescued by expression of exogenous Kiaa0319 (Paracchini et al. 2006). Control transfection animals received a scrambled sequence control of Kiaa0319 shRNA, also previously used, that contained 6 bases in the sequence scrambled to render the shRNA inactive in terms of reducing Kiaa0319 expre ...
Neural mechanisms underlying the evolvability of behaviour
... underlying the development of nervous system complexity include rules that enable novel structures to be incorporated. Furthermore, neural dynamics allow the generation of multiple activity patterns. Thus, precisely because it is complex, the nervous system exhibits features that allow for and even ...
... underlying the development of nervous system complexity include rules that enable novel structures to be incorporated. Furthermore, neural dynamics allow the generation of multiple activity patterns. Thus, precisely because it is complex, the nervous system exhibits features that allow for and even ...
Zebrafish foxd3 is selectively required for neural crest specification
... et al., 2000). At 48 hpf, the expression of these markers indicated a reduction of peripheral glia within the cranial ganglia as well as those associated with axons of the lateral line system (Figs. 1L, P; and data not shown). The development of neural crest-derived chromatophores is delayed in sym1 ...
... et al., 2000). At 48 hpf, the expression of these markers indicated a reduction of peripheral glia within the cranial ganglia as well as those associated with axons of the lateral line system (Figs. 1L, P; and data not shown). The development of neural crest-derived chromatophores is delayed in sym1 ...
Neural tube defects and abnormal brain development in F52
... First, the overall brain size was reduced, although the body weight was not significantly lower than that of wild-type littermates. Second, the six cortical layers were intact, but the cortex was thinner and its staining appeared to be more intense (Fig. 3). Finally, all four ventricles were enlarge ...
... First, the overall brain size was reduced, although the body weight was not significantly lower than that of wild-type littermates. Second, the six cortical layers were intact, but the cortex was thinner and its staining appeared to be more intense (Fig. 3). Finally, all four ventricles were enlarge ...
Module 3 and 4 Practice Test
... b. delayed by the refractory period. c. an all-or-none response. d. dependent on neurotransmitter molecules. e. primarily electrical rather than chemical. ____ ...
... b. delayed by the refractory period. c. an all-or-none response. d. dependent on neurotransmitter molecules. e. primarily electrical rather than chemical. ____ ...
Reinforcement Learning Using a Continuous Time Actor
... affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. ...
... affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. ...
An Imperfect Dopaminergic Error Signal Can Drive Temporal
... What are the physiological changes that take place in the brain when we solve a problem or learn a new skill? It is commonly assumed that behavior adaptations are realized on the microscopic level by changes in synaptic efficacies. However, this is hard to verify experimentally due to the difficulti ...
... What are the physiological changes that take place in the brain when we solve a problem or learn a new skill? It is commonly assumed that behavior adaptations are realized on the microscopic level by changes in synaptic efficacies. However, this is hard to verify experimentally due to the difficulti ...
Stochastic dynamics as a principle of brain function
... contribution to the outcome that is reached, in that this noise is a factor in a network with a finite (i.e., limited) number of neurons. The spiking noise can be described as introducing statistical fluctuations into the finite-size system. It is important that the outcome that is reached, and not jus ...
... contribution to the outcome that is reached, in that this noise is a factor in a network with a finite (i.e., limited) number of neurons. The spiking noise can be described as introducing statistical fluctuations into the finite-size system. It is important that the outcome that is reached, and not jus ...
Race modulates neural activity during imitation
... race effects in the brain mainly using two types of tasks: action observation without imitation or simply looking at the faces of own-race and other-race individuals. Action observation studies have included observing the hand actions (Désy and Théoret, 2007) and hand gestures of own-race and other- ...
... race effects in the brain mainly using two types of tasks: action observation without imitation or simply looking at the faces of own-race and other-race individuals. Action observation studies have included observing the hand actions (Désy and Théoret, 2007) and hand gestures of own-race and other- ...
The Neuropsychology of Sigmund Freud
... Freud begins with a first postulate. He calls this inertia, which in many respects is similar to what we today know as homeostasis. Inertia is homeostasis in its baldest form: an organism, when stimulated, attempts to get rid of that stimulation, i.e., to return to the unstimulated condition. By inv ...
... Freud begins with a first postulate. He calls this inertia, which in many respects is similar to what we today know as homeostasis. Inertia is homeostasis in its baldest form: an organism, when stimulated, attempts to get rid of that stimulation, i.e., to return to the unstimulated condition. By inv ...
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.