Experimental Models of Parkinson`s Disease: Insights from Many
... erties in the catecholaminergic nervous system (13–15). 6OHDA uses the same catecholamine transport system as do dopamine and norepinephrine, leading to specific damage via oxidative stress to these neurons (14). To be neurotoxic to the brain, 6-OHDA must be administered by intracerebral or intraven ...
... erties in the catecholaminergic nervous system (13–15). 6OHDA uses the same catecholamine transport system as do dopamine and norepinephrine, leading to specific damage via oxidative stress to these neurons (14). To be neurotoxic to the brain, 6-OHDA must be administered by intracerebral or intraven ...
Uygar Sümbül - Department of Statistics
... following viral delivery of the Brainbow construct. An unsupervised algorithm is developed to automate the segmentation. • Dynamical system models of cortical activity (with Prof. Liam Paninski, Prof. John Cunningham, and Prof. Mark Churchland) Single-unit and array recordings are obtained from the ...
... following viral delivery of the Brainbow construct. An unsupervised algorithm is developed to automate the segmentation. • Dynamical system models of cortical activity (with Prof. Liam Paninski, Prof. John Cunningham, and Prof. Mark Churchland) Single-unit and array recordings are obtained from the ...
Na¨ıve Inference viewed as Computation
... and 0s) are deployed, the behaviour is that of a digital device with iterative behaviour; a connection can then be made between naı̈ve inference and computation. In fact, as Section 4 demonstrates, naı̈ve inference machines are Turing equivalent: they can model any form of computational behaviour. T ...
... and 0s) are deployed, the behaviour is that of a digital device with iterative behaviour; a connection can then be made between naı̈ve inference and computation. In fact, as Section 4 demonstrates, naı̈ve inference machines are Turing equivalent: they can model any form of computational behaviour. T ...
On Constrained Optimization Approach to Object
... minimization formulation. It allows that the knowledge of understanding the object be represented in terms of the features and dynamic behavior of its constituent parts of interest and be expressed as constraints of its optimization index. But before we get to this point, we should review the basics ...
... minimization formulation. It allows that the knowledge of understanding the object be represented in terms of the features and dynamic behavior of its constituent parts of interest and be expressed as constraints of its optimization index. But before we get to this point, we should review the basics ...
A Dynamic Knowledge Base - K
... (i.e. non stationarity condition) can be paired with the more permanent/long term asymptotic information coming from the averages and distributions (i.e. stability condition). By the interaction between the short term and long term information evolutions, we may have to design different strategies f ...
... (i.e. non stationarity condition) can be paired with the more permanent/long term asymptotic information coming from the averages and distributions (i.e. stability condition). By the interaction between the short term and long term information evolutions, we may have to design different strategies f ...
Single-trial decoding of intended eye movement goals from lateral
... analyzing neurons with low firing rates and short epochs. The quantile indexes were used as r in Eq. 1. MI was compared against a null distribution obtained by shuffling target locations and calculating MI for 1,000 different shuffles. We labeled neurons as having significant target location informa ...
... analyzing neurons with low firing rates and short epochs. The quantile indexes were used as r in Eq. 1. MI was compared against a null distribution obtained by shuffling target locations and calculating MI for 1,000 different shuffles. We labeled neurons as having significant target location informa ...
Skeletal System
... and must function over a lifetime They do not divide • As fetal neurons assume their roles as communication links in the nervous system, they lose their ability to undergo mitosis • Cells cannot be replaced if destroyed - Some limited exceptions do exist in the CNS as neural stem cells have been ide ...
... and must function over a lifetime They do not divide • As fetal neurons assume their roles as communication links in the nervous system, they lose their ability to undergo mitosis • Cells cannot be replaced if destroyed - Some limited exceptions do exist in the CNS as neural stem cells have been ide ...
Figure 1 - Journal of Neuroscience
... to a light-sensitive area, likely the SC (Schnupp and King, 1997). The borders of the SC were roughly located by recording multiunit entrainment to a pulsed (1 or 4 Hz), red LED located in front of the monkey, or ⬃20 or ⬃40° to the side (contralateral to the IC under study). Although clear light res ...
... to a light-sensitive area, likely the SC (Schnupp and King, 1997). The borders of the SC were roughly located by recording multiunit entrainment to a pulsed (1 or 4 Hz), red LED located in front of the monkey, or ⬃20 or ⬃40° to the side (contralateral to the IC under study). Although clear light res ...
Down
... responds during learning with a Gaussian firing profile around the stimulus that excites the node maximally. Each node is assigned a center of the receptive field randomly from a pool of centers covering the periodic training domain. (A) Before training all nodes have the same relative weights betwe ...
... responds during learning with a Gaussian firing profile around the stimulus that excites the node maximally. Each node is assigned a center of the receptive field randomly from a pool of centers covering the periodic training domain. (A) Before training all nodes have the same relative weights betwe ...
Down - 서울대 Biointelligence lab
... responds during learning with a Gaussian firing profile around the stimulus that excites the node maximally. Each node is assigned a center of the receptive field randomly from a pool of centers covering the periodic training domain. (A) Before training all nodes have the same relative weights betwe ...
... responds during learning with a Gaussian firing profile around the stimulus that excites the node maximally. Each node is assigned a center of the receptive field randomly from a pool of centers covering the periodic training domain. (A) Before training all nodes have the same relative weights betwe ...
the iterative reprocessing model
... are the dynamic result of an integrated set of distributed processes, each of which responds to and resolves specific computational problems (see Cunningham & Johnson, 2007). In other words, rather than corresponding to a discrete neural process, evaluation is an emergent property of multiple proces ...
... are the dynamic result of an integrated set of distributed processes, each of which responds to and resolves specific computational problems (see Cunningham & Johnson, 2007). In other words, rather than corresponding to a discrete neural process, evaluation is an emergent property of multiple proces ...
ILP turns 20 | SpringerLink
... reconstruct that computation from examples and is therefore, in its generality, a much harder problem requiring a strong inductive bias. Furthermore, declaratively there is no difference between the answers computed by an efficient program—say, quicksort—and an inefficient one—say, permutation-sort. ...
... reconstruct that computation from examples and is therefore, in its generality, a much harder problem requiring a strong inductive bias. Furthermore, declaratively there is no difference between the answers computed by an efficient program—say, quicksort—and an inefficient one—say, permutation-sort. ...
Placing prediction into the fear circuit
... signal that instructs learning, and synaptic plasticity, across CS–US pairings. Aversive USs might act as teaching signals to trigger plasticity at CS input synapses to the LA, at least in part, by causing depolarization and action potential firing in LA neurons while CS inputs are active [9,10]. Th ...
... signal that instructs learning, and synaptic plasticity, across CS–US pairings. Aversive USs might act as teaching signals to trigger plasticity at CS input synapses to the LA, at least in part, by causing depolarization and action potential firing in LA neurons while CS inputs are active [9,10]. Th ...
Noise and Coupling Affect Signal Detection and Bursting in a
... and autonomous SR that of a single neuron. In CR, the ability of noise to produce a synchronous, periodic response in the system is dependent on the noise characteristics and coupling between the neurons. When analyzed in the frequency domain, a sharp peak is produced as the system becomes more peri ...
... and autonomous SR that of a single neuron. In CR, the ability of noise to produce a synchronous, periodic response in the system is dependent on the noise characteristics and coupling between the neurons. When analyzed in the frequency domain, a sharp peak is produced as the system becomes more peri ...
CS2053
... 5. Davis E.Goldberg, “Genetic Algorithms: Search, Optimization and Machine Learning”, Addison Wesley, N.Y., 1989. 6. S. Rajasekaran and G.A.V.Pai, “Neural Networks, Fuzzy Logic and Genetic Algorithms”, PHI, 2003. 7. R.Eberhart, P.Simpson and R.Dobbins, “Computational Intelligence - PC Tools”, AP Pro ...
... 5. Davis E.Goldberg, “Genetic Algorithms: Search, Optimization and Machine Learning”, Addison Wesley, N.Y., 1989. 6. S. Rajasekaran and G.A.V.Pai, “Neural Networks, Fuzzy Logic and Genetic Algorithms”, PHI, 2003. 7. R.Eberhart, P.Simpson and R.Dobbins, “Computational Intelligence - PC Tools”, AP Pro ...
