NEURAL NETWORKS
... defer: L. differre- dis-, asunder (adv. apart, into parts, separately), ferre, to bear , to carry v.t. to put off to another time, to delay defer: L. deferre- de-, down, ferre, to bear v.i. to yield (to the wishes or opinions of another, or to authority), v.t. to submit or to or to lay before somebo ...
... defer: L. differre- dis-, asunder (adv. apart, into parts, separately), ferre, to bear , to carry v.t. to put off to another time, to delay defer: L. deferre- de-, down, ferre, to bear v.i. to yield (to the wishes or opinions of another, or to authority), v.t. to submit or to or to lay before somebo ...
Chordate evolution and the origin of craniates
... elaborated brains with paired sense organs and unique derivatives of neural crest and placodal tissues, including peripheral sensory ganglia, visceral arches, and head skeleton. The craniate sister taxon, cephalochordates, has rostral portions of the neuraxis that are homologous to some of the major ...
... elaborated brains with paired sense organs and unique derivatives of neural crest and placodal tissues, including peripheral sensory ganglia, visceral arches, and head skeleton. The craniate sister taxon, cephalochordates, has rostral portions of the neuraxis that are homologous to some of the major ...
The C. elegans Connectome Consists of Homogenous Circuits with
... These massive efforts will yield gigantic networks composed of millions of inter-connected neurons. This poses a genuine challenge: how to analyze these perplexing connectomes such that functional principles can be extracted based on connectivity data alone. Various approaches and theories had been ...
... These massive efforts will yield gigantic networks composed of millions of inter-connected neurons. This poses a genuine challenge: how to analyze these perplexing connectomes such that functional principles can be extracted based on connectivity data alone. Various approaches and theories had been ...
![arXiv:1604.00289v3 [cs.AI] 2 Nov 2016](http://s1.studyres.com/store/data/003558001_1-ebbf1f5b4b7daca920a4ae5f6fdbbe61-300x300.png)
Chapter 2 Intrinsic Dynamics of an Excitatory
... A pair of an excitatory and an inhibitory neurons, coupled to each other and evolving in discrete time intervals, is one of the simplest systems capable of showing chaotic behavior. This has guided the choiceof this system for extensive study in this thesis. The present chapter examines the discrete ...
... A pair of an excitatory and an inhibitory neurons, coupled to each other and evolving in discrete time intervals, is one of the simplest systems capable of showing chaotic behavior. This has guided the choiceof this system for extensive study in this thesis. The present chapter examines the discrete ...
AutomatedUnderstandingofFinancialStatements2
... amount found in the second column. It is also necessary to distinguish between a "period end date" that applies only to column one, and a period end date that applies to columns three and four. The easiest way to do this is to build "cases" into the language that are analogous to the cases used in ...
... amount found in the second column. It is also necessary to distinguish between a "period end date" that applies only to column one, and a period end date that applies to columns three and four. The easiest way to do this is to build "cases" into the language that are analogous to the cases used in ...
A flexible genetic toolkit for arthropod neurogenesis
... with patterning cells with neurogenic potential (module A) and further early steps include the patterning (module B), proliferation (module C) and movement (module D) of neural progenitors. Variants of these modules can be detected across the animal kingdom and allow conclusions to be drawn on the m ...
... with patterning cells with neurogenic potential (module A) and further early steps include the patterning (module B), proliferation (module C) and movement (module D) of neural progenitors. Variants of these modules can be detected across the animal kingdom and allow conclusions to be drawn on the m ...
Spatio-Temporal Reasoning and Context Awareness
... anything abnormal. From the application point of view, this obviously removes the need for the system at all. For machine learning methods based on real data it is a challenge to solve this problem, and there are two typical approaches. The first is to select the training data very carefully to ensu ...
... anything abnormal. From the application point of view, this obviously removes the need for the system at all. For machine learning methods based on real data it is a challenge to solve this problem, and there are two typical approaches. The first is to select the training data very carefully to ensu ...
Neurophysiological investigation of the basis of the fMRI signal
... such potentials, including single-spike responses and ®eld potentials, whereby the latter relate well not only to spike activity but also to subthreshold integrative processes in areas such as dendrites that are otherwise inaccessible. Microelectrode recording methods have been used extensively to o ...
... such potentials, including single-spike responses and ®eld potentials, whereby the latter relate well not only to spike activity but also to subthreshold integrative processes in areas such as dendrites that are otherwise inaccessible. Microelectrode recording methods have been used extensively to o ...
Feeding in an Artificial Insect
... also proven to be essential for explaining the behavior of simpler animals as well. Unfortunately, the explanatory utility of these internal factors is limited by the fact that they are hypothetical constructs, inferred by the theorist to intervene between stimulus and action in order to account for ...
... also proven to be essential for explaining the behavior of simpler animals as well. Unfortunately, the explanatory utility of these internal factors is limited by the fact that they are hypothetical constructs, inferred by the theorist to intervene between stimulus and action in order to account for ...
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.