Titlebook: Neuronal Network Dynamics in 2D and 3D in vitro Neuroengineered Systems; Monica Frega Book 2016 The Editor(s) (if applicable) and The Auth

Allure

instructive

Monica Fregavations can provide us with an answer. When little children play together, for instance, they all speak at once and seldom listen to what their neighbors are saying. Obviously, this is b~cause it is not so important what they are actually saying as that something is being said at all.978-3-642-71417-7978-3-642-71415-3

吝嗇性

vations can provide us with an answer. When little children play together, for instance, they all speak at once and seldom listen to what their neighbors are saying. Obviously, this is b~cause it is not so important what they are actually saying as that something is being said at all.978-3-642-71417-7978-3-642-71415-3

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障礙

al backgrounds. The speakers were recorded in two speaking contexts. In the first one, they read a passage, and in the second context, they talked about themselves and their families. Two metrics were used to examine rhythm in both these speaking contexts: A normalised Pairwise Variability Index (nP

加劇

2190-5053 lementation of bio-hybrid microsystems for network electroph.The bookpresents a new, powerful model of neuronal networks, consisting of athree-dimensional neuronal culture in which 3D neuronal networks are coupled tomicro-electrode-arrays (MEAs). It discusses the main advantages of thethree-dimensio

彎曲的人

Book 2016led tomicro-electrode-arrays (MEAs). It discusses the main advantages of thethree-dimensional system compared to its two-dimensional counterpart, and showsthat the network dynamics, recorded during both spontaneous and stimulatedactivity, differs between the two models, with the 3D system being bett

緯線

虛情假意

In Vitro Neuronal Networks.); then I will introduce confined or patterned neuronal networks coupled to MEAs (cf. Sect. .). Finally I will present an overview of the advantages and limitation of this in vitro bi-dimensional model (cf. Sect. .).

excursion

he Weighted Finite-State Transducer (WFST) approach. The decoding process for speech recognition is viewed as a search problem whose goal is to find a sequence of words that best matches an input speech signal. Since this process becomes computationally more expensive as the system vocabulary size i