Neural states

Bechtel, W. and Mundale, J. (1999), Multiple realizability revisited linking cognitive and neural states , Philosophy of Science, 66, 175-207. 

Enteric polymers can be coated from aqueous latexes or from aqueous solutions that are produced by solubilizing the polymer via pH neutralization with the addition of an alkali or organic base. Typical neutralizing agents used to create aqueous solutions of enteric polymers include ammonia sodium hydroxide, triethanolamine 2-amino-2-methyl-1-propanol and ammonium hydrogen carbonate. In most cases acid preireal-ment is required to convert the enteric polymer from its salt state back to the neural state to achieve enteric functionality of the polymer however, it has been reported that acid... 

It is common place now (even amongst those who consider themselves reductionists) to think that mental properties are multiply realizable with respect to then-neural realizers. So type identical mental states can be realized by different neural states. Even within a single individual, the mental state of, say, being in pain, could be realized by a number of different brain states. There seems to be no one-to-one correspondence between mental types and physical types. 

First view Appearance properties are properties of internal entities of some sort, such as sensations or neural states of the visual system. 

The first reason that might be offered is that the immediate causes of phenomenal states are neural states. There is a direct causal dependence between the phenomenal and the neural. So, of course, if you fix what goes on in the brain, you fix what goes on at the level of phenomenology. 

Bechtel, WiUiam, and Jennifer Mundale. 1999. Multiple realizability revisited Linking cognitive and neural states. Philosophy of Science 66 175-207. 

The local dynamics of tire systems considered tluis far has been eitlier steady or oscillatory. However, we may consider reaction-diffusion media where tire local reaction rates give rise to chaotic temporal behaviour of tire sort discussed earlier. Diffusional coupling of such local chaotic elements can lead to new types of spatio-temporal periodic and chaotic states. It is possible to find phase-synchronized states in such systems where tire amplitude varies chaotically from site to site in tire medium whilst a suitably defined phase is synclironized tliroughout tire medium 51. Such phase synclironization may play a role in layered neural networks and perceptive processes in mammals. Somewhat suriDrisingly, even when tire local dynamics is chaotic, tire system may support spiral waves... 

A challenging task in material science as well as in pharmaceutical research is to custom tailor a compound s properties. George S. Hammond stated that the most fundamental and lasting objective of synthesis is not production of new compounds, but production of properties (Norris Award Lecture, 1968). The molecular structure of an organic or inorganic compound determines its properties. Nevertheless, methods for the direct prediction of a compound s properties based on its molecular structure are usually not available (Figure 8-1). Therefore, the establishment of Quantitative Structure-Property Relationships (QSPRs) and Quantitative Structure-Activity Relationships (QSARs) uses an indirect approach in order to tackle this problem. In the first step, numerical descriptors encoding information about the molecular structure are calculated for a set of compounds. Secondly, statistical and artificial neural network models are used to predict the property or activity of interest based on these descriptors or a suitable subset. 

Recently, several QSPR solubility prediction models based on a fairly large and diverse data set were generated. Huuskonen developed the models using MLRA and back-propagation neural networks (BPG) on a data set of 1297 diverse compoimds [22]. The compounds were described by 24 atom-type E-state indices and six other topological indices. For the 413 compoimds in the test set, MLRA gave = 0.88 and s = 0.71 and neural network provided... 

Neural networks have been proposed as an alternative way to generate quantitative structure-activity relationships [Andrea and Kalayeh 1991]. A commonly used type of neural net contains layers of units with connections between all pairs of units in adjacent layers (Figure 12.38). Each unit is in a state represented by a real value between 0 and 1. The state of a unit is determined by the states of the units in the previous layer to which it is connected and the strengths of the weights on these connections. A neural net must first be trained to perform the desired task. To do this, the network is presented with a... 

A key featui-e of MPC is that a dynamic model of the pi ocess is used to pi-edict futui e values of the contmlled outputs. Thei-e is considei--able flexibihty concei-ning the choice of the dynamic model. Fof example, a physical model based on fifst principles (e.g., mass and energy balances) or an empirical model coiild be selected. Also, the empirical model could be a linear model (e.g., transfer function, step response model, or state space model) or a nonhnear model (e.g., neural net model). However, most industrial applications of MPC have relied on linear empirical models, which may include simple nonlinear transformations of process variables. 

Terry, P.A. and D.M. Himmelhlau, Data Rectification and Gross Error Detection in a Steady-State Process via Artificial Neural Networks, Indushial and Engineeiing Chemistiy Reseaieh, 32, 199.3,. 3020-3028. (Neural networks, measurement test)... 

Vision is vital for human activities, and eyes are very sensitive to a number of toxic insults induced by chemical compounds. The most serious outcome is permanent eye damage which may be so severe as to cause loss of vision. The eye consists of the cornea and conjunctiva, the choroid, the iris, and the ciliary body. It also contains the retina, which is of neural origin, and the optic nerve. The retina contains photoreceptors, a highly specific light-sensitive type of neural tissue. The eye also contains the lens and a small cerebrospinal fluid system, the aqueous humor system, that is important for the maintenance of the steady state of hydration of the lens and thus the transparency of the eye. 

Technical term for properties of electrical or neural circuits (flip-flop switch) to rest in two distinct states while avoiding intermediate states (e.g., behavioral state sleep-wake transitions). 

Smooth muscle cell activity is in general under neural control. Thus, the many transmitters of the autonomic nervous system are paired with receptors on the smooth muscle cell membrane. One of the current questions about smooth muscle function is What intracellular processes are the different transmitters modulating in the smooth muscle cells, in addition to their effects on the contractile state ... 

Aqueous solubility is selected to demonstrate the E-state application in QSPR studies. Huuskonen et al. modeled the aqueous solubihty of 734 diverse organic compounds with multiple linear regression (MLR) and artificial neural network (ANN) approaches [27]. The set of structural descriptors comprised 31 E-state atomic indices, and three indicator variables for pyridine, ahphatic hydrocarbons and aromatic hydrocarbons, respectively. The dataset of734 chemicals was divided into a training set ( =675), a vahdation set (n=38) and a test set (n=21). A comparison of the MLR results (training, r =0.94, s=0.58 vahdation r =0.84, s=0.67 test, r =0.80, s=0.87) and the ANN results (training, r =0.96, s=0.51 vahdation r =0.85, s=0.62 tesL r =0.84, s=0.75) indicates a smah improvement for the neural network model with five hidden neurons. These QSPR models may be used for a fast and rehable computahon of the aqueous solubihty for diverse orgarhc compounds. 

The 2D model was built from a wide array of descriptors, including also E-state indices, by Simulations Plus [89], The model is based on the associative neural network ensembles [86, 87] constructed from n=9658 compounds selected from the BioByte StarList [10] of ion-corrected experimental logP values. The model produced MAE = 0.24, r = 0.96 (R. Fraczkiewicz, personal communication). 

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