E. R. Kandel and J. R. Schwartz. Principles of neural sciences. Elsevier North Holland, 1993.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....to the cortex. Second, there is a massive feedback projection from the primary visual cortex to the thalamus. Unfortunately, the computational role of this feedback pathway is still a mystery. For a textbook account of these and other well known facts about the visual pathway, see, e.g. [68]. It is interesting to note that a sizable part of the brain is in fact devoted to vision. In macaque monkeys, for example, it has been estimated that approximately half of the neocortex is concerned with this task [152] It seems likely that the fraction is somewhat smaller for humans, but this ....

....smaller for humans, but this does not change the fact that an enormous amount of machinery is dedicated to vision. This further reinforces our sense of the complexity of the problem of vision. 3. 3 Neural receptive fields The main information processing workload of the brain is carried by neurons [68]. The majority of neurons communicate by action potentials (also called spikes) stereotyped electrical impulses traveling down the axons of neurons. Although in principle information could be carried by very complex patterns of spikes [147] in practice most research to date 19 time stimulus ....

E. R. Kandel, J. H. Schwartz, and T. M. Jessell, Principles of neural science. McGraw-Hill, 4th ed., 2000.

....something that a neuron does before sigmoidal nonlinearity. What is the relationship between IPCA and our brain A clear answer is not available yet, but Rubner Schulten [21] proved that the well known mechanisms of biological Hebbian learning and lateral inhibition between nearby neurons [22] (pages 1020, 376) result in an incremental way of computing PCA. Although we do not claim that the computational steps of the proposed CCIPCA can be found physiologically in the brain, the link between incremental PCA and the developmental mechanisms of our brain is probably more intimate than we ....

E.R. Kandel, J.H. Schwartz, and T.M. Jessell, Eds., Principles of Neural Science, Appleton and Lance, Norwalk, Connecticut, third edition, 1991. 16

....factor. However, since k (1 k ) 1 k , 105) mesocolumnar averaging washes out discrimination of A G 1,3 from A G 2,4 unless these possess additional distinguishing functional features. Similar calculations are proposed to further investigate phenomena as encountered in habituation [170]. For instance, a system considered was N I = 1. 75, G = 0. 25, v G = 0. 1 mV, and J G = 0 (no long ranged connectivity) 15] This system was synaptically modified about its most probable firing state by A E3 = 0. 01 tanh F , e.g. numerically equivalent to a substantial ....

E.R. Kandel and J.H. Schwartz, Principles of Neural Science (Elsevier, New York, NY, 1981).

....to the cortex. Second, there is a massive feedback projection from the primary visual cortex to the thalamus. Unfortunately, the computational role of this feedback pathway is still a mystery. For a textbook account of these and other well known facts about the visual pathway, see, e.g. [68]. It is interesting to note that a sizable part of the brain is in fact devoted to vision. In macaque monkeys, for example, it has been estimated that approximately half of the neocortex is concerned with this task [152] It seems likely that the fraction is somewhat smaller for humans, but this ....

....smaller for humans, but this does not change the fact that an enormous amount of machinery is dedicated to vision. This further reinforces our sense of the complexity of the problem of vision. 3. 3 Neural receptive fields The main information processing workload of the brain is carried by neurons [68]. The majority of neurons communicate by action potentials (also called spikes) stereotyped electrical impulses traveling down the axons of neurons. Although in principle information could be carried by very complex patterns of spikes [147] in practice most research to date 19 time stimulus ....

E. R. Kandel, J. H. Schwartz, and T. M. Jessell, Principles of neural science. McGraw-Hill, 4th ed., 2000.

....of such organization is unknown. In cortex, each neuron has a receptive field centered at a specific location, while earlier cortex has neurons with a smaller receptive field than those in a later cortex. An overview of sensory maps is available in an excellent work by Kandel, Schwartz and Jessell [4]. Is sensory mapping completely determined by human genes The answer is negative. As early as 1970, Blakemore and Cooper [5] reported that the kittens visual cortex does not have cells sensitive to edged orientations that they did not observe, if they lived in a controlled environment after ....

E. R. Kandel, J. H. Schwartz, and T. M. Jessell, eds., Principles of Neural Science, Appleton and Lance, Norwalk, Connecticut, third ed., 1991.

....solving problems. We will here very brie y review the basic components and functionality of the vertebrate central nervous system (CNS) whose details were revealed around 1940 with the emergence of the electron microscope. For more extensive literature on this subject we refer the reader to ref. [1]. The human brain contains approximately 10 neurons. These can be of many di erent types, but most of them have the same general structure (see g. 2.1) The cell body or soma receives electric input signals to the dendrites by means of ions. The interior of the cell body is negatively charged ....

....exactly the collective behavior of the neurons that is interesting from the point of view of intelligent data processing. For a general textbook on ANN see e.g. ref. 2] 2.2. 1 Basics The basic computational entities of an ANN are the neurons v i , which can take real values within the interval [0,1] (or [ 1,1] Sometimes the even simpler binary neuron s i is used, where s i = f0; 1g (or f1; 1g) These are of course simpli cations of the biological neurons described in the previous section. Common to most neural models is a local updating rule v i = g( ij v j i ) 2.1) where v j ....

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E.R. Kandel and J.H. Schwartz, Principles of Neural Science, 3 ed., Elsevier Publ. :New York (1991).

....to a well known class. The aim of this algorithm is to produce classification rules for assigning new examples to classes. There are numerous methods which use inductive learning, including the k nearest neighbor, Bayesian techniques, discriminant analysis, neural network, decision tree and others [16,20,24]. In deductive learning algorithms, the classification rules are given a priori by the interaction with the decision maker, or the expert. From these rules we determine the assignment classes of the objects. The expert system [2] and the rough set approaches [15] belong to this kind of learning. ....

E. R. Kandel, J. H. Schwartz, T. M. Jesse, Principles of Neural Science, Elsevier, New York (1991).

....very limited coverage of the brain area, especially for CSF (Fig. 1) Intuitively, this will not give a good estimate of the true tissue intensity distributions (which is needed by a supervised classifier) for two reasons: Brain tissue, as seen in aMRI, is not homogeneous throughout the brain [8]. MRI artifacts, such as intensity non uniformity (INU) introduce additional spatial variations in the measured tissue signal. Thus, sampling at a lower # would be beneficial for the intensity distribution estimation; however, a lower # also means more false positives. Our novel contribution ....

Kandel, E.R., et al. : Principles of Neural Science. fourth edn. McGraw Hill (2000)

....many preparations, particularly in experiments on sensory or motor systems. A classical example is the stretch receptor in a muscle spindle [13] The number of spikes emit ted by the receptor neuron increases with the force applied to the muscle. Another example is the touch receptor in the leech [11]. The stronger the touch stimulus, the more spikes occur during a stimulation period of 500 ms. These classical results show that the experimenter as an external observer can evaluate and classify neuronal firing by a spike count measure but is this really the code used by neurons in the brain In ....

....to be bound. This is because these neurons are not distinct from one another. Figure A.i: The refined kernel 104 to gives rose[If 0V [2] Figure A.2: The refined neural network Sample [1.1,2.1] 1,2] 3.2, 4.2] 3, 4] 5.1, 6.1] 5, 6] 7.1,8.1] 7,8] 9. 1, 10.1] 9, 10] 11.1, 12.1] [11, 12] [13.1, 14.1] 13, 14] 15.1, 16.1] 15, 16] 17.1,18.1] 17,18] 19.1,20.1] 19,20] 21.1,22.1] 21,22] 23.1,24.1] 23,24] 25.1,26.1] 25,26] 27.1,28.1] 27,28] 30.1,29.1] 29,30] 20.1,19.1] 19,20] 22.1,21.1] 21,22] 24.1,23.1] 23,24] 26.1,25.1] 25,26] 28.1,27.1] ....

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Kandel E. C. and Schwartz J. H. Principles of Neural Science. Elsevier New York, 3rd edition, 1991.

....abilities. Otherwise, they can have little idea of what the other is sensing and responding to. Vision is one important sensory modality for human interaction, and the one focused on in this paper. We endow our robots with visual perception that is human like in its physical implementation [2]. Human eye movements have a high communicative value. For example, gaze direction is a good indicator of the locus of visual attention. Knowing a person s locus of attent ion reveals what that person currently considers behaviorally relevant, which is in turn a powerful clue to their intent. ....

E. R. Kandel, J. H. Schwarz and T. M. Jessel. Principles of Neural Science, 4 th Edition, McGraw-Hill, 2000.

....information and perceive images on global level. The smoothing process is partly due to the nature of the channel between the retina and the visual cortex, where the neighboring receptors converge into one ganglion, while the groups of ganglions converge to single neurons in the visual cortex [13]. However, the amount of averaging depends on the spatial frequencies, spatial relationships among the colors, size of the observed objects and the global context. For example, the capability of human visual system to distinguish different colors drops rapidly for high spatial frequencies. ....

E. R. Kandel, J. H. Schwartz, and T. M. Jessel, Principles of neural science, Appleton and Lange, New York, 1991.

....young (30 year old) normal male. This simulated data is identical to what is publicly available through the BrainWeb Internet interface [1, 14] It should be mentioned that these (BrainWeb) simulations assume homogeneous tissue MR properties throughout the brain. In practice, this is not the case [10, 41, 50]. However, these simulations provide a realistic approximation for testing MRI classi cation methods. Moreover, partial volume (which reduces the cluster separation in feature space) is a challenge for any classi cation method. A simple classi er was used for estimating the tissue fractions in ....

....Kawashima, Sendai, Japan. 14 coverage of the brain area (especially for CSF) Intuitively, this will not give a good estimate of the true tissue intensity distributions desired for the nal supervised classi cation stage, for two reasons: Brain tissue is not homogeneous throughout the brain [10, 41, 50]. MRI artifacts, such as INU, introduce additional spatial variations in the measured tissue signal. Thus, sampling at a lower would be bene cial for the intensity distribution estimation; however, a lower also means more false positives. The contribution of this thesis is a way to ....

E. R. Kandel, J. H. Schwartz, and T. M. Jessel. Principles of Neural Science. McGraw Hill, fourth edition, 2000.

....We have a quantitative and a qualitative change of synapses in the neural network. It involves neurotransmitters, second messengers and differential gene expression, that is an interaction of shortterm, long term and very long term processes. For more details about these processes see for example [22]. Since dendrites are the cell parts which have most of the synaptic contacts with other cells, changes of synapses and dendrites are closely related. In [33] it is pointed out that dendrites play a more active role than traditionally believed; they are dynamic structures whose growth depends on ....

....or learning during the lifetime of an individual which is generally referred to as ontogenetic learning [3] within the context of evolution. From the viewpoint of cellular and molecular neurobiology ontogenetic learning is the fine tuning of developed synapses by experience. According to [22] this commences approximately at the end of the first stage of neurodevelopment (prenatal) after initial synapse formation, controlled by genetic and early developmental processes, is completed. The outcome of learning is memory, either implicit and unconscious (e.g. for motor skills) or explicit ....

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E.R. Kandel, J.H. Schwartz, and T.M. Jessell. Principles Of Neural Science. McGraw--Hill, 4th edition, 2000.

....changes occur, thus determining the number of distinct color categories that can be perceived. For this, the hue saturation wlue color space was used as it specifies a given color in terms of its hue, purity and brilliance attributes that have been found to give a perceptual description of color [20]. The details of these experiments are described in Appendix A and will not be elaborated here, except to mention the following. The entire spectrum of computer recordable colors (224 colors) was 5 quantized into 7200 bins corresponding to a 5 degree resolution in hue, and 10 levels of ....

E. Kandel and J. Schwartz, Principles of Neural Science, Elsevier: New York, 1985, Chapter 30, pp.386-388.

....that are not modelled. Part II Topographic Map Formation in the Visual System 59 Chapter 4 A Model of Topographic Map Formation Topographically ordered projections from one region to another are a prominent feature in the brain. The presence of such maps has been known since the 1920 s [31], when the spatial locations of electrical responses to sensory stimulation in the somatosensory cortex were discovered to lie in an orderly arrangement relativetothe location of the stimulated body part. These maps have since been found throughout the brain. The process of map formation in ....

....as it does during the learning trials. As is seen in biology, the axonal arbors become more narrowover time, and the final receptive fields are strongest when input stimulation is at the center of the receptive field, and taper off in strength as input stimulation moves away from their center [31]. 4.5 The Role of Target Activity In constructing this simulation, wehave considerable latitude in modelling the target activity pattern, o i (t) In the adult animal, patterns of activity tend to follow acharacteristic mexican hat pattern, with short range excitatory and mid range inhibitory ....

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Eric R. Kandel and James H. Schwartz. Principles of Neural Science. Elsevier, 1985.

.... such as the projections of rotating wire frame objects [162] or random dot stereograms [73] Physiological evidence for module independence is provided by the existence of separate sub cortical and cortical pathways carrying different kinds of visual information ( 142] 102] 174] see also [77], p.381) This independence is probably not complete: single units that respond to several different cues, such as stereo disparity and direction of motion, are found in some visual cortical areas ( 35] cf. 27] 2.1.1 Stereopsis Since the image on the retina of an eye, or in a camera, is ....

E. R. Kandel and J. H. Schwartz. Principles of neural science. Elsevier, New York, 1985.

....occur, thus determining the number of distinct color categories that can be perceived. For this, the hue saturation value color space was used as it specifies a given color in terms of its hue, purity and brilliance attributes that have been found to give a perceptual description of color [20]. The details of these experiments are described in Appendix A and will not be elaborated here, except to mention the following. The entire spectrum of computer recordable colors (224 colors) was 5 quantized into 7200 bins corresponding to a 5 degree resolution in hue, and 10 levels of ....

E. Kandel and J. Schwartz, Principles of Neural Science, Elsevier: New York, 1985, Chapter 30, pp.386-388.

....of neuron firings is filtered, full wave rectified, and then integrated over a 33 ms period to correspond with a video camera frame rate. It has been shown in many studies that movement in a direction is determined not by one neuron, but by the summed spike activities of many neural units [1 3]. By accurate placement of electrodes, it is possible to acquire signals that have correlations with muscle groups in the arm wrist area. Accurate pattern recognition techniques are the key to controlling the robotic arm. The integrated neural data, used as input into the interface, is time ....

Kandel, E.R., Schwartz, J.H., and Jessell, T.M. Principles of Neural Science, 3rd edt., Elsevier Science Pub. Co., Chapters 1,19 20,35-40., 1991

.... psychological processes that lead to completion of given task are reflected by a measurable change of electric potentials, generated by the appropriate neuronal systems [Stejskal et al. 1993] The recording is based on the averaging of EEG potentials time locked to some specific stimulus in time [Kandel et al. 2000, Polich, 1998] The ERP trace can be divided into early and late components. The early, or exogenous components, are more related to the physical structure of the stimulus and to the modality used. The late components are relatively independent on the structure and modality of the stimulus. It is ....

Kandel, E. R., Schwartz, J. H., and Jessell, T. M., editors (2000). Principles of neural science. MgGraw-Hill, New York.

....levels is recognized sometimes, but is usually neglected in practice. Nevertheless, a thoughtful look at a few pictures readily convinces that in many cases edge detection, segmentation, three dimensional reconstruction and image understanding are impossible without each other. Neuroanatomical [43] and psychophysical [48] evidence suggests that in primates these processes influence each other, and take place simultaneously or at least overlap in time. A neighbor field of image processing poses other problems: image enhancement, restoration, compression that may seem di#erent and even ....

Kandel, E. R., J. H. Schwartz, and T. M. Jessell (eds.): 1991, Principles of Neural Science. Elsevier, third edition.

....given that signi#cant delays occur during nerve conduction; for instance, the conduction speed in local cortical circuits Neural Computation 13, 1003 1021 (2001) c 2001 Massachusetts Institute of Technology 1004 J. J. Fox, C. Jayaprakash, D. Wang, and S. R. Campbell is less than 1 m sec (Kandel, Schwartz, Jessell, 1991; Murakoshi, Guo, Ichinose, 1993) With the commonly reported 40 Hz oscillation frequency, such a conductionspeed impliesthat connected neurons that are 1 mm apart have a time delay of more than 4 of the period of oscillation. Extensive experimental #ndings of neural synchrony have stimulated ....

Kandel, E. R., Schwartz, J. H., & Jessell, T. M. (1991). Principles of neural science (3rd ed.), New York: Elsevier.

....C, which can act over other neuron connections. 2 The Biological Support The ordinary biological neuron has many dendrites usually branched, which receive information from other neurons, and an axon which transmits the processed information, usually by propagation of an action potential [1, 5]. The axon is divided into several branches, which make synapses onto the dendrites and cell bodies of other neurons. The nervous cells influence others by (a) excitation, that is, they contribute to produce impulses on other cells, and (b) inhibition, that is, they prevent the releasing of ....

....impulses on other cells, and (b) inhibition, that is, they prevent the releasing of impulses on other cells. The predominant type of synapse in the mamma lian brain is chemical, and operates through the releasing of a transmitter substance from the pre synaptic to the post synaptic terminal [5 7]. This release occurs in active zones, inside pre synaptic terminals. Certain chemical synapses lack active zones, so synaptic actions between these cells are slower and more diffuse. The coupling result of a neurotransmitter with a receptor makes the post synaptic cell releases a protein. The ....

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Kandel, E. R., Schwartz, J. H., and Jessell, T. M. (eds.): Principles of Neural Science. 4th ed., McGraw-Hill, 2000.

..... 51 3 1 Introduction Oscillations arise throughout the central nervous system [47] 38] 79] 89] 9] These oscillations have been implicated in the generation of sleep rhythms, epilepsy, parkinsonian tremors, sensory processing, and learning [30] 39] 76] 79] [40]. Oscillatory behavior also arises in such activities as respiration, movement, and secretion [13] 10] Models for the relevant neuronal networks often exhibit a rich structure of dynamic behavior. The behavior of even a single cell can be quite complicated [11] 13] 82] 83] 31] an ....

....to the envelope of a burst s rapid spikes. We only consider a form of coupling between cells that represents chemical synapses, since this is the primary means by which neurons 4 in the central nervous system communicate with each other. There are, however, many forms of synaptic coupling [40]. It may be excitatory or inhibitory and it may exhibit either fast or slow dynamics. We will be primarily interested in whether excitatory or inhibitory coupling leads to either synchronous or desynchronous rhythms. We will demonstrate that all four combinations are possible, depending on the ....

E.R. Kandel, J.H. Schwartz, and T.M. Jessell. Principles of Neural Science. Appleton & Lange, Norwalk, Conn., 1991.

....computational model for such theories, and consequently such ideas cannot currently be used in natural language understanding or problem solving systems. This work describes an attempt to develop computational models of causal semantics based on a synthesis of results in sensory motor control #Kandel et al. 1991; Latash 1993; Sternberg 1978#, insights from structured connectionist systems #Feldman 1989# and linguistic research in CognitiveSemantics. Furthermore, we exploit the fact that the deep semantics of the causal narratives are dynamic and arise from a continuous interaction between input and ....

....that recur in sensory motor control #such as goal, periodicity, iteration, #nal state, duration, and parameters such as force and e#ort#. An x schema is a computational model that encodes such patterns. Results from research on high level sensory motor control #Sternberg 1978; Latash 1993; Kandel et al. 1991; Bernstein 1967; Arbib 1992# suggest that x schemas should be active structures capable of modeling coordinated control programs with sequential, conditional, hierarchical and concurrent actions. They should also be capable of modeling asynchronous event based control with interrupts. Wehave ....

Kandel, Eric R., James H. Schwartz, and Thomas M. Jessell#eds.# #1991# Principles of Neural Science. New York: Elsevier, third edition.

....#hand or foot#, moving it to the needed location, and initiating a cyclic scratching motion. 1 Manytypes of grasps are encapsulated as synergies. Cutkosky Howe #1990# catalogs these grasps according to their uses, and argues that motion planning involves discrete choices amongst them. 1 See Kandel et al. #1991: Chapters 37#38# for a review of these and other motor re#exes. CHAPTER 3. EXECUTING SCHEMAS FOR CONTROLLING ACTIONS 22 The restriction on arbitrary jointmovements implicit in the notion of synergies is evident from experiments in bimanual coordination. Franz et al. #1991# has shown, for ....

.... be driven by a single labelled line #i.e. axon# from cortex to a central pattern generator #CPG# which not only controls the speed of the cat s gait, but also induces a switch to a di#erent gait #e.g. trot to gallop# as required to achieve the commanded speed #Shik Orlovsky 1976# #reviewed in Kandel et al. #1991##. Parameters seem to be speci#ed separately from the coordinative structure, and often are encoded in an ensemble of neurons. For example, Georgopoulos #1993# has discovered population coding of direction and force in motor cortex of behaving monkeys. According to this scheme, a precise ....

Kandel, Eric R., James H. Schwartz,&Thomas M. Jessell #eds.# 1991. Principles of Neural Science. New York: Elsevier, third edition.

....are instantaneous [2, 3, 4, 7, 8, 23] This assumption is certainly an approximation of the physiological data: any physical process takes time to accomplish. The rising time of excitatory postsynaptic potentials, for example, ranges from a few milliseconds to a few hundred milliseconds (see [15] at page 184) Theoretically in [24] the authors have found the interesting impact of EPSP rising time courses in synchronising neuronal activities. In this paper we address the following two important issues: how the time courses of EPSPs and IPSPs affect the output of a single neuron model the ....

.... that for the same neurone the time course of 4 input EPSPs might be very different: for example, the rising time for an increasedconductance EPSP due to the opening of a channel could be a few milliseconds; but the rising time for a decreased conductance EPSP is a few hundreds milliseconds (see [15] at page 184) When ff is small, ff wave inputs can be thought of as an approximation to continuous current inputs; when ff is large they approximate Poisson inputs. Example 3 When f(t Gamma T E k ) 1 ffi I fT E k t T E K ffig we have square wave inputs and its duration time is ffi. ....

Kandel E.R., Schwartz J.H., and Jessell T.M.(1991) Principles Of Neural Science, 3ed edition, Prentice-Hall International Inc.

....phenomena are of crucial importance for computations in biological neural systems. It is not only the average spiking frequency, but also the temporal difference between spikes that matters (see e.g. Abeles 91] Aertsen 93] Bialek 92] Bair 94] Sejnowski 95] Thorpe 89] Hopfield 95] Kandel 91] Thus the communication and also the computation of biological neurons completely differs from the way in which processors in digital computers and also neurons in artificial neural networks operate. The basic mechanism, on which computations in biological neural systems are based, is the ....

E. R. Kandel, J. H. Schwartz, T. M. Jessel. (1991) Principles of Neural Science. Prentice-Hall.

....is the most di#cult transformation for methods that rely on edge detection. Blurring with DOG filter: Di#erence of Gaussians (DOG) filter, which produces a Mexican hat type receptive field, is a form of image preprocessing known to be present in early mammal Stainvas et al. 11 vision (Marr, 1982; Kandel and Schwartz, 1991). It enhances edges while smoothing the image. Standard deviations of the on and o# center (positive and negative Gaussians) were 1 and 2 (pixels) respectively. Out of focus blur: This blur is a common type of degradation when a lens with a circular aperture is defocused. The point spread ....

Kandel, E. R. and Schwartz, J. H. (1991). Principles of Neural Science. Elsevier, New York, third edition.

....wrapping effects in stereopsis by tuning to frequencies below a certain limit which is not an inconceivable one. The center of the receptive field of a typical complex cell in the visual cortex 13 subtends and angle of about 1 ffi on the retina, which corresponds to about 0:25mm at the fovea (Kandel and Schwartz, 1981). Therefore, the spatial width of the receptive fields is about oe = 0:025cm. The human visual system can extract accurate disparity information only over a small range of depths around the fixation point, corresponding to a disparity difference on the retina of about 40 minutes of arc or 0:0167cm ....

Kandel, E. R. and Schwartz, J. H. (1981). Principles of Neural Science. Elsevier/North-Holland, New York.

....areas 42 and 43 are the primary auditory cortex, area 4 is the motor cortex, and areas 8 to 11 are the prefrontal association cortex. Later experiments have given more detailed mappings of the cerebral cortex, but Brodman s map still gives a sufficient basic insight in the cerebral structure. From [24] rise to the long axons that leave cortex to descend to the basal ganglia, the brain stem and the spinal cord. Layer 6 also contains pyramidal cells, most of which project back to the thalamus. Even though the 6 layer structure recurs in all of the cortex, the thickness of the different layers ....

....matter. The layers contain different proportions of the two main classes of cortical neurons, pyramidal and nonpyramidal cells. The pyramidal cell send long axons to other parts of the brain and down the spinal cord and are the major output neurons. Most nonpyramidal axons terminate locally. From [24] For computational reasons it is impossible to model the whole brain, but using networks of reasonable size it seems possible to model the behavior of a cortical column. That, even using only a small number of cells compared to the approximately 10 5 cells contained in a cortical column. For a ....

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E. R. Kandel, J. H. Schwartz, and T. M. Jessell. Principles of Neural Science. Appleton & Lange, third edition, 1991.

....for this kind of lexical storage therefore is content addressable memory citeHinton86a. The principle of content addressable memory has been thoroughly used in Parallel Distributed Processing methods for different purposes like biased pattern recognition and disambiguation of words or sentences [22]. More on this subject is discussed in chapter 6. Apart from the representation of the lexicon, there are several alternatives in accessing the relevant parts of the lexicon during processing. Approaches differ between linear and parallel access of items, and between selective access, guided by ....

....accessing the relevant parts of the lexicon during processing. Approaches differ between linear and parallel access of items, and between selective access, guided by contextual information or frequency, and exhaustive access of all relevant lexical items. For a discussion of different methods see [22]. 14 3.5 A Processing Paradigm Resuming the material presented in this chapter, this thesis will be founded by a language paradigm formulated by MacDonald et al. [25] Processing involves factors such as the frequency of occurrence and co occurrence of different types of information, and the ....

[Article contains additional citation context not shown here]

R.R. Kandel, J.H. Schwartz, and T.M. Jessell, editors. Principles of Neural Science. Elsevier, New York, NY, USA, third edition, 1991.

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E. R. Kandel and J. R. Schwartz. Principles of neural sciences. Elsevier North Holland, 1993.

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E. R. Kandel, J. H. Schwartz, and T. M. Jessel, Principles of Neural Science. Prentice-Hall, New Jersey, 1991.

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E. R. Kandel, T. M. J. Schwartz, and T. M. Jessel, editors. Principles of neural sciences. Elsevier, New York, 1991.

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Kandel, E. R., & Schwartz, J. H. (1985). Principles of neural science (2 ed.). Amsterdam, The Netherlands: Elsevier.

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E.R. Kandel, Schwartz J.H., and Jessel T.M., editors. Principles of Neural Science. McGraw Hill, New York, 4th edition, 2000. Part V: Perception.

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E. R. Kandel, Principles of Neural Science, E. Kandel and J. Schwartz, Eds. Norwalk, CT: Appleton and Lange, 1991.

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E. Kandel & J. Schwartz. Principles of Neural Science. Elsevier/North-Holland, New York, 1981.

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Eric R. Kandel, James H. Schwartz, Principles of Neural Science, Elsevier North-Holland, 1981

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E. R. Kandel, J. H. Schwartz, and T. H. Jessel, editors. Principles of Neural Science. Elsevier, Amsterdam, Holland, 3rd edition, 1991. 135

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E.R. Kandel, J.H. Schwartz, and T.M. Jessell (Eds.), Principles of Neural Science, 3rd edn., Elsevier, 1991.

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Kandel, E. R., Schwartz, J. H. and Jessell T. M. (Eds.) (1991). Principles of Neural Science. New York, Elsevier.

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E. Kandel, J.H. Schwartz, "Principles of Neural Science," Elsevier, New

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E. R. Kandel and J. H. Schwartz, editors. Principles of Neural Science, chapter 33, 34, 38. Elsevier, 2nd edition, 1985.

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Kandel, E.R ., Schwartz, J.H. and Jessell, T.M. Principles of neural science. Appleton & Lange, third edition, 1991.

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Kandel E.R., Schwartz J.H., and Jessell T.M.(1991) Principles Of Neural Science, 3ed edition, Prentice-Hall International Inc.

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E. R. Kandel, J. H. Schwarz and T.M. Jessel. Principles of Neural Science, 4 th Edition, McGraw-Hill, 2000.

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