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Jun 22, [email protected] [email protected] Library of Congress Cataloging-in- Publication Data. Neuroscience / edited by Dale Purves [et al.]. Muscle Contrac5on. – Alpha motor neurons release. ACh. – ACh produces large EPSP in muscle fiber via nico5nic Ach receptors. – EPSP evokes muscle ac5on. AM Page iii. NEUROSCIENCE Exploring the Brain THIRD EDITION MARK F. BEAR, Ph.D. Picower Professor of Neuroscience Howard Hughes Medical.
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Bear, Mark F. Neuroscience: exploring the brain I Mark F. Bear, Barry W. Conners . Michael A. Paradiso.- 3rd ed. p.; em. Includes bibliographical references and. Figure Components of the mature mammalian retinogeniculocortical pathway. (a) A midsagittal view of a cat brain, showing the location of primary visual. Neuroscience: Exploring the brain: Fourth edition | 𝗥𝗲𝗾𝘂𝗲𝘀𝘁 𝗣𝗗𝗙 on as well sub- cortical structures including the basal ganglia (Bear et al., ; Kandel.
There are many examples where models making very different cognitive assumptions provide approximately equal levels of goodness-of-fit, so in many cognitive domains there are many competing mathematical models that make very different cognitive assumptions. For example, the results from many memory experiments can be well fit by a variety of models that make radically different cognitive assumptions e.
Neuroscience: exploring the brain
Many other examples exist in the cognitive literature, including the well known difficulty in discriminating between serial and parallel models of visual or memory search e. These problems are greatly reduced in CCN because goodness-of-fit to behavioral data is only one of a number of criteria that are used to assess model validity.
This section describes four ideal principles used during model building and testing in CCN.
It should be stressed that these are ideals. Arguably, no existing models meet all these criteria. Nevertheless, these principles are useful for helping researchers build and evaluate CCN models. The Neuroscience Ideal A CCN model should not make any assumptions that are known to contradict the current neuroscience literature.
In general, the Neuroscience Ideal means that when building or evaluating a CCN model, the validity of four types of assumptions should be considered.
First, the model should only postulate connections among brain regions that have been verified in neuroanatomical tracing studies. Second, the model should correctly specify whether each projection is excitatory or inhibitory. Third, the qualitative behavior of units in each brain region should agree with studies of single neurons in these regions.
Finally, any learning assumptions that are made should agree with existing data on neural plasticity [e. If a model makes an assumption that is known to be incompatible with the neuroscience literature then the model should be rejected, regardless of how well it accounts for behavioral data. Note that the Neuroscience Ideal does not say that a CCN model must be compatible with all existing neuroscience data. Every model is an abstraction and thus omits some of the complexity found in the natural world.
Decision neuroscience: neuroeconomics
One key to building a successful CCN model is to identify the critical features of the existing neuroscience literature that are most functionally relevant to the behavior being modeled. For example, neuroanatomical tracing studies will identify more interconnections among brain regions than typically should be included in a CCN model because for the behavior under study some of these interconnections are likely to be more functionally important than others.
A related problem is that when building most CCN models it will be necessary to make some choices for which the neuroscience literature is little help — either because there are no neuroscience data or because the existing data are equivocal. Thus, the Neuroscience Ideal should not be interpreted as suggesting that all known neuroscience must be incorporated into a CCN model or that every feature of a CCN model must be grounded in neuroscience, but rather only that the neuroscience that is incorporated should not contradict the existing neuroscience literature.
Finally, it is important to keep in mind that the Neuroscience Ideal is just that: an ideal. No model is ever correct and, even if one were eventually able to design a model fully compatible with the Neuroscience Ideal, this would make the model so complex that it likely would be impossible to test.
The Simplicity Heuristic No extra neuroscientific detail should be added to the model unless there are data to test this component of the model or the model cannot function without this detail.
This is just a version of Occam's razor. It is especially important with CCN models however, because unlike cognitive models, there will almost always be many extra neuroscientific details that one could add to a CCN model.
For example, one could use multi-compartment models of each neuron or even model specific ion channels. Adding untested complexity, even if it is neuroscientifically valid, increases the number of free parameters in the model and the computing time required for fitting.
In addition, when untested details are added, it becomes difficult to determine whether the success of the model is due to these details or to the more macroscopic properties that inspired the model in the first place. For example, if previous research shows that a behavior is dependent on the cerebellum then the cerebellum could be included in the model, even if no cerebellar data will be fit by the model e. The Set-in-Stone Ideal Once set, the architecture of the network and the models of each individual unit should remain fixed throughout all applications.
Connections between brain regions do not change from task to task, nor does the qualitative nature via which a neuron responds to input. Thus, the model's analogues of these features should also not change when the empirical application changes.
This ideal greatly reduces the mathematical flexibility of CCN models. Ideally, the overall architecture is constrained by known neuroanatomy and the model of each individual unit is constrained by existing single-unit recording data from the analogous brain region. Thus, although a CCN model will initially have many unknown constants, most of these will be set by single-unit recording data and then, by the Set-in-Stone Ideal, they will remain invariant across all applications of the model.
However, note that such revisions do not add flexibility to the existing model; rather they lead to the creation of a new model.
Thus, after a constant is set in stone, it should not be considered a free parameter in any future application of the model. The Set-in-Stone Ideal applies to the brain areas that constitute the focus of explanation of the model, and should not be expected to apply to brain regions that are either upstream or downstream from this hypothesized network.
For example, many models of learning, memory, or cognition will require visual input. In tasks where variation in behavior depends primarily on processing within the hypothesized network rather than on details of the visual processing, it is common to grossly oversimplify the model of this visual input.
For example, a simple square wave might be used, rather than a spiking model. Applying such a model to a different task, which depends on different visual inputs, might require changing the abstract model of visual input. Similarly, a model of working memory might include a greatly oversimplified model of motor responding that could change when the model is applied to a new task with different motor requirements. So in summary, the Set-in-Stone Ideal is meant to apply to the brain regions that are the focus of the model and not to the inputs or outputs of that model.
A model must make predictions at both the behavioral and neuroscience levels to classify as a CCN model. If it only makes behavioral predictions then it should be classified as a cognitive model, whereas if it only makes neuroscience predictions then it should be classified as a computational neuroscience model.
Neuroscience exploring the brain bear pdf
Thus, in general, CCN models are more ambitious than traditional cognitive models because CCN models are expected to account simultaneously for a wider range of data than cognitive models. Every CCN model should make both behavioral and neuroscience predictions, but the ideal CCN model provides good accounts of both data types.
There are many different types of neuroscience data, so there is wide latitude in how this ideal can be approached. For example, a CCN model might be tested against single-unit recording data, BOLD responses from fMRI experiments, or even behavioral data collected from animal or human participants with some specific brain lesion, or under the influence of some particular psychoactive drug.
Because a CCN model can be tested against data from multiple sources, it can gain or lose support more easily than a cognitive model. For instance, following the Neuroscience Ideal Section 4. After all, given their names, this seems a sensible assumption. Another possibility however, is that new neuroscience data could verify a previously unsupported assumption, thereby lending new support to the CCN model. Of course, similar outcomes could follow the collection of behavioral data; a model prediction could be verified or invalidated after new behavioral data are collected.
What is crucial here is that both types of data can be used to argue for or against the CCN model. This is different from cognitive or computational neuroscience models that restrict their application to only one data type. For instance, O'Reilly proposed six principles for computational models of the cortex: 1 biological realism, 2 distributed representations, 3 inhibitory competition, 4 bidirectional activation propagation, 5 error-driven learning of specific tasks and, 6 Hebbian learning of task-free statistical properties of the environment.
Most of these principles are related to the Neuroscience Ideal above in that they specify biological constraints that should be included in any CCN model of the cortex. Hence, they can be seen as an unpacking of the Neuroscience Ideal.
A decade later, Meeter et al. Specifically, they suggested that a good CCN model 1 has few assumptions, 2 is inflexible and, 3 exhibits ontological clarity. The first of these is similar to our Simplicity Heuristic, while the second is similar to the Set-in-Stone Ideal. Both sets of criteria emphasize that a model should be simple and inflexible two ideas that are mathematically related. The last criterion, ontological clarity, is similar to our Goodness-of-Fit Ideal, in that the scope of the model must be clearly established in order to determine what kind of data needs to be fit and what kind of experiments should be run.
In other words, the rules need to be set early on to specify what counts as evidence for or against the CCN model. Discussion The Neuroscience Ideal makes the relationship between computational neuroscience and CCN explicit by ensuring that no biological detail in the CCN model is inconsistent with existing neuroscientific data as in computational neuroscience.
However, following the Simplicity Heuristic, CCN models typically make simplifying assumptions about the biological details included in the model. This is because the lowest level of data usually accounted for by CCN models is single-cell recordings. Hence, although an increasing amount of data is now available about the molecular neurobiology of neurons, these data are usually not accounted for by CCN models.
This makes CCN models biologically simpler and thus more scalable than most computational neuroscience models. The Set-in-Stone Ideal is used to control the growth of complexity in the model. Theoretically, the Set-in-Stone Ideal is an implementational constraint: the same brain is used in every task. Computationally, the Set-in-Stone Ideal is used to fix the value of most constants in the model, thus drastically reducing the CCN model complexity.
Once set-in-stone, the Goodness-of-Fit Ideal states that many different types of data should be used to test the adequacy of CCN models, at least some of which are behavioral and some neuroscientific.
In addition, the Goodness-of-Fit Ideal makes the relationship between connectionism and CCN models explicit: The biological details in the CCN model should not make the model unscalable and prevent it from explaining behavioral data i. However, before doing any data fit, one needs to clearly define the scope of the model.
These principles are used to guide model development and evaluation. Because of the Neuroscience Ideal there are three areas where the mathematical details of CCN models differ substantially from traditional connectionist models.
The first fundamental difference is in how each individual unit is modeled and the second is in how learning is modeled. The third difference concerns the generation of behavior from neurally realistic individual units.
Neuroscience: exploring the brain
Sections 5 - 7 discuss some example CCN solutions to these problems. Axon arbors of X and Y retinal ganglion cells are differentially affected by prenatal disruption of binocular inputs.
Proceedings of the National Academy of Sciences , Shatz and M. Visual Neuroscience 1: , Frost and S.
Expression of a surface antigen on Y-cells in the cat lateral geniculate nucleus is regulated by visual experience. Journal of Neuroscience 8: , Callosal and ipsilateral cortical connections of the body surface representations in S-I and S-II of tree shrews.
Somatosensory Research 5: , Frost and M. The morphology of retinogeniculate X- and Y-cell axonal arbors in dark-reared cats. Experimental Brain Research , Sur, R. Weller and S. Morphology of retinogeniculate X and Y axon arbors in monocularly enucleated cats. Journal of Comparative Neurology , What does the cortex do? Behavioral Brain Science 9: , Wall, J. Kaas, M. Nelson, D.
Felleman and M. Functional reorganization in somatosensory cortical areas 3b and 1 of adult monkeys after median nerve repair: possible relationships to sensory recovery in humans.
Journal of Neuroscience 6: , Garraghty and C.This item: If it only makes behavioral predictions then it should be classified as a cognitive model, whereas if it only makes neuroscience predictions then it should be classified as a computational neuroscience model. Is this feature helpful? A brief history The field of computational neuroscience became popular with Hodgkin and Huxley's Nobel Prize winning efforts to model the generation of action potentials in the giant squid axon.
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It is actually a pleasure to read. The present article represents a natural extension and summary of this earlier work. The authors have written a text that makes complex material easily understandable.
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