By Chris Eliasmith
For years, researchers have used the theoretical instruments of engineering to appreciate neural platforms, yet a lot of this paintings has been performed in relative isolation. In Neural Engineering, Chris Eliasmith and Charles Anderson offer a synthesis of the disparate methods present in computational neuroscience, incorporating principles from neural coding, neural computation, body structure, communications concept, keep watch over conception, dynamics, and chance thought. This synthesis, they argue, permits novel theoretical and sensible insights into the functioning of neural platforms. Such insights are pertinent to experimental and computational neuroscientists and to engineers, physicists, and laptop scientists drawn to how their quantitative instruments relate to the brain.The authors current 3 ideas of neural engineering in accordance with the illustration of signs by means of neural ensembles, differences of those representations via neuronal coupling weights, and the mixing of regulate thought and neural dynamics. via exact examples and in-depth dialogue, they make the case that those guiding rules represent an invaluable thought for producing large-scale types of neurobiological functionality. A software program package deal written in MatLab to be used with their technique, in addition to examples, path notes, workouts, documentation, and different fabric, can be found on the net.
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Additional info for Neural engineering: computation, representation, and dynamics in neurobiological systems
Sample text
Simpler yet, the behavior of single neurons can be well-characterized in terms of their electrical properties. , Fitzhugh 1958; Rieke et al. 1997; Abbott 1994; Seung and Sompolinsky 1993). TLFeBOOK 10 Chapter 1 for about 50 years, and continue to be improved (see Bower and Beeman 1995 for a history and some recent models). By far the majority of vertebrate neurons can be understood as physical devices that convert an ‘input’ voltage change on their dendrites into an ‘output’ voltage spike train that travels down their axon.
Including the geometry of rotation in three dimensions). Thus, it is essential that we be able to account for nonlinear as well as linear transformations. 3 we discuss how to characterize nonlinear transformations in general. 5). Thus we show that assumptions about the linearity of decoding do not limit the possible functions that can be supported by neurobiological systems. This result will not be surprising to researchers familiar with current computational neuroscience. 4). Nevertheless, our analysis TLFeBOOK 18 Chapter 1 sheds new light on standard approaches.
This is because the decoding depends on the conditional probability of input given the output and on the statistics of the noise (hence our addendum). Perhaps surprisingly, linear decoding works quite well in many neural systems. Specifically, the additional information gained with nonlinear decoding is generally less than 5%. , Salinas and Abbott 1994). Furthermore, even if there initially seems to be a case in which nonlinear decoding is employed by a neural system, that decoding may, in the end, be explained by linear decoding.