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 rules from neural coding, neural computation, body structure, communications thought, regulate conception, dynamics, and likelihood conception. This synthesis, they argue, permits novel theoretical and functional insights into the functioning of neural platforms. Such insights are pertinent to experimental and computational neuroscientists and to engineers, physicists, and computing device scientists attracted to how their quantitative instruments relate to the brain.The authors current 3 rules of neural engineering in keeping with the illustration of indications by means of neural ensembles, alterations of those representations via neuronal coupling weights, and the combination of keep an eye on thought and neural dynamics. via special examples and in-depth dialogue, they make the case that those guiding ideas represent an invaluable thought for producing large-scale versions of neurobiological functionality. A software program package deal written in MatLab to be used with their technique, in addition to examples, direction notes, routines, documentation, and different fabric, can be found on the net.
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Extra resources for Neural engineering: computation, representation, and dynamics in neurobiological systems
Recently, a consensus has begun to develop about the state of theories in neuroscience; there aren’t any. Or at least there aren’t any good ones (Churchland and Sejnowski 1992; TLFeBOOK 24 Chapter 1 Marder et al. 1997; Stevens 1994; Crick and Koch 1998; see also Stevens 2000 or many of the other ‘Viewpoints’ in the same issue). Neuroscience is, in other words, “data rich, but theory poor” (Churchland and Sejnowski 1992, p. 16). 4, to continue the metaphor, can put a few coins into theory’s purse.
As we show, describing population coding more-or-less independently of the speciﬁcs of individual neuron function provides the ﬂexibility to model many levels of representational detail concurrently in a single model. , the neurons), does not necessarily result in difﬁcult-to-analyze representations. This result is not too surprising if we again consider the analogy between neurons and transistors. , the transistors). 1). 2). , has a range of neuron properties). TLFeBOOK 12 Chapter 1 In other words, population activity can be linearly decoded to give an increasingly accurate indication of what was originally (nonlinearly) encoded, with an increasing number of heterogeneous neurons.
Perhaps the easiest way to see why these principles constitute a theory is to compare them to a paradigmatic theory. Consider Newton’s laws of motion. Newton’s laws constitute a theory of motion for a number of reasons. First, they unify what was, until Newton, considered two distinct kinds of motion; celestial and sublunar. Second, Newton’s laws are constructed to be consistent with the known data about the motion of objects, gathered largely by Galileo. Hence his laws are data-driven. Third, Newton’s laws allow for the prediction of planetary orbits, changes in tides, and the ﬂattening of the earth at the poles.