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It grasps the essential features of neural coding and yet is simple enough for theoretic analysis. [43] Experimental studies have revealed that this coding paradigm is widely used in the sensory and motor areas of the brain. For example, in the visual area medial temporal (MT), neurons are tuned to the direction of object motion. [44]
One of the implications of the efficient coding hypothesis is that the neural coding depends upon the statistics of the sensory signals. These statistics are a function of not only the environment (e.g., the statistics of the natural environment), but also the organism's behavior (e.g., how it moves within that environment).
According to the theory, such a mental model is used to predict input signals from the senses that are then compared with the actual input signals from those senses. Predictive coding is member of a wider set of theories that follow the Bayesian brain hypothesis.
In both cases, the mathematical theory can be developed for continuous time, which is then, if desired for the use in computer simulations, transformed into a discrete-time model. The relation of noise in neuron models to the variability of spike trains and neural codes is discussed in Neural Coding and in Chapter 7 of the textbook Neuronal ...
Models of neural computation are attempts to elucidate, in an abstract and mathematical fashion, the core principles that underlie information processing in biological nervous systems, or functional components thereof. This article aims to provide an overview of the most definitive models of neuro-biological computation as well as the tools ...
Common coding theory is a cognitive psychology theory describing how perceptual representations (e.g. of things we can see and hear) and motor representations (e.g. of hand actions) are linked. The theory claims that there is a shared representation (a common code) for both perception and action.
Neural Simulation Language (NSL) has been developed to provide a simulation system for large-scale general neural networks. It provides an environment to develop an object-oriented approach to brain modeling. NSL supports neural models having as basic data structure neural layers with similar properties and similar connection patterns.
This approach, with its emphasis on behavioral outcomes as the ultimate expressions of neural information processing, is also known for modeling sensory and motor decisions using Bayesian decision theory. Examples are the work of Landy, [15] [16] Jacobs, [17] [18] Jordan, Knill, [19] [20] Kording and Wolpert, [21] [22] and Goldreich. [23] [24] [25]