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As seen in Neural Engineering Chapter 4, the main benefits to using Adaptive LIF neurons is the amount of information encoded and the ability to match neural data more realistically. Firstly, as seen in the following table (taken from Neural Engineering) Adaptive LIF neurons, by virtue of their temporally varying firing patterns encode more information per ...


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This answer supports the comment by Krysta that we are simply used to the mirrors we have and could just as easily learn to use a "true mirror". In 1950, Theodor Erismann and Ivo Kohler performed a famous self-experiment in Innsbruck, Austria. Kohler wore a pair of glasses that turned his view of the world upside down continually for 124 days (sic). After ...


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I am struggling to recall the scientific term for what you are describing, but it is a simple cognitive phenomenon that guides and informs the design of physical, real-world interfaces. Let's look at a mouse on a computer. The motion of the mouse in the real world corresponds to the motion of the cursor on the screen. As an experiment, try to use the mouse ...


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The model does not care what x is. You first create a population of 100 neurons to represent an input that will vary from -1 to 1. That input, which varies from -1 to 1, is x. For sensory neurons, the variable x would likely represent a transformation of some aspect of the sensory stimulus (e.g., the visual angle, sound level, or temperature), but it ...


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My answer is that you have the beginnings of a grasp of neuronal tuning. But the point is not that neurons can represent functions. The point is generally that neurons contain information about certain experimental conditions. Rather, neurons can represent functions, but in most cases they tend to represent something closer to propositions. The seminal ...


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Within the context of the tutorial referenced, this graph is showing the first principle of the NEF, which is that neurons approximate functions by encoding them with their firing rates. Here the input being represented it the range 1 to -1. What the graph shows is the firing rates of all the neurons given the value being represented. So say you have the ...


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Amos (2000) and Monchi et al. (2000) use similar approaches. Although their models are biologically plausible and make many neuroanatomical predictions, they are functionally implausible. Their networks are created for the unique purpose of of completing the WCST. This type of specialisation isn't found in the brain as far as I know. I'm also having a really ...



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