# Tag Info

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The field of study you should focus on is the one for which you have already identified in your paragraph above which is EEG based "brain-computer interface". EEG signals are compared by their "features". Each of the signal you have provided above have different features. These features can be mean, variance, frequency, kurtosis, skewness of each of the ...

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One way the biological plausibility of an artificial neural network could be assessed is to look at how much a neural network abstracts away from the behavior of real neurons. For instance, it is common in psychology and machine learning to use a sigmoidal activation function to determine the output of a node. If biological plausibility is a concern, one ...

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The location of a sound is defined on three dimensions: distance, elevation, and azimuth. When the distance between a listener and a sound source is changed there is a change in the overall level as well as the relative levels of direct and reverberant sound energy. When the elevation is changed the overall level and the direct to reverberant ratio say ...

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Cognitive Architectures The description most closely matches the concept of a cognitive architecture. Whereas I would say most empirical cognitive science focuses on isolating cognitive functions or behavioral substrates, cognitive architectures are relatively unique because they attempt to run bottom-up simulations of interdependent sets of cognitive ...

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Not intuitive, but to save the value of a curve plotted in a graph: Right click the graph window, and choose "Pick Vector" Click on the desired curve (it should change color i.e. to red) In the NEURON Main Menu > Vector > Save to File Type in the file name > Save File will have two columns, first one for X-axis values (i.e. time) and second for the Y-axis ...

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To my knowledge, with respect to the context of the question, the first neural-like model of computations capable of learning – or, for that matter, computational model of neural processing and learning – has been put forward in McCulloch/Pitts (1943), as is also acknowledged in some of the texts about Turing's unorganized machines (›A-/B-type neural ...

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Due to my newness to the field, I can only talk about comparisons of biological plausibility when discussing the Neural Engineering Framework (NEF) and functional modeling. What is missing from this answer is a purely bottom-up modelling perspective in the same vein as the Blue-Brain project, but I'll leave that to another user. One of the claims driving ...

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Amos (2000) and Monchi et al. (2000) use the similar approach of assigning each card attribute to a node and using mutual inhibition to choose the right one. 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 ...

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Interesting question. "Ontology" is often used in confusing and polyvalent ways, so let's start by clearing up the terminology very quickly for those who aren't intimate with the various different meanings. What does "ontology" mean? Broadly, ontology the field is the philosophical study of being. An ontology is a method for establishing what beings or ...

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Short answer Mostly Cl- is disregarded in calculations of the resting membrane potential and action potential voltage changes, because it is less important for the neural membrane characteristics than Na+ and K+. Background In some neurons Cl- is not actively transported. In terms of the resting membrane potential, Cl- hence settles its gradient passively ...

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In normal neurons, Chloride's reversal potential is near the resting potential for the neuron and also happens to be near the leak conductance reversal potential for the neuron. While not exactly the same these three are sometimes confused. The difference between these three reversal potentials is subtle. Chloride Reversal Potential: is the potential ...

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Rigotti et al. have a model of the wisconsin card sorting task using a neural network and compare it with data from prefrontal cortex http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2967380/

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Both ACT-R and Spaun are modular, and could be extended to include capabilities of each other. Comparing the functionalities of the two architectures by a simple checklist is not the most appropriate way to compare them. Here are some points to consider: 1) ACT-R is primarily symbolic, whereas Spaun is a neural network. ACT-R is actually making a ...

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Typing the following into the NEURON console will reset and run the simulation: run()

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Are there more complete criterion? The criterion provided above provide a high-level view of the goals that should be sought after by any cognitive system, however this does not address how a system should pursue this goal. For a more complete evaluation of the methods and tools that should be used, please see Terry Stewart's PHD thesis A Methodology for ...

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Information and signal detection theory are commonly applied to cognitive situations. Examples include TSA agents searching for weapons among carry-on bags and sonar operators attempting to discern ships from fish. In addition, information theory itself is highly general, and has been proven to apply across disciplines. So I do think the premise is valid. ...

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My lab uses the Semantic Pointer Architecture (where vectors are used as pointers between different dimensions, for more information check out "How to Build a Brain" by Chris Eliasmith) which is a Vector Symbolic Architecture (where sparse vectors represent symbols) to model working memory in a biologically plausible manner. So far this has been used in ...

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It is generally not possible to measure where a single calcium ion is in three dimensional space. However, it is possible to measure relative calcium ion concentration across a population of neurons using Two-Photon-Microscopy. I stress relative because it does not give you an exact concentration of Calcium in terms of micro-molar concentration. However 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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Unintuitive, but it's in the right click > View sub-menu: Pan/translate: Right click the chart View > Translate Left click drag will pan around the chart Zoom: Right click the chart View > Zoom In/Out Left click drag horizontally or vertically to zoom the x or y axis Zoom in on a region: Right click the chart View > NewView Left click on the chart ...

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The only modelling method that I know of for creating large-scale biologically based models is the Neural Engineering Framework (NEF). The NEF is basically a framework for associating functional computations and dynamic systems to biologically plausible populations of neurons. Given this foundation, advanced applications linking behaviour to neural function ...

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0: As you suggested, use a temporal filter to get a smoothed rate representation 1.a: Clean up each frame by using (e.g.) a morphological opening then closing step 1.b: Locate the centres of bumps, perhaps using non-maximum suppression. 1.c: Choose a reasonable activity threshold for neurons to still be "inside" the bump: Maybe 80% of the distance between ...

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Long-term memory storage and forgetting are the two main features that are found in ACT-R, but are missing from Spaun. Currently, Spaun only has a working memory and a fixed long-term memory; it lacks the ability to store new items in a long-term memory. This is currently being tackled by trying to create better hippocampal models and encoder learning ...

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The Neurological Engineering Framework does not explicitly state a mechanism for memory. There is no "hard-drive" in the brain for easy retrieval and access. Rather, memory is captured in the connection weights between neural populations and the dynamics of the network. In the Hierarchical Reinforcement Learning example, linked to in the previous question, ...

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To review how neurons encode information, please check out these class notes review encoding. In those notes, you'll notice the intercept $J_{bias}$ and the maximum firing rate $\alpha$ are randomly selected when encoding functions in large populations of neurons. These variations can account for heterogeneity in attributes of neurons.

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At this point in time, the difference in neurotransmitter types affect the synaptic time constant (i.e. the filter on the incoming spike train) between neurons in Nengo. See Neural Engineering p.112 and these notes (see the section called "Biologically plausible filter") from a course covering the book. Neurotransmitters are also leveraged more ...

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