# Tag Info

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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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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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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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The weights in an artificial neural network are an approximation of multiple processes combined that take place in biological neurons. Myelination plays a role, but not a major one. Weights in artificial neural networks can be positive or negative numbers. Weight magnitude. The magnitude of a weight is analogous to a combination of increased dendritic ...

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The weight matrix is typically considered to be a strength of connectivity metric between nodes in the parallel computronium model that neural networks are based on. That fact is fairly evident when you investigate ANN learning algorithms. For instance, the backpropagation algorithm for feedforward networks is designed to strengthen a string of associations ...

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

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If anyone ever finds this and has the same problem: The original paper has a flaw in the paper: alphac=(exp((Vd/mV-10)/11)-exp((Vd/mV-6.5)/27))/(18.975)* (Vd<=50*mV)/ms+2*exp((6.5-Vd/mV)/27)*(Vd>50*mV)/ms : Hz An errata published later changes this to: ...

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What they mean is that as long as you rotate $C_i$ and $C_j$ by the same amount, the dot product will be the same. This is rotational invariance because the dot product is invariant to coherent rotation of the relevant vectors. In neural terms, the correlation between the activity of two neurons (in a population representing a one dimensional circular ...

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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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I'm assuming that you want some kind of "computer vision" model (in that you want to be able to provide the model with input stimuli in the form of an image), and that you want to predict some kind of behaviour? (e.g., RT from a search task). Fleshing out the different processes involved is not going to be trivial, so there probably isn't a ...

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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 thought that thalamic input comes in layer 4, feed back from higher areas come through layer 1 to layer 2/3 and feed forward is sent from the deeper layers. see Canonical Microcircuits for Predictive Coding Andre Bastos, W. Martin Usrey, Rick Adams, George Mangun, Pascal Fries, and Karl Friston ...

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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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