# Tag Info

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Very many references may easily be found with a Google search for "mathematical model memory". Probably the most classic and iconic reference is Atkinson and Shiffrin (1965), which is also described on Wikipedia. Its three components and their relationships are nicely encapsulated in this figure: Many other, lesser-known mathematical models of memory exist, ...

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This very much depends on what, exactly, you're trying to do. EEG measurements tend to be extremely reliable, but the inferences one may draw on mental state are not necessarily so. EEG-driven BCI overwhelmingly relies on machine learning to correctly classify signals into a finite number of categories and act upon them. Typically, you'll do something ...

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As you have already hinted at, the issue is controversial. I could leave it at that and say "no, there is no consensus", and it would be a true answer, but it wouldn't be satisfying, wouldn't it? Instead, I'll briefly define the topic, give a few examples, and then a few recent criticisms. My answer will be weighted somewhat towards "cognition" instead of "...

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Humans actually exhibit both slow and fast learning and they have somewhat different properties. One distinction is between "declarative" memory (for example, facts like "tigers have stripes" or "Paris is the capital of France") and "procedural" learning (such as perceptuo-motor skills like riding a bike or playing a musical instrument). Declarative memory ...

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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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Biological Plausibility of Back-Prop No, the algorithm of back-prop (BP) isn't biologically plausible. However, there are other means which involve propagating the error through multiple layers of neurons in a feed-forward network which are biologically plausible. But before we evaluate these substitutions, let's review why back-prop isn't biologically ...

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The Hodgkin and Huxley model of neuronal firing is based on non-linear differential equations. A significant portion of research on sensation and perception is based on such models.

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Introduction Your thoughts seem to straddle panpsychism and computationalism. It is also possible you are just raising a question about physicalism: "if mental thoughts are a result of physical interactions, then why would consciousness be limited to things with brains?". Well, the short answer is that it's fundamentally not, but neither is a ...

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I have participated in NIPS, CNS, and COSYNE at least a couple times each. In fact, I have participated in all 3 last year. COSYNE is the smallest conference, but it's growing fast. It's a great conference because it has a good balance between experimentalists and theorists. It takes an extended abstract (2 pages). It emphasizes the systems aspect of the ...

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I think this recent paper fits your requirements. It considers biological plausibility by showing that the number of neurons required in the proposed method is within a reasonable size for the human brain, and dismisses a series of unreasonable models. Specifically, they create a neural network using the Neural Engineering Framework (NEF) and the Semantic ...

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There is no difference between "computational neuroscience" and "theoretical neuroscience" in practice. The two are almost always used interchangeably. Neuroinformatics, like bioinformatics, is more about managing data and designing analysis software (that's always somehow integrated with data storage and management). Generally, it is informational ...

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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 ...

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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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Read dayan and abbot "theoretical neuroscience" Learn differential equations Know the relationship between voltage, current, resistance and conductance Differential equations is absolutely essential though. you don't need to learn to solve them (the computer will do that for you), you just need to learn to know what they mean. How do researchers ...

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Cortical columns are groups of neurons in the brain that are oriented perpendicularly to the cortical surface. Cells within a column respond to the same stimulus property (Fig. 1). For example, primary visual cortex columns extract small bars with a specific orientation. A single cortical column consists of six distinct layers of neurons. The upper three ...

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Motion perception This article on motion perception might be a good start. pure motion perception is referred to as "first-order" motion perception and is mediated by relatively simple "motion sensors" in the visual system, that have evolved to detect a change in luminance at one point on the retina and correlate it with a change in luminance at ...

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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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Here's a quick answer from general background knowledge, not from any specific knowledge of "Bayesian Program Synthesis (BPS)" In general, Bayesian models can use strongly informed priors or diffuse "could be anything" priors. Strong priors specify that a lot of parameter values are very unlikely, while a few other parameter values are possible descriptions ...

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There's the naïve version of spike triggered averaging, and the sophisticated version. Both of them are consistent estimators for a linear-nonlinear system under certain conditions (Paninski, 2003). If your stimulus is $x_i$ and your spike count in a small bin is $y_i$, naïve version is $$\mathrm{STA} = \frac{1}{N} \sum_i x_i y_i$$ The sophisticated version ...

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To my knowledge, there is no adjusted RMSD. RMSD, unlike $R^2$, isn't typically used to compare models across the literature. $R^2$ represents the proportion of variance explained by the model, a construct which translates well across different experimental designs. Adjusted $R^2$ distorts this by accounting for the number of parameters in your model, but ...

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From the comments: I'm going to hazard a guess that these are neurons that are tuned to a particular direction in space and that the x-axis is the angle in multiples of π radians, particularly since these are related to the work of Georgopoulos and colleagues. Since we know these are positionally tuned neurons, you can see some other examples in this ...

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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 WCST....

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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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A repository of publicly available NEURON models can be found on ModelDB by filtering for Models that contain the Modeling Application: NEURON.

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Check out all of the videos in this playlist https://www.youtube.com/watch?v=lrppe54fixc&list=PL1hKzFfV5qJlVTjD8XWzyiaHxBZZ7iWs9&index=3 The particular video I linked gives the example of the "Papez circuit". Other interviewees mention other examples as well.

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In my opinion as a computational neuroscience researcher, graph theory has not made major inroads into computational neuroscience because we don't have good evidence for what graphs characterise brains. For example, my research revolves around how patterns of connections between neurons within local cortical circuits relate to information processing ...

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There are TONS of other Cognitive Models being researched. Specifically, see this giant comprehensive list of Cognitive Models [1]. Given such a wide array of models, it might be more helpful to focus on which framework are still under active use, in addition to Spaun and ACT-R. In "How to Build a Brain" by Chris Eliasmith, he compares Spaun with some other ...

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I also like https://bayesmodels.com/. I posted the question on twitter, you could check out the responses. Joachim Vandekerckhove suggested: Lewandowsky, S., & Farrell, S. (2010). Computational modeling in cognition: Principles and practice. Sage Publications. https://www.amazon.com/Computational-Modeling-Cognition-Principles-Practice/dp/1412970768 ...

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This question's reference to a classical computer refers to a "Turing Machine" style of computation, also known as a knowledge system, in which decisions and possible results are pre-programmed using if-statements, loops, and other logical constructs. However, most modern computer programmers and engineers are at least somewhat familiar with neural ...

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