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9

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 ...


8

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 ...


7

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 ...


4

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 ...


4

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 ...


4

If you have a physics background, you may be particularly interested in Sparse Distributed Memory, a model that provides a number of psychologically plausible characteristics, and is also neuroscientifically plausible. The model and some of its characteristics are summarized in this paper. Many great references have been provided by Nick Stauner, but ...


4

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 ...


4

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 ...


4

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 ...


4

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 ...


3

The steady state response is a sustained oscillatory response to a stimulus that is varying in strength with some regularity. But it is not a true oscillation, it's closer to an ERP in nature: it is a stimulus-driven response to variations in the stimulation. In auditory processing, which I am familiar with, people take a tone and then add an amplitude ...


3

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 ...


3

Hofstadter provides a detailed set of criteria in Fluid Concepts and Creative Analogies. Some of his criteria that I do not see mentioned above concern the flow of information-processing: whether or not the model evaluated flows through possibility space in a psychologically plausible way, and how to measure that. For instance... Suppose the following: ...


3

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/


3

Beyond what you've already listed, you'll need advanced signal processing skills, as so far, nobody has figured out how to get much more meaningful information out of the EEG than broad attentional state (alpha blocking and the P300, and the evoked potentials), and maybe some correlates of motor imagery, though none of these have realistically proved ...


2

On the EEG side, you'll need a way to access the EEG data in real-time so that you can perform whatever computational techniques you need to do in order to generate signals to control the TV. Some of the newer commercial-oriented EEG headsets have SDKs that you may be able to use. We've used the Emotiv system in our lab for mobile EEG research, and they have ...


2

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 ...


2

The following book may help you: Wermter, S., Palm, G., & Elshaw, M. (Eds.). (2005). Biomimetic neural learning for intelligent robots: Intelligent systems, cognitive robotics, and neuroscience (Vol. 3575). Springer. LINK It is about neurology and robotics. One should have a strong background in many subjects. Search Google with the following phrase ...


2

According to the paper, the advantage of this new approach over conventional ANNs, Deep Belief Networks (DBN) and Self-Organising Networks (SON) are: Remains functional during online learning. Requires only two layers connected with simultaneous supervised and unsupervised learning Employs spiking neuron models to reproduce central features ...


2

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 ...


2

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 ...


1

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. ...


1

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 ...


1

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 ...


1

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 ...


1

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 ...


1

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 ...


1

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 ...


1

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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