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8

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 ...


7

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 ...


6

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

Computational neuroscience and neural networks are both studied on this MSc at the University of Sussex. When I took the course in 2004/5, the Neural Networks module was compulsory, and the Computational Neuroscience was optional in the 2nd semester, so that would suggest the course designers (world leaders in biologically inspired computing) thought that ...


4

I haven't read the book, just googled, so: NEF is a mathematical model that simulates neural systems. It consists of formulae that you can use to (manually) compute the behavior of neurons. NENGO is a software (version 1.0 in the programming language Java and is scriptable in Python, version 2.0 is pure python) that implements the NEF, so that it computes ...


4

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 ...


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 ...


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 ...


3

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 (and I ...


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

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 ...


2

Neural Mechanisms of Stereoscopic Vision: This paper is a 1998 review of the experimental data surrounding stereoscopic vision. Neural Encoding of Binocular Disparity: Energy Models, Position Shifts, and Phase Shifts: In this paper, Heeger puts forward computational models of binocular disparity using phenomenological models of neurons (i.e., using ...


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 ...


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

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 ...


1

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 ...


1

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. He also mentions, metaphorically, the ...


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 ...


1

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, ...


1

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.


1

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 ...


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

From the physics(acoustics) perspective, the sensory input changes depending upon pitch. When you hear a sound that is high-pitched, your head blocks the sound wave, creating a sound shadow for the ear on the opposite side of your head from the sound source. This sound shadow means that your ears hear the sound at two different volumes, which the brain then ...


1

In "How to Build a Brain", Chris Eliasmith draws parrallels between Spaun and various architectures. All of my quotes in my answer are from that book. The Dynamic Field Theory of Cognition is similarly pre-occupied with outside stimulation as being essential to understanding cognitive function, consequently "there is little in the SPA that could not be ...



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