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9

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 ...


5

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 ...


5

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 a 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 ...


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

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 ...


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 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 that contains 2.5 million neurons to contain a network of ...


4

Partial answer: Douglas Hofstadter has written quite a lot about this from a more philosophical approach. His style isn't for everyone, I think it's introduced well in this chapter ('Ant Fugue'). For more applied work from the same, you might look at Mitchell and Hofstadter's CopyCat model of analogies (described briefly here, as well as on wikipedia). ...


4

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 ...


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

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

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

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 ...


3

Some neuroscience papers on sound localization: Joris Philip X, Smith Philip H, and Yin Tom C.T Coincidence Detection in the Auditory System // Neuron (1998) Agmon-Snir Hagai, Carr Catherine E. and Rinzel John The role of dendrites in auditory coincidence detection // Nature (1998) Trussell Laurence O. Synaptic mechanisms for coding timing in auditory ...


2

The question is resolved : The name HMAX ( Hierarchical Model And X ) was coined by “Mike Tarr”, who wrote the “News and Views” accompanying the paper in Nature Neuroscience. What was meant by Hierarchical? The model has a hierarchical architecture : it contains different stages (layers). -- Increase in receptive field size and complexity in unit ...


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

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 ...


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

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

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

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 ...


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

It seems there is! Check out Marsalli's module from The Mind Project's curriculum and let me know if it works for you. It seems McCullough and Pitts' paper was important enough to be cited very many times, so there are probably several other options out there for you. Reference Marsalli, M. McCulloch-Pitts neurons. The Mind Project: Curriculum. Retrieved ...


1

I got the answer for this question: Because the dataset becomes imbalanced. That is why the performance of our classifier decreases instead to decrease. Usually you should have a balance between out positive and negative training.



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