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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 ...


6

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 ...


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

In psychology, you have two types of measurement: either a trained observer judges the behavior of the subjects without them having to actively partake in any kind of test or experiment; or the test subjects fill in a test (self-report) or take part in an experiment. Obviously the mood in a city cannot be re-created and measured in an experiment, but you ...


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

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

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

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). ...


3

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 ...


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 ...


3

Mario Liotti and Don M. Tucker (Brain Asymmetry, MIT, 1996) attempt to explain that the 'corticolimbic architecture is not left/right, but dorsal/ventral". In their opinion, the reason for hemispheric asymmetries can be found in the asymmetries of the dorsal and ventral systems. They proposed that emotional behavior could be interpreted by analyzing the ...


3

The question of how "rapid" learning could be possible relates to Hume's problem of induction -- how can we learn so much from so little. Historically, in both philosophy and psychology, the solution has fallen into one of two camps: either some form of the knowledge was already there to begin with (a 'nativist' view), or we use statistical inference to ...


2

Henderson summarizes very well a number of approaches on human gaze control during real-world scenes and tasks. http://cvcl.mit.edu/iap05/henderson_03.pdf In a nutshell, our visual system combines knowledge about the task (e.g. color of the search target) and external audio/visual stimuli (saliency) to control our gaze inside a scene. Quite insightful about ...


2

You may be interested in the FARS model from Fagg and Arbib (1999) that describes the interaction of the two visual streams in the primate brain during object grasping. The article What Puts the How in Where? Tool Use and the Divided Visual Streams Hypothesis (2007) makes use of the dorsal/ventral streams to explain our ability to use complex tool. As Frey ...


2

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 ...


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

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 ...


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 ...



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