1
vote
1answer
37 views

Why do I get smaller accuracy when I use 80% of training sets using HMAX model?

I am trying to compute the accuracy of the HMAX model. I am using the Face category (containing 435 images) from the Caltech101 database. I split it into $x$ ...
4
votes
1answer
73 views

Computational model of biological object recognition

The human brain can achieve a remarkable ability to recognize visual patterns in an Invariant, selective and fast manner. The human visual system is quite powerful. It has an exquisite selectivity ...
10
votes
3answers
181 views

Is there any recent work on modeling how we rapidly acquire new knowledge?

I work with neural network models of human cognition a lot, and one thing that bugs me about them is the timescale: they learn over thousands of trials whereas humans seem to learn after a couple ...
7
votes
1answer
95 views

Can processing effort for sub-tasks in neural networks be measured?

I often heard statements like: 80% of your brain processing is computing the effect of gravity or, similarily: You only use 20% of your brain power My question isn't about the truth of ...
7
votes
1answer
121 views

How can STDP fit with reciprocal connectivity?

I have rather technical question regarding STDP dynamics. I am working on a neural network implementing an STDP learning algorithm, and have noticed that it is extremely anti-reciprocal. When two ...
10
votes
3answers
362 views

What are the key examples of the use of computational methods in the study of biological neural networks?

In an upcoming postdoc, I'm going to be looking through biological neural network data in the hopes of finding some interesting "patterns". I'm coming at this field from a mathematics/computer ...
8
votes
0answers
289 views

Modern treatments of Alan Turing's B-type neural networks

In the cognitive sciences Alan Turing is best known for launching AI with his Computing machinery and intelligence (1950). However, this was not his first contribution to the cognitive sciences, in ...
9
votes
1answer
503 views

Spurious attractors in Hopfield networks

A classic "Hopfield network" is a type of artificial neural network in which the units are bi-stable and fully interconnected by symmetrically weighted connections. In 1982, Hopfield showed that such ...
4
votes
1answer
218 views

Computational differences between spiking neural networks and previous ANNs

This is an AI question regarding "3rd generation neural networks" - spiking neural networks (SNN). I hve been studying this concept online from various papers, mainly Maass (1997). I and am not ...
10
votes
2answers
235 views

References for biologically plausible models of knowledge representation?

I'm looking for references that deal with the issue of how various kinds of semantic knowledge are (or might be) represented neurally. Most of the discussion of this topic seems skewed by social ...
21
votes
2answers
1k views

Neural networks with biologically plausible accounts of neurogenesis

One of the reasons artificial neural net algorithms like cascade correlation (pdf) have been generating interest is because they start with a minimal topology (just input and output unit) and recruit ...