Archive for the ‘Memory and learning’ Category

Combinatorial Structures in Language and Visual Cognition

Wednesday, March 22nd, 2006

What gives humans the unique ability to construct novel sentences from the building blocks of language? A recent article in Behavioral and Brain Sciences proposes a “neural blackboard architecture” is capable of just this.

From the article (doi: 10.1017/S0140525X06009022):

“This paper aims to show that neural “blackboard” architectures can provide an adequate theoretical basis for a neural instantiation of combinatorial cognitive structures. [...] We also discuss the similarities between the neural blackboard architecture of sentence structure and neural blackboard architectures of combinatorial structures in visual cognition and visual working memory [...]”

As with all main articles in Behavioral and Brain Sciences, this one is followed by extensive comment and criticism from colleagues, and finally a reply by the authors. This provides a very deep look at the article and the issues surrounding it.

An older, but freely available, version of the article is available here.

Prediction vs. postdiction in self-movement

Sunday, March 5th, 2006

PLoS Biology: Attenuation of Self-Generated Tactile Sensations Is Predictive, not Postdictive [open access]

I haven’t gotten a chance to fully digest this article (What is the attenuation phenomena that happens when the taps are delayed?), but it seems like a deep result from a relatively simple haptics experiment. Just thought I’d share it with the crowd.

Also, Happy Birthday to fellow Neurodude Bayle! Congrats, man. :)

EEG study of states of mind conducive to storing memories

Monday, February 27th, 2006

“Scans of brain activity, published online in the journal Nature Neuroscience, indicate that the brain can actually get into the ‘right frame of mind’ to store new information and that we perform at our best if the brain is active not only at the moment we get new information but also in the seconds before.
….
Tests showed that the brain’s electrical activity differed after the cue question and before the word was presented and this was linked to whether the subject would remember or forget the word in a later unexpected memory test. If the electrical activity maintained a high level over frontal parts of the scalp just before the word was shown, then it was likely that the subject would remember the word up to 50 minutes later - and after doing a series of other word tests. On the other hand, if the voltage was lower, the subjects were less likely to remember the word.”

(from the press release)

Leun J. Otten, Richard N. A. Henson & Michael D. Rugg. State-related and item-related neural correlates of successful memory encoding. Nature Neuroscience 5, 1339 - 1344 (2002). Published online: 28 October 2002; doi:10.1038/nn967

Posit Science: Exercises for your mind

Wednesday, January 25th, 2006

As you know, here at Neurodudes, we’re always interested in seeing how people are applying results from neuroscience to areas outside of academic neuroscience. Today, I got the following in my inbox:

Special Lecture
BRAIN PLASTICITY IN AGING
Wednesday, February 1, 2006
10:00 am 46-4062
Bonnie Connor, Ph.D., Laila Spina, Psy.D., and Natasha Belfor, Ph.D.
Posit Science Corporation
San Francisco, CA

Leading me to the natural question: What is Posit Science Corp? Well, here is their website. They seem to be a company focused on keeping mental abilities sharp. To that end, they have lead seminars and sell computer programs based on aging research results.

I haven’t looked over their website in detail but they do seem to have a lot of information on the science behind their techniques and Mike Merzenich is their Chief Scientist. (There’s quite a few names you might recognize on their list of advisors and consultants.)

On the function of sleep

Tuesday, November 8th, 2005

The nice NYT article on the function of sleep follows on a recent NIH-funded Nature insight series.

Some interesting facts from the NYT article:

  • Sleep patterns vary greatly. Some bats sleep 20 hours, giraffes get 2 hours. (hmmm… grad students might be evolving toward giraffes…)
  • Sleep has recently been found to occur in invertebrates too. Alternatively stated: Sleep is evolutionarily very old.
  • Slow wave sleep is also found in fruit flies. (Divergence from fruit flies for us was 600 million years ago.)
  • Some people don’t have any REM sleep. Behaviorally, these people are entirely normal, implying that it’s purpose might not be as obvious as one had thought (ie. required for the preservation of new memories, etc.)
  • If you put a bunch of ducks in a row, the ones on the inside will sleep more often with both eyes closed. The ones on the outside will sleep with one eye open and it is (always?) the eye facing outward from the huddle. They are able to “sleep” one half of the brain at a time and, apparently, this sleeping with one eye open was lost in higher mammalian evolution. Fascinating.

Wired mag article on hippocampal prothesis

Sunday, October 9th, 2005

This article is about efforts by six teams to develop a hippocampal prothesis by monitoring the input/output transformations performed by the hippocampus in slice, and then creating an electronic device to mimic them.

The article quotes noted memory researchers Howard Eichenbaum and Norbert Fortin who seem to approve of the methodology.

Jimbo et al ‘99: plasticity at the network level in culture

Thursday, September 8th, 2005

Jimbo, Tateno, and Robinson did a network plasticity experiment using cultured networks and a multi-electrode array.

They determine the effect of a tetanus at one electrode in a network on the network. Specifically, they look at how the tetanus potentiates or depresses the ability of a test pulse at another electrode to evoke spike trains at various neurons across the network.

They grew cultures on a MEA for a month. They stimulated each electrode in succession with a test pulse. They recorded the response at all electrodes after each test pulse. They used spike sorting to identify the reponses of individual neurons out of the electrode traces. They found that the network’s response to a given test pulse was reproducable for about 50ms after the test pulse.

Then they applied a strong stimulus (a tetanus) to a single electrode (to make it learn :) ). After that they re-characterized the network’s responses to test pulses at every site.

They found that some electrode sites became more potent (”potentiated response”) after the tetanus was applied. This means that, when a test pulse was applied to this electrode site, neurons in all areas of the network responded either the same, or more strongly than they had before the tetanus.

Other sites became less potent (”depressed response”) after the tetanus was applied.

Surprisingly, it was very rare for any given electrode site to become better at stimulating some neurons and worse at stimulating others as a result of the tetanus.

What determined which electrode sites became potentiated and which ones became depressed? The tetanus potentiated electrodes which evoked spike trains that tended to contain spikes which were within 40ms of the spike trains evoked by the tetanus electrode, and depressed others. That is, it potentiated sites which evoked patterns similar to the patterns evoked by the tetanus site.

However, the spike trains evoked by both potentiated and depressed neurons became more synchronized with the tetanus electrode after applying the tetanus.

See page 5 of “Distributed processing in cultured neuronal networks” for another review of this work.

See this NeuroWiki page for more details (the strange {{}} over there are because we will soon have footnotes).

Jimbo, Y., Tateno, T., and Robinson, H. P. C.,
Simultaneous Induction of Pathway-Specific Potentiation and Depression in Networks of Cortical Neurons. Biophysical Journal, 1999. 76: p. 670-678.

Machine learning theory blog

Tuesday, August 30th, 2005

For those with theoretical interests with respect to machine learning flavored AI, the ML Theory blog run by John Langford is highly recommended. Though recently started, Langford and others have so far been doing an excellent job of commenting on both the science and culture of theoretical learning research.

Ampakine CX717 improves delayed match to sample performance

Monday, August 29th, 2005

Scientific Clearing House: Mind enhancing drugs

Apparently, CX717, an ampakine developed by Cortex Pharmaceuticals, shows some signs of preventing the cognitive impairment brought on by sleep deprivation. The original study in PLoS Biology (news & views) was done with monkeys.

Neuroimaging with Rescorla-Wagner model

Sunday, August 28th, 2005

Neuroimaging data of different brain areas fit to a Rescorla-Wagner model show that different cortical areas integrate stimulus changes over different time intervals. The result itself probably isn’t that shocking but I liked the nice combination of theory and experiment.

From the July 21 Neuron:

Formal Learning Theory Dissociates Brain Regions with Different Temporal Integration

Jan Gläscher and Christian Büchel

Learning can be characterized as the extraction of reliable predictions about stimulus occurrences from past experience. In two experiments, we investigated the interval of temporal integration of previous learning trials in different brain regions using implicit and explicit Pavlovian fear conditioning with a dynamically changing reinforcement regime in an experimental setting. With formal learning theory (the Rescorla-Wagner model), temporal integration is characterized by the learning rate. Using fMRI and this theoretical framework, we are able to distinguish between learning-related brain regions that show long temporal integration (e.g., amygdala) and higher perceptual regions that integrate only over a short period of time (e.g., fusiform face area, parahippocampal place area). This approach allows for the investigation of learning-related changes in brain activation, as it can dissociate brain areas that differ with respect to their integration of past learning experiences by either computing long-term outcome predictions or instantaneous reinforcement expectancies.

How does this relate to Hawkins’s idea that all cortex implements the same underlying “algorithm”? Is the integration time constant (or, in RW terms, the learning rate) tuned differently by different inputs?