Archive for the ‘Attention’ Category

Joaquin Fuster on Cortical Dynamics

Saturday, April 5th, 2008

I recently watched this talk (below) by Joaquin Fuster. His theories provide a good integration of cortical functions and distributed processing in working and long-term memory. He also has some cool videos of likely network interactions across cortex (in real time) in his talk.

Here is a diagram of Dr. Fuster’s view of cortical hierarchies:

Joaquin Fuster’s talk:

Link to Joaquin Fuster’s talk [Google Video]

Here is an excerpt from Dr. Fuster’s amazing biography:
(more…)

The Cognitive Control Network

Sunday, October 7th, 2007

The Cognitive Control NetworkI recently published my first primary-author research study (Cole & Schneider, 2007).

The study used functional MRI to discover a network of brain regions responsible for conscious will (i.e., cognitive control). It also revealed the network’s specialized parts, which each uniquely contribute to creating the emergent property of conscious will.

I believe this research contributes substantially to our understanding of how we control our own thoughts and actions based on current goals. Much remains a mystery, but this study clearly shows the existence of a functionally integrated yet specialized network for cognitive control.

What is cognitive control? It is the set of brain processes necessary for goal-directed thought and action. Remembering a phone number before dialing requires cognitive control. Also, anything outside routine requires cognitive control (because it’s novel and/or conflicting with what you normally do). This includes, among other things, voluntarily shifting attention and making decisions.

What brain regions are involved? A mountain of evidence is accumulating that a common set of brain regions are involved in cognitive control. We looked for these regions specifically, and verified that they were active during our experiment [see top figure]. The brain regions are spread across the cortex, from the front to the back to either side. However, it’s not the whole brain: there are distinct parts that are involved in cognitive control and not other behavioral demands. (more…)

Grand Challenges of Neuroscience: Day 4

Saturday, July 7th, 2007

After a bit of a hiatus, I'm back with the last three installments of "Grand Challenges in Neuroscience". picture-1.png

Topic 4: Time

Cognitive Science programs typically require students to take courses in Linguistics (as well as in the philiosphy of language).  Besides the obvious application of studying how the mind creates and uses language, an important reason for taking these courses is to realize the effects of using words to describe the mental, cognitive states of the mind.

In fact — after having taken courses on language and thought, it seems that it would be an interesting coincidence if the words in any particular language did map directly onto mental states or brain areas.  (As an example, consider that the amygdala is popularly referred to as the "fear center".) 

It seems more likely that mental states are translated on the fly into language, which only approximates their true nature.  In this respect, I think it's important to realize that time may be composed of several distinct subcomponents, or time may play very different roles in distinct cognitive processes.

Time. As much as it is important to have an objective measure of time, it is equally important to have an understanding of our subjective experience of time.  A number of experimental results have confirmed what has been known to humanity for some time: Time flies while you're having fun, but a watched pot never boils.   
Time perception strongly relates cognition, attention and reward.  The NSF committee proposed that understanding time is going to be integrative, involving brain regions whose function is still not understood at a "systems" level, such as the cerebellum, basal ganglia, and association cortex.  

Experiments?

The NSF committee calls for the develpoment of new paradigms for the study of time.  I agree that this is critical.  To me, one of the most important issues is the dissociation of reward from time (e.g., "time flies when your having fun"):  most tasks involving time perception in both human and non-human primates involved rewarding the participants. 

In order to get a clearer read on the neurobiology of time perception and action, we need to observe neural representations that are not colored by the anticipation of reward.

-PL 

Brain image from http://www.cs.princeton.edu/gfx/proj/sugcon/models/
Clock image from http://elginwatches.org/technical/watch_diagram.html

History’s Top Brain Computation Insights: Day 24

Wednesday, April 25th, 2007

Cognitive control network (Cole & Schneider, 2007)24) Cognitive control processes are distributed within a network of distinct regions (Goldman-Rakic – 1988, Posner – 1990, Wager & Smith 2004, Cole & Schneider – 2007)

Researchers investigating eye movements and attention recorded from different parts of the primate brain and found several regions showing very similar neural activity. Goldman-Rakic proposed the existence of a specialized network for the control of attention.

This cortical system consists of the lateral frontal cortex (fronto-polar, dorsolateral, frontal eye fields), medial frontal cortex (anterior cingulate, pre-SMA, supplementary eye fields), and posterior parietal cortex. Subcortically, dorsomedial thalamus and superior colliculus are involved, among others.

Many computational modelers emphasize the emergence of attention from the local organization of sensory cortex (e.g., local competition). However, when a shift in attention is task-driven (i.e., top-down) then it appears that a specialized system for attentional control drives activity in sensory cortex. Many properties of attention likely arise from the organization of sensory cortex, but empirical data indicate that this is not sufficient.

With the advent of neuroimaging in humans (PET and fMRI), Posner et al. found very similar regions as those reported by Goldman-Rakic. He found that some regions are related more to orienting to stimuli, while others are related more to cognitive control (i.e., controlled processing).

After many fMRI studies of cognitive control were published, Wager et al. performed a meta-analysis looking at most of this research. They found a set of cortical regions active in nearly all cognitive control tasks.

My own work with Schneider (in press) indicates that these regions form an innate network, which is better connected than the rest of cortex on average. We used resting state correlations of fMRI BOLD activity to determine this. This cognitive control network is involved in controlled processing in that it has greater activity early in practice relative to late in practice, and has greater activity for conflicting responses (e.g., the Stroop task).

Though these regions have similar responses, they are not redundant. Our study showed that lateral prefrontal cortex is involved in maintaining relevant task information, while medial prefrontal cortex is involved in preparing and making response decisions. In most cases these two cognitive demands are invoked at the same time; only by separating them in time were we able to show specialization within the cognitive control network. We expect that other regional specializations will be found with more work.

I'll be covering my latest study in more detail once it is published (it has been accepted for publication at NeuroImage and should be published soon). The above figure is from that publication. It lists the six regions within the human cognitive control network. These regions include dorsolateral prefrontal cortex (DLPFC), inferior frontal junction (IFJ), dorsal pre-motor cortex (dPMC), anterior cingulate / pre-supplementary motor area (ACC/pSMA), anterior insula cortex (AIC), and posterior parietal cortex (PPC).

A general computational insight arising from this work (starting with Goldman-Rakic) is that cortex is composed of specialized regions that form specialized networks. This new paradigm for viewing brain function weds the old warring concepts of localized specialization and distributed function.

Implication: The mind, largely governed by reward-seeking behavior on a continuum between controlled and automatic processing, is implemented in an electro-chemical organ with distributed and modular function consisting of excitatory and inhibitory neurons communicating via ion-induced action potentials over convergent and divergent synaptic connections altered by timing-dependent correlated activity often driven by expectation errors. The cortex, a part of that organ organized via local competition and composed of functional column units whose spatial dedication determines representational resolution, is composed of many specialized regions forming specialized networks involved in perception (e.g., touch: parietal, vision: occipital), action (e.g., frontal), and memory (e.g., short-term: prefrontal, long-term: temporal), which depend on inter-regional connectivity for functional integration, population vector summation for representational specificity, and recurrent connectivity for sequential learning.

[This post is part of a series chronicling history's top brain computation insights (see the first of the series for a detailed description). See the history category archive to see all of the entries thus far.]

-MC

History’s Top Brain Computation Insights: Day 19

Friday, April 20th, 2007

Center-surround organization used in SOMs19) Neural networks can self-organize via competition (Grossberg – 1978, Kohonen – 1981)

Hubel and Wiesel's work with the development  of cortical columns (see previous post) hinted at it, but it wasn't until Grossberg and Kohonen built computational architectures explicitly exploring competition that its importance was made clear.

Grossberg was the first to illustrate the possibility of self-organization via competition. Several years later Kohonen created what is now termed a Kohonen network, or self-organizing map (SOM). This kind of network is composed of layers of neuron-like units connected with local excitation and, just outside that excitation, local inhibition. The above figure illustrates this 'Mexican hat' function in three dimensions, while the figure below represents it in two dimensions along with its inputs.

These networks, which implement Hebbian learning, will spontaneously organize into topographic maps.

For instance, line orientations that are similar to each other will tend to be represented by nearby neural units, while less similar line orientations will tend to be represented by more distant neural units. This occurs even when the map starts out with random synaptic weights. Also, this spontaneous organization will occur for even very complex stimuli (e.g., faces) as long as there are spatio-temporal regularities in the inputs.

Another interesting feature of Kohonen networks is that the more frequent input patterns are represented by larger areas in the map. This is consistent with findings in cortex, where more frequently used representations have larger cortical areas dedicated to them.

There are several computational advantages to having local competition between similar stimuli, which SOMs can provide.

One such advantage is that local competition can increase specificity of the representation by ruling out close alternatives via lateral inhibition. Using this computational trick, the retina can discern visual details better at the edges of objects (due to contrast enhancement).

Another computational advantage is enhancement of what's behaviorally important relative to what isn't. This works on a short time-scale with attention (what's not important is inhibited), and on a longer time-scale with increases in representational space in the map with repeated use, which increases representational resolution (e.g., the hand representation in the somatosensory homonculus).

You can explore SOMs using Topographica, a computational modeling environment for simulating topographic maps in cortex. Of special interest here is the SOM tutorial available at topographica.org.


Implication: The mind, largely governed by reward-seeking behavior on a continuum between controlled and automatic processing, is implemented in an electro-chemical organ with distributed and modular function consisting of excitatory and inhibitory neurons communicating via ion-induced action potentials over convergent and divergent synaptic connections strengthened by correlated activity. The cortex, a part of that organ organized via local competition and composed of functional column units whose spatial dedication determines representational resolution, is composed of many specialized regions involved in perception (e.g., touch: parietal, vision: occipital), action (e.g., frontal), and memory (e.g.,short-term: prefrontal, long-term: temporal),which depend on inter-regional communication for functional integration.

[This post is part of a series chronicling history's top brain computation insights (see the first of the series for a detailed description). See the history category archive to see all of the entries thus far.]

-MC

History’s Top Brain Computation Insights: Day 18

Thursday, April 19th, 2007

Reaction times for a visual search task illustrating controlled and automatic processing18) Behavior exists on a continuum between controlled and automatic processing (Schneider & Shiffrin – 1977)

During the 1970s those studying the cognitive computations underlying visual search were at an impasse. One group of researchers claimed that visual search was a flat search function (i.e., adding more distracters doesn't increase search time), while another group claimed that the function was linear (i.e., adding more distracters increases search time linearly).

Both groups had solid evidence supporting their view. What were the two groups doing differently that could explain such different results?

As a graduate student working with Shiffrin, Schneider sat the two groups down during a scientific conference to have them figure out why their results differed so much. Needless to say, little was accomplished as both sides talked past one another.

Several years later Schneider & Shiffrin came to the realization that the two groups were practicing their subjects differently. The group with the flat search function allowed their subjects to practice the search task many times before collecting data. In contrast, the group with the linear search function began collecting data as soon as their subjects could perform the task.

This realization lead Schneider & Shiffrin to posit a distinction between automatic (flat search function) and controlled (linear search function) processing. In a landmark set of papers they clearly demonstrated this dual process distinction along with the boundary conditions of controlled and automatic task performance. (more…)

History’s Top Brain Computation Insights: Day 17

Wednesday, April 18th, 2007

Examples of monkey and human working memory neural activity17) Reverbatory activity in lateral prefrontal cortex maintains memories and attention over short periods (Fuster – 1971, Jacobsen – 1936, Goldman-Rakic – 2000)

Patient H.M., with his lack of long term memory but largely intact working (short-term) memory, illustrated a dissociation between these two forms of memory. While long-term memory seemed to rely on hippocampus and the neocortical temporal lobes, in the 1960s it was not clear how working memory might be maintained.

Hebb had postulated a distributed and dynamic mechanism for working memory that was quite hard to test. However, a more testable hypothesis had emerged from observations of patients with prefrontal cortex damage. Such patients had trouble making and carrying out plans over time, possibly due to a working memory deficit. Previous work by Jacobsen lesioning primate prefrontal cortex supported this theory, but this work was far from conclusive.

In 1970 Joaquin Fuster cooled the monkey prefrontal cortex, showing a reversible deficit in working memory. The following year he recorded from monkey prefrontal cortex neurons and found cells that maintained activity over delay periods ('memory cells'). These neurons respond not just to the stimulus presentation and the response, but also maintain activity between the two events (see figure for illustration).

A decade later Fuster found visual memory cells in inferior temporal cortex. Subsequent research has suggested that the prefrontal memory cells drive the temporal cortex memory cells to maintain their activity.

Patricia Goldman-Rakic, another monkey neurophysiologist, was instrumental in elucidating the network properties of working memory function. She showed in 2000 that lateral prefrontal and posterior parietal cortices mutually support the sustained working memory activity discovered by Fuster. She also showed the importance of the dopamine system and thalamus in working memory function.

Since the advent of PET and functional MRI in the 1990s a number of researchers have extended the primate working memory findings to humans. Some of these researchers include Jonathan Cohen, Mark D'Esposito, Michael Petrides, Julie Fiez, and John Jonides.

Implication: The mind, largely governed by reward-seeking behavior, is implemented in an electro-chemical organ with distributed and modular function consisting of excitatory and inhibitory neurons communicating via ion-induced action potentials over convergent and divergent synaptic connections strengthened by correlated activity. The cortex, a part of that organ composed of functional column units whose spatial dedication (determined via local competition) determines representational resolution, is composed of many specialized regions involved in perception (e.g., touch: parietal, vision: occipital), action (e.g., frontal), and memory (e.g., short-term: prefrontal, long-term: temporal), which depend on inter-regional communication for functional integration.
[This post is part of a series chronicling history's top brain computation insights (see the first of the series for a detailed description). See the history category archive to see all of the entries thus far.]

-MC