Figuring out how the brain decides between two options is difficult. This is especially true for the human brain, whose activity is typically accessible only via the small and occasionally distorted window provided by new imaging technologies (such as functional MRI (fMRI)).

In contrast, it is typically more accurate to observe monkey brains since the skull can be opened and brain activity recorded directly.

Despite this, if you were to look just at the human research, you would consider it a fact that the anterior cingulate cortex (ACC) increases its activity during response conflict. The thought is that this brain region detects that you are having trouble making decisions, and signals other brain regions to pay more attention.

If you were to only look at research with monkeys, however, you would think otherwise. No research with macaque monkeys (the ‘non-human primate’ typically used in neuroscience research) has found conflict activity in ACC.

My most recent publication looks at two possible explanations for this discrepancy: 1) Differences in methods used to study these two species, and 2) Fundamental evolutionary differences between the species.

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Thousands of brain imaging studies are published each year. A subset of these studies are replications, or slight variations, of previous studies. Attempting to come to a solid conclusion based on the complex brain activity patterns reported by all these replications can be daunting. Meta-analysis is one tool that has been used to make sense of it all.

Meta-analyses take locations of brain activity in published scientific papers and pool them together to see if there is any consistency.

This is typically done using a standardized brain that all the studies fit their data to (e.g., Talairach). Activation coordinates are then placed on a template brain as dots. When dots tend to clump together then the author can claim some consistency is present across studies. See the first figure for an example of this kind of result.

More sophisticated ways of doing this have emerged, however. One of these advanced methods is called activation likelihood estimation (ALE). This method was developed by Peter Terkeltaub et al. (in conjunction with Jason Chein and Julie Fiez) in 2002 and extended by Laird et al. in 2005.

ALE computes the probability of each part of the brain being active across studies. This is much more powerful than simple point-plotting because it takes much of the guess-work out of deciding if a result is consistent across studies or not.

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Topic 6: Causal Understanding

Causal understanding is an important part of human cognition.  How do we understand that a particular event or force has caused another event?  How do realize that inserting coins into a soda machine results in a cool beverage appearing below?  And ultimately, how do we understand people’s reactions to events?

The NSF workshop panel on the Grand Challenges of Mind and Brain highlighted the question of ‘causal understanding’ as their 6th research topic.   (This was the final topic in their report.)

In addition to studying causal understanding, it is probably just as important to study causal misunderstanding: that is, why do individuals infer the wrong causes for events?  Or incorrect results from causes? Studying the errors we make in causal inference and understanding may help us discover the underlying neural mechanisms.  

It probably isn’t too difficult to imagine that progress on causal understanding, and improvements in our ability to be correct about causation, will probably be important for the well-being of humanity.  But what kinds of experiments and methods could be used to human brain mechanisms of  causal understanding?

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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:
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Modern technologies allow eye movements to be used as a tool for studying language processing during tasks such as natural reading. Saccadic eye movements during reading turn out to be highly sensitive to a number of linguistic variables. A number of computational models of eye movement control have been developed to explain how these variables affect eye movements. Although these models have focused on relatively low-level cognitive, perceptual and motor variables, there has been a concerted effort in the past few years (spurred by psycholinguists) to extend these computational models to syntactic processing.

During a modeling symposium at ECEM2007 (the 14th European Conference on Eye Movements), Dr. Ronan Reilly presented a first attempt to take syntax into account in his eye-movement control model (GLENMORE; Reilly & Radach, Cognitive Systems Research, 2006). (more…)

In the dark confines behind our eyes lies flesh full of mysterious patterns, constituting our hopes, desires, knowledge, and everything else fundamental to who we are. Since at least the time of Hippocrates we have wondered about the nature of this flesh and its functions. Finally, after thousands of years of wondering we are now able to observe the mysterious patterns of the living brain, with the help of neuroimaging.

First, electroencephalography (EEG) showed us that these brain patterns have some relation in time to our behaviors. EEG showed us when things happen in the brain. More recent technologies such as functional magnetic resonance imaging (fMRI) then showed us where things happen in the brain.

It has been suggested that true insights into these brain patterns will arise when we can understand the patterns’ complex spatio-temporal nature. Thus, only with sufficient spatial and temporal resolution will we be able to decipher the mechanisms behind the brain patterns, and as a result the mechanisms behind ourselves.

Magnetoencephalography (MEG) may help to provide such insight. This method uses superconducting sensors to detect subtle changes in the magnetic fields surrounding the head. These changes reflect the patterns of neural activity as they occur in the brain. Unlike fMRI (and similar methods), MEG can measure neural activity at a very high temporal resolution (>1 kHz). In this respect it is similar to EEG. However, unlike EEG, MEG patterns are not distorted by the skull and scalp, thus providing an unprecedented level of spatio-temporal resolution for observing the neural activity underlying our selves.

Despite being around for several decades, new advances in the technology are providing unprecedented abilities to observe brain activity. Of course, the method is not perfect by any means. As always, it is a method complimentary to others, and should be used in conjunction with other noninvasive (and the occasionally invasive, where appropriate) neuroimaging methods.

MEG relies on something called a superconducting quantum interference device (SQUID). Many of these SQUIDs are built into a helmet, which is cooled with liquid helium and placed around the head. Extremely small magnetic fields created by neural activity can then be detected with these SQUIDs and recorded to a computer for later analysis.

I recently got back from a trip to Finland, where I learned  a great deal about MEG. I’m planning to use the method to observe the flow of information among brain regions during cognitive control tasks involving decision making, learning, and memory. I’m sure news of my work in this area will eventually make it onto this website.

-MC

This image is not of a neuron.

This image is of the other universe; the one outside our heads.

It depicts the "evolution of the matter distribution in a cubic region of the Universe over 2 billion light-years", as computed by the Millennium Simulation. (Click the image above for a better view.)

The next image, of a neuron, is included for comparison.

It is tempting to wax philosophical on this structure equivalence. How is it that both the external and internal universes can have such similar structure, and at such vastly different physical scales?

If we choose to go philosophical, we may as well ponder something even more fundamental: Why is it that all complex systems seem to have a similar underlying network-like structure?

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Topic 2: Conflict and Cooperation

Generally, cognitive neuroscience aims to explain how mental processes such as believing, knowing, and inferring arise in the brain and affect behavior.  Two behaviors that have important effects on the survival of humans are cooperation and conflict. 

According to the NSF committee convened last year, conflict and cooperation is an important focus area for future cognitive neuroscience work.  Although research in this area has typically been the domain of psychologists, it seems that the time is ripe to apply findings from neuroscience to ground psychological theories in the underlying biology.

Neuroscience has produced a large amount of information about the brain regions that are relevant to social interactions.  For example, the amygdala has been shown to be involved in strong emotional responses.  The "mirror" neuron system in the frontal lobe allows us to put ourselves in someone else's shoes by allowing us to understand their actions as though they were our own.  Finally, the superior temporal gyrus and orbitofrontal cortex, normally involved in language and reward respectively, have also been shown to be involved in social behaviors.

Experiments?

The committee has left it up to us to come up with a way to study these phenomena! How can we study conflict and cooperation from cognitive neuroscience perspective?

At least two general approaches come to mind. The first is fMRI studies in which social interactions are simulated (or carried out remotely) over a computer link to the experiment participant.  A range of studies of this sort have recently begun to appear investigating trust and decision-making in social contexts.

The second general approach that comes to mind is that of  using neurocomputational simulations of simple acting organisms with common or differing goals.  Over the past few years, researchers have been carrying out studies with multiple interacting "agents" that "learn" through the method of Reinforcement Learning. 

Reinforcement Learning is an artificial intelligence algorithm which allows "agents" to develop behaviors through trial-and-error in an attempt to meet some goal which provides reward in the form of positive numbers.  Each agent is defined as a small program with state (e.g., location, sensory input) and a memory or "value function" which can keep track  of how much numerical reward it expects to obtain by choosing a possible action.

Although normally thought to be of interest only to computer scientists, Reinforcement Learning has recently attracted the attention of cognitive neuroscientists because of emerging evidence that something like it might be used in the brain.

By providing these agents with a goal that can only be achieved through some measure of coorperation or under some pressure, issues of conflict and coorperation can by studied in a perfectly controlled computer simulation environment.

-PL 

Following up on MC's posts about the significant insights in the history of neuroscience, I'll now take Neurevolution for a short jaunt into neuroscience's potential future.

In light of recent advances in technologies and methodologies applicable to neuroscience research, the National Science Foundation last summer released a document on the "Grand Challenges of Neuroscience".  These grand challenges were identified by a committee of leading members of the cognitive neuroscience community.

The document, available at http://www.nsf.gov/sbe/grand_chall.pdf, describes six domains of research the committee deemed to be important for progress in understanding the relationship between mind and brain.

Over the next few posts, I will discuss each of the research domains and explain in layperson's terms why these questions are interesting and worth pursuing.  I'll also describe potential experimental approaches to address these questions in a cognitive neuroscience framework.

Topic 1:  "Adaptive Plasticity"

One research topic brought up by the committee was that of adaptive plasticity.  In this context, plasticity refers to the idea that the connections in the brain, and the behavior governed by the brain, can be changed through experience and learning.  

Learning allows us to adapt to new circumstances and environments.  Arguably, understanding how we learn and how to improve learning could be one of the greatest contributions of neuroscience.

Although it is widely believed that memory is based on the synaptic changes that occur during long-term potentiation and long-term depression (see our earlier post) this has not been conclusively shown!

What has been shown is that drugs that prevent synaptic changes also prevent learning.  However, that finding only demonstrates a correlation between synaptic change and memory formation,  not causation. (For example, it is possible that those drugs are interfering with some other process that truly underlies memory.)

The overarching question the committee raises is: What are the rules and principles of neural plasticity that implement [the] diverse forms of memory?

This question aims to quantify the exact relationships between changes at the neuronal level and at the level of behavior.  For instance, do rapid changes at the synapse reflect rapid learning?  And, how do the physical limitations on the changes at the neuronal level relate to cognitive limitations at the behavioral level?

Experiments?
My personal opinion is that the answers to these questions will be obtained through new experiments that either implant new memories or alter existing ones (e.g., through electrical stimulation protocols). 

There is every indication that experimenters will soon be able to select and stimulate particular cells in an awake, behaving animal to alter the strength of the connection between those cells.  The experimenters can then test the behavior of the animals to see if their memory for the association that might be represented by that connection has been altered.

-PL 

This post is the culmination of a month-long chronicling of the major brain computation insights of all time.

Some important insights were certainly left out, so feel free to add comments with your favorites.

Below you will find all 26 insights listed with links to their entries. At the end is the summary of the insights in two (lengthy) sentences.

1) The brain computes the mind (Hippocrates- 460-379 B.C.)

2)  Brain signals are electrical (Galvani - 1791, Rolando - 1809)

3)  Functions are distributed in the brain (Flourens - 1824, Lashley - 1929)

4) Functions can be localized in the brain (Bouillaud - 1825, Broca - 1861, Fritsch & Hitzig - 1870)

5) Neurons are fundamental units of brain computation (Ramon y Cajal - 1889)

6) Neural networks consist of excitatory and inhibitory neurons connected by synapses (Sherrington - 1906)

7) Brain signals are chemical (Dale - 1914, Loewi - 1921)

8) Reward-based reinforcement learning can explain much of behavior (Skinner - 1938, Thorndike - 1911, Pavlov - 1905)

9) Convergence and divergence between layers of neural units can perform abstract computations (Pitts & McCulloch - 1947)

10) The Hebbian learning rule: 'Neurons that fire together wire together' [plus corollaries] (Hebb, 1949)

11) Action potentials, the electrical events underlying brain communication, are governed by ion concentrations and voltage differences mediated by ion channels (Hodgkin & Huxley - 1952)

12) Hippocampus is necessary for episodic memory formation (Milner - 1953)

13) Larger cortical space is correlated with greater representational resolution; memories are stored in cortex (Penfield - 1957)

14) Neocortex is composed of columnar functional units (Mountcastle - 1957, Hubel & Wiesel - 1962)

15) Consciousness depends on cortical communication; the cortical hemispheres are functionally specialized (Sperry & Gazzaniga - 1969)

16) Critical periods of cortical development via competition (Hubel & Wiesel - 1970)

17) Reverbatory activity in lateral prefrontal cortex maintains memories and attention over short periods (Fuster - 1971, Jacobsen - 1936, Goldman-Rakic - 2000)

18) Behavior exists on a continuum between controlled and automatic processing (Schneider & Shiffrin - 1977)

19) Neural networks can self-organize via competition (Grossberg - 1978, Kohonen - 1981)

20) Spike-timing dependent plasticity: Getting the brain from correlation to causation (Levy - 1983, Sakmann - 1994, Bi & Poo - 1998, Dan - 2002)

21) Parallel and distributed processing across many neuron-like units can lead to complex behaviors (Rumelhart & McClelland - 1986, O'Reilly - 1996)

22) Recurrent connectivity in neural networks can elicit learning and reproduction of temporal sequences (Jordan - 1986, Elman - 1990, Schneider - 1991)

23) Motor cortex is organized by movement direction (Schwartz  & Georgopoulos - 1986, Schwartz - 2001)

24) Cognitive control processes are distributed within a network of distinct regions (Goldman-Rakic - 1988, Posner - 1990, Wager & Smith - 2004, Dosenbach et al. - 2006, Cole & Schneider - 2007)

25) The dopamine system implements a reward prediction error algorithm (Schultz - 1996, Sutton - 1988)

26) Some complex object categories, such as faces, have dedicated areas of cortex for processing them, but are also represented in a distributed fashion (Kanwisher - 1997, Haxby - 2001)

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 and/or overlapping distributed 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, dopamine signals for reinforcement learning, and recurrent connectivity for sequential learning.

-MC 

11) Action potentials, the electrical events underlying brain communication, are governed by ion concentrations and voltage differences mediated by ion channels (Hodgkin & Huxley - 1952)

Hodgkin & Huxley developed the voltage clamp, which allows ion concentrations in a neuron to be measured with the voltage constant. Using this device, they demonstrated changes in ion permeability at different voltages. Their mathematical model of neuron function, based on the squid giant axon, postulated the existence of ion channels governing the action potential (the basic electrical signal of neurons). Their model has been verified, and is amazingly consistent across brain areas and species.

You can explore the Hodgkin & Huxley model by downloading Dave Touretsky's HHSim, a computational model implementing the Hodgkin & Huxley equations.

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.

[This post is part of a series chronicling history's top brain computation insights (see the first of the series for a detailed description)]

-MC

Most neuroscientists don't use human subjects, and many tend to forget this important point: 
All neuroscience with non-human subjects is theoretical.

If the brain of a mouse is understood in exquisite detail, it is only relevant (outside veterinary medicine) in so far as it is relevant to human brains.

Similarly, if a computational model can illustrate an algorithm for storing knowledge in distributed units, it is only as relevant as it is similar to how humans store knowledge.

It follows from this point that there is a certain amount of uncertainty involved in any non-human research. An experiment can be brilliantly executed, but does it apply to humans?

Circumventing this uncertainty problem by looking directly at humans, another issue arises:  Only non-invasive techniques can be used with humans, and those techniques tend to involve the most uncertainty.

For instance, fMRI is a non-invasive technique that can be used to measure brain processes in humans. However, it measures the oxygenation levels, which is only indirectly related to neural activity. Thus, unlike with animal models, measures of neuronal activity are surrounded by an extra layer of uncertainty in humans.

So, if you're a neuroscientist you have to "choose your poison": Either deal with the uncertainty of relevance to humans, or deal with the uncertainty of the processes underlying the measurable signals in humans.
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I thought this article at Howstuffworks was appropriate just after the all day drinking fest that is St. Patrick's Day for many.

According to the article, a hangover from a heavy night (and/or day) of drinking is mainly due to dehydration.

The dehydration process begins with a chemical reaction in the brain; specifically the pituitary gland. This reaction causes less vasopressin to be released from the pituitary gland, which in turn causes the kidneys to send water directly into the bladder (rather than reabsorbing it).

So why do hangovers cause headaches? Apparently the massive amount of dehydration by the morning causes the body's organs to steal water from the brain.

According to the article, this causes "the brain to decrease in size and pull on the membranes that connect the brain to the skull, resulting in pain". This cannot be good for neuronal health!

Based on this information it seems that the best way to avoid a hangover is to drink plenty of water along with those heavy booze. There are other things (e.g. electrolytes) that are lost along with H2O, however.

I'm not convinced that these really work, but some hangover prevention pills on the market may help to avoid losing these essential chemicals.

Each type of drink has a different kind of hangover associated with it, according to this article. Red wine and dark liquors have the worst side effects, while vodka is the least likely to cause a hangover.

If these articles are right, a good way to drink without getting a hangover is to take shots of water between shots of vodka (no one could tell the difference!), and maybe add a little orange juice (making a screwdriver) to add some electrolytes back into the mix. Anyone care to test out this theory…?

-MC

Most neuroscience writing touts statements like 'the human brain is the most complex object in the universe'. This serves only to paint the brain as a mysterious, seemingly unknowable structure.

This is somehow comforting to some, but it's not for me. I want to understand this thing!

Here are some facts to demystify the brain (most points courtesy of Dave Touretzky and Christopher Cherniak):

• The brain is very complex, but not infinitely so

• A few spoonfuls of yogurt contain 10¹¹ lactobaccillus bacteria: ten times the number of cortical neurons in a human brain
• There are roughly 10¹³ synapses in cortex. Assume each stores one bit of information: that’s 1.25 terabytes.
• To put this in perspective, this 2.0 terabyte hard drive is readily available and costs less than $900.

• Also, the Library of Congress (80 million volumes, average 300 typed pages each) contains about 48 terabytes of data.

• Volume of the human brain: about 1.4 liters.
• Number of neurons in a human brain: 10¹². Number of neurons in a rat brain: 10¹⁰.
• Number of neurons in human spinal cord: 10⁹ [source]
• Average loss of neocortical neurons = 1 per second; 85,000 per day; ~31 million (31×10⁶) per year [source]
• No neuron fires faster than 1 kHz
• The total energy consumption of the brain is about 25 watts [source]
• Average number of glial cells in brain = 10-50 times the number of neurons [source]
• "For all our neurocomputational sophistication and processing power, we can barely attend to more than one object at a time, and we can hardly perform two tasks at once" [source]

What to take from all this? Simply that the brain is a real, physical object with real, physical limitations. As such, we really do have a chance to understand it.

Of course, if the brain is so limited, how can we expect our brains to be up to the task of understanding it?

We can, and we will. How can I be so sure? Because (as illustrated above) we have already built, and thus understand, computational devices that are rivaling and in some cases surpassing (e.g., in memory capacity) the computational powers of the brain.

This shows that we have what it takes to understand ourselves in the deepest way possible: by learning the neural mechanisms that make us who and what we are.

-MC 

In my most recent post I gave an overview of the "simple recurrent network" (SRN), but I'd like to take a step back and talk about neuromodeling in general.  In particular I'd like to talk about why neuromodeling is going to be instrumental in bringing about the cognitive revolution in neuroscience.

A principal goal of cognitive neuroscience should be to explain how cognitive phenomena arise from the underlying neural systems.  How do the neurons and their connections result in interesting patterns of thought?  Or to take a step up, how might columns, or nuclei, interact to result in problem solving skills, thought or consciousness?

If a cognitive neuroscientist believes they know how a neural system gives rise to a behavior, they should be able to construct a model to demonstrate how this is the case.

That, in brief, is the answer.

But what makes a good model?  I'll partially answer this question below, but in future posts I'll bring up specific examples of models, some good, some poor.

First, any "model" is a simplification of the reality.  If the model is too simple, it won't be interesting.  If it's too realistic, it will be too complex to understand.  Thus, a good model is at that sweet spot where it's as simple as possible but no simpler.
Second, a model whose ingredients spell out the result you're looking for won't be interesting.  Instead, the results should emerge from the combination of the model's resources, constraints and experience.

Third, a model with too many "free" parameters is less likely to be interesting.  So an important requirement is that the "constraints" should be realistic, mimicking the constraints of the real system that is being modeled.

A common question I have gotten is:  "Isn't a model just a way to fit inputs to outputs?  Couldn't it just be replaced with a curve fitter or a regression?"  Well, perhaps the answer should be yes IF you consider a human being to just be a curve fitting device. A human obtains inputs and generates outputs.  So if you wish to say that a model is just a curve fitter, I will say that a human is, too.

What's interesting about neural systems, whether real or simulated, is the emergence of complex function from seemingly "simple" parts.

In future posts, I'll talk more about "constraints" by giving concrete examples.  In the meantime, feel free to bring up any questions you have about the computational modeling of cognition.
-PL 

[Image by Santiago Ramon y Cajal, 1914.]