Grand Challenges of Neuroscience: Day 3

Topic 3: Spatial Knowledgeskaggs96figure3.png

Animal studies have shown that the hippocampus contains special cells called "place cells".  These place cells are interesting because their activity seems to indicate not what the animal sees, but rather where the animal is in space as it runs around in a box or in a maze. (See the four cells in the image to the right.)

Further, when the animal goes to sleep, those cells tend to reactivate in the same order they did during wakefulness.  This apparent retracing of the paths during sleep has been termed "hippocampal replay".

More recently, studies in humans — who have deep microelectrodes implanted to help detect the origin of epileptic seizures — have shown place-responsive cells.  Place cells in these studies were found not only in the human hippocampus but also in nearby brain regions.

The computation which converts sequences of visual and other cues into a sense of "place" is a very interesting one that has not yet been fully explained.  However, there do exist neural network models of the hippocampus that, when presented with sequences, exhibit place-cell like activity in some neurons.

The notion of place cell might also extend beyond physical space.  It has been speculated that computations occur to convert sequences events and situations into a distinct sense of "now".  And indeed, damage to the hippocampus has been found not only to impair spatial memory but also "episodic" memory, the psychological term for memory for distinct events.


How can we understand the ways in which we understand space? Understanding spatial knowledge seems more tangible than understanding the previous two topics in this series. It seems that researchers are already using some of the most effective methods to tackle the problem.

First, the use of microelectrodes throughout the brain while human participants play virtual taxi games and perform problem solving tasks promises insight into this question.  Second, computational modeling of regions (e.g., the hippocampus) containing place cells should help us understand their properties and how they emerge.  Finally, continued animal research and possibly manipulation of place cells in animals to influence decision making (e.g., in a T-maze task) may provide an understanding of how spatial knowledge is used on-line. 


Grand Challenges of Neuroscience: Day 2

swarm-thumb.jpgTopic 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.


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.