Nothing but networks of neurons

Why are elite sport players so much better at their game than you or I? A professional basketballer would probably make eight or nine free throws from every ten, while I’d be happy with five. Why the difference?

There are a variety of reasons. The pro has probably developed (or been born with) muscle areas which I rarely use. Also, he or she has learned, through intense practice, how to control these muscles very precisely. This is how a golfer can put the ball within a couple of metres of the flag from halfway down the fairway. Despite our instinctive notions to the contrary, this fine motor control does not originate from the muscles themselves, but is orchestrated by the brain – where, of course, all learning occurs.

So, apart from being able to effectively co-ordinate their finely tuned muscles, do the top athletes have any other advantages over the rest of us? A paper published today in Nature Neuroscience suggests that they do. Aglioti et al investigated the ability of elite basketball players to anticipate whether a shot was heading for the basket or not. It turned out that these athletes can not only perform this task significantly better than expert watchers (sports journalists and coaches) and lay persons, but that their muscles activated differently for shots going in or out of the hoop, even though they were just sitting still in a chair for the duration of the experiment. And it doesn’t take an expert to see that the ability to predict the future would boost your chances of the catching that next rebound.

Intriguingly, the elite athletes’ advantage seems to stem not from being better able to visualise the path of the ball through the air, but from a superior ability to read body movements before the ball has left the player’s hands. The task was to watch a video clip of people shooting baskets. The clips were cut short at varying times before the ball reached the basket. Unsurprisingly, seeing more of the clip improved all groups’ (players, expert watchers and novices) ability to guess whether the ball finished in or out of the basket. But for the clips that were cut just at or before the player in the video released the ball (781ms in the graph), the elite basketball players were getting it right 70% of the time, compared to about 40% for the other groups. Here’s a glimpse of the data for the curious:

It would be interesting to see if the same phenomenon was found in top sports people who play games where anticipation is not a key skill, like golf or swimming. If you are interested, I recommend reading the full paper, especially for more references. I’m not an expert on the topic, but this action-perception debate and the mirror system theory is really fascinating.

Ref:
Salvatore M Aglioti, Paola Cesari, Michela Romani, Cosimo Urgesi (2008). Action anticipation and motor resonance in elite basketball players Nature Neuroscience, 11 (9), 1109-1116 DOI: 10.1038/nn.2182

The grid cell code (part 1)

In this three-part article I will:

1. explain a little about what grid cells are,
2. summarise some of the current ideas about how they might be used to code an animal’s location, and
3. discuss some of the many unanswered questions surrounding this fascinating neural coding scheme.

In 2005, Torkel Hafting and Marianne Fyhn permanently changed the neuroscience of spatial navigation by discovering the grid cell. In their seminal Nature paper, the Moser lab reported that certain cells in the rat medial entorhinal cortex (MEC) fire action potentials only when the animal occupies certain locations in space. When you map at these locations from above, they look suspiciously like a 2-D triangular grid which appears to tile any sized area. The grid persists when the rat is placed into a new environment, and even when it is kept in the dark. It is an internal, metric representation of space. To me at least, this is stunning.

The image below is a beautiful example recording. On the left, the black trace is the path of the rat over a few minutes roaming inside a 1m by 1m box, and the red dots are where the cell fired. The right image is a colour-coded map of the firing rate of the same cell. Note the triangular grid pattern. I know it’s not perfect, but in biology this is about as orderly as things get.

(I stole this image from the excellent Scholarpedia article on grid cells by Mr and Mrs Moser. I highly recommend it, along with their recent Annual Review of Neuroscience paper, for great overviews and more references.)

Along with simply reporting their discovery, the Moser group went on to describe the properties of these grids in some detail. They found that, in general, neighbouring grid cells don’t have overlapping firing fields. Although the grids of nearby neurons do usually have the same spacing and rotation, their firing patterns are typically shifted with respect to each other, in a seemingly random way. This means that if you were to collect the output from enough neighboring grid cells, then at any given moment in time and no matter where the rat is in space, some, but not all of these cells will be firing. You (or a downstream neural circuit) could potentially use this information to track the rat’s relative position within this ‘cognitive map’.

Although the repeating grid pattern might be considered aesthetically pleasing, one problem with this type of coding scheme is that it provides only ambiguous information about the rat’s position. Each neuron fires at a (presumably) infinite number of locations in space, so how could this code ever be used to represent a unique position? One clue to a possible solution lay in the topographic relationship the Mosers uncovered between the spacing of a given cell’s grid pattern and its physical location in the MEC. More dorsal cells (toward the top of the head) were found to have progressively smaller grid spacing than more ventral cells (away from the top of the head). When grid spacing is plotted against dorso-ventral location it looks roughly proportional. See the below figure for some examples (taken from McNaughton et al, 2006). The more dorsal cells (toward the upper part of the image) have smaller grid spacing.

Hafting et al suggested that a unique location could be represented by “integrating over grids with different spacing and orientation”. If you look at the above image you can see that there are very few locations in space where all three cells would be simultaneously firing. If you now imagine looking at the firing fields of 50 or 100 or 500 of these cells there might be only one or two locations in a very big area where all the cells would be firing together. In this way a population of grid cells could code for a single specific location, even though their individual activity is ambiguous. The Mosers and other groups have since elaborated on this idea to suggest that the place cells found in the hippocampus are formed by summing the output from many grid cells (see Solstad et al, 2006).

So why this unusual coding scheme? Why has evolution favoured this surprising triangular grid representation? No-one really knows for sure, but, of course, people have ideas. There are a few things we can already conclude, even without knowing anything about how the grids are generated and with only scant knowledge on how the information is subsequently used in other parts of the rodent brain. I will discuss all of this, and more, in the part 2 of the article.

Refs:

• Microstructure of a spatial map in the entorhinal cortex.
  Torkel Hafting, Marianne Fyhn, Sturla Molden, May-Britt Moser, Edvard I Moser.
  Nature (2005) 436 (7052), 801-6
  PMID: 15965463
• Grid Cells.
  Edvard Moser, May-Britt Moser.
  Scholarpedia (2007), 2(7):3394
  http://www.scholarpedia.org/article/Grid_cells
• Place Cells, Grid Cells, and the Brain’s Spatial Representation System.
  E Moser, E Kropff, M Moser.
  Annu Rev Neurosci (2008) 31, 69-89
  PMID: 18284371
• Path integration and the neural basis of the ‘cognitive map’.
  Bruce L McNaughton, Francesco P Battaglia, Ole Jensen, Edvard I Moser, May-Britt Moser.
  Nat Rev Neurosci (2006) 7 (8), 663-78
  PMID: 16858394
• From grid cells to place cells: a mathematical model.
  Trygve Solstad, Edvard I Moser, Gaute T Einevoll.
  Hippocampus (2006) 16 (12), 1026-31
  PMID: 17094145