Use of partially overlapping cells

Celia

Overlap

_Cellb

_Cellb

4.9 Hz _{1.7 Hz} 0.3 Hz All spikes occur

in field of cell a. 2.9 Hz 15.3 Hz 7.1 Hz In the square spikes from spaceW

trial. CeU b square

Irial*^

Circle

Square

Figure 6cx. Illustration of the cutting procedure. A) B) and C)

See Figures and Explanation on opposite page. D) Example of the use of partially overlapping cells.

Both cells a and b fire in the circle. Cell b does not fire in the square. An artificial, “overlap” cell is cut

according to the procedure shown on the opposite page. Sections I and II show the firing rate maps for the cells in the square (I) and circle (II). The right hand column in I and II shows raw spike-location data superimposed upon the firing rate maps. Section III shows, for each trial, 2 of the 6 available scatterplots plotting spikes in terms of peak-to-peak amplitude on two of four channels. The larger plot shows the spikes on channel 2 vs 4, the smaller plots show them on channel 1 vs 3.

Note that the firing of “cell b” in the square is in cell a’s field.

Small-interval dashed arrow indicates region of North-West region firing in “Overlap” cell map possibly due to cell b. Larger-interval dashed arrow indicates region of Central region of firing in cell b map possibly due to cell a.

two, three, or even four trials are superimposed, thus creating a generalised tetrode cluster space (see Part A o f Figure 2cx). It is the template (in the form o f a set o f ellipses, each ellipse corresponding to one virtual cell) derived from this cluster space, that served to cut all the cells in all the trials on that day (See Parts B and C o f Figure 2cx). It was not permitted to fine-tune a trial’s template generated cluster cut, by cutting individual spikes, noise etc. If the cut seemed obviously misleading, it was only permitted to change the general template, and start again.

It was sometimes necessary to use cells which partially overlap in the superimposed tetrode scatterplot template space for a given day. (Note that this kind o f cell will often be completely isolated from other cells on the tetrode it is recorded from, in the trial in which the cell fires.) This is necessary because otherwise one introduces a sampling bias against cells which only fire in one shape. Accordingly, the overlap o f two cells was sometimes cut out and ignored. O f course, it implies a reduction in the reported absolute peak firing rate o f the two cells in question. Part D o f Figure 2cx shows an example o f the use o f partially overlapping cells. I would estimate that between 5% to 20% o f cells may be affected in this way.

As stated above, shown by Harris et al (2000) human operators using manual cutting tend to have consistent biases. In my opinion, the cutting procedure used in the present study tends to produce more false negative than false positive errors.

Data analysis - derivation of place field characteristics

There is no standard protocol used to construct and present place fields. The

used recently in the O ’Keefe laboratory (O ’Keefe and Burgess, 1996; Jeffery and O ’Keefe, 1999). The software used is the previously mentioned TINT program (written by Neil Burgess).

To determine place field characteristics (eg. peak firing rate, and location o f peak firing rate), a boxcar averaging process was employed, which converts the raw spike data into false-colour contour firing rate maps. This process is described below, after considering the spatial framework used for analysis.

The camera viewing area was divided into a 64x64 bin grid system. The camera was an analogue camera, with digitisation performed by the tracker at 10 bit resolution. Thus its spatial view is divided into a theoretical maximum o f 1024 x 1024 spatial units. In practice, the total possible camera viewing area is 768 pixels in the x dimension, and 574 pixels in the y dimension. A mask created by the software

excluded all but 400 x 400 pixels. The environments are always centred, every trial at the centre o f this 400 x 400 pixel coordinate frame. The analysis software (TINT) places all coordinate frames less than or equal to 5 1 2 x 5 1 2 pixels within a 5 1 2 x 5 1 2 viewing area. It is this “camera viewing area” that is divided into 64 x 64 bins. Accordingly 1 bin is 8 pixels long. Thus the minimal spatial unit in the TINT analysis software is 8 pixels, while the spatial resolution o f the camera/tracker is 1 p ix e l. In experiment 1, 100 cm equals 300 pixels. In experiment 2 using a Trespa platform raised from the floor, 100 cm equals 332 pixels. (In practice, particularly with the “morph box” described below, the two-dimensional filled squares and circles “drawn” by the LEDs on the rats’ heads differed (within the same shape) by small amounts, but

The effects of smoothing