Figure 3.

The contribution of untuned cells for encoding position. We show an extreme situation in which one simulated neuron has the same activity distribution when the animal is in two different locations of the arena. Hence the neuron is not selective to position. Nevertheless, for a decoder this neuron can be as important as other selective neurons due to its contribution to the population coding. a) Activity of two simulated neurons as a function of time. Top: The simulated animal visits the same discrete location twice (location A in green, location B in red). Bottom: Simulated traces around the time of passage through each location. Different responses for the two neurons are elicited by different experiences, for example due to the different direction of motion. b) Example of how place cells and non place-cells can be equally important for encoding the position of the animal. In the scatter plot, the x-axis represents the average activity of the first neuron during one pass and the y-axis is the activity of the second neuron. Each point in the space represents an average population response in a single pass. Their responses are typically highly variable and are scattered around their mean values. The two neurons in the example have very different activity profiles: the first has a strong spatial tuning (place cell) while the second has only a weak tuning. The distributions of their activities in each location, reported along the axis, only partially (neuron 1, place cells) or almost completely overlap (neuron 2). Despite this variability in the single neuron responses, the neural representations at the population level are well separated, making it possible for a linear decoder (blue dashed line) to discriminate them with high accuracy. The resulting decoder’s weight vector has two equal components corresponding to the importance of the two neurons in encoding position. In this example both neurons are important for encoding position despite their very different tuning properties.