Timing over Tuning: Overcoming the Shortcomings of a Line Attractor during a Working Memory Task

How the brain stores information about a sensory stimulus in working memory is not completely known. Clues about the mechanisms responsible for working memory can be gleaned by recording from neurons during the performance of a delayed response task. I focus on the data recorded during such an experiment, a classic tactile discrimination task. I describe how the observed variability in the firing rate during a trial suggests that the type of attractor that is responsible for holding the stimulus information is not a fixed-point type attractor. I propose an alternate mechanism to a line attractor that allows the network to hold the value of an analog stimulus variable for the duration of the delay period, but rather than maintain a constant level of activity, the cells' firing rate varies throughout the delay period. I describe how my proposed mechanism offers a substantial advantage over a line attractor: The tuning requirements of cell to cell connections are greatly eased from that of a line attractor. To accommodate a change in the length of the delay period, I show that the network can be altered by changing a single parameter - the timing of an executive signal that originates outside of the network. To demonstrate the mechanism, as well as the tuning benefits, I use a well known model of propagation in neuronal networks.
Author Summary The ability to retain stimulus information after the stimulus has ceased is important for survival. The term “working memory” refers to storage of stimulus information for a short period of time, so that this information can be recalled for a decision making process. A common way to probe for the cellular basis of working memory is recording of neurons during a delayed response task. This study focuses on one of these studies - the now classic experiments of Romo et al.. This experiment demonstrates that the frequency of a tactile vibration is held in memory using a type of encoding where the cellular output depends monotonically on the stimulus variable (frequency). In this paper, I develop a model that accounts for a number of features found in the data. Using the model, I am able to account for a diversity of cellular responses, as well as variability during a trial. This paper builds on previous modeling studies of this data set. The advance is an executive input that controls the behavior of the network, and reduces the burden of tuning compared to previous models.