Volume 15 Supplement 1
Hebbian-inspired rewiring of a random network replicates pattern of selectivity seen in PFC
© Lindsay et al; licensee BioMed Central Ltd. 2014
Published: 21 July 2014
Responses of neurons in pre-frontal cortex (PFC) are very diverse and often depend on complex non-linear combinations of task-relevant variables (a property known as mixed selectivity). We recently showed that this type of selectivity is a signature of high dimensional neural representations and can be important for performing complex cognitive tasks . Previous modeling work has shown that mixed selectivity can arise when cells receive fixed random synaptic inputs from populations that represent the task-relevant variables (see, e.g. ). Here we show by analyzing the data of  that these simple models only partially explain the mixed selectivity observed in the data.
The data we analyzed is from a delayed-response task wherein monkeys are presented a sequence of two image cues on each trial, followed by a 1-second delay period after which the monkey either saccades to them in the correct order when a display of images is presented (“recall task”) or reports if a presented sequence is a match (“recognition task”). We characterized the statistics of the recorded PFC responses by determining the number of mixed selective cells. Specifically, we performed a 3-way ANOVA test on firing rate responses using the three task parameters (task type, cue 1, and cue 2) as factors. The requirement for mixed selectivity was for at least one interaction term to have a significant coefficient (p-value <0.05). Cells were classified as “pure selectivity” neurons if they had at least one significant pure task parameter term and no significant interaction term.
Initially, we modeled PFC as a population of cells receiving random connections from a population of binary input cells each representing a task-relevant variable: a task type, cue 1 image, or cue 2 image. However the statistics of the neuronal responses of the model only partially reproduced the data.
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