Even fruitflies can 'count': Scientists create model that could be used to understand how human brains tally data CSHL NatureComms
responses provides evidence for the presence of more than two discrete count categories along the novelty-familiarity axis. To show this, the activity of MBON-must be significantly different across multiple experiences of the same odor. At some point, the difference in activity between successive experiences becomes indistinguishable, and this is where the “many” category kicks in, indicating that responses to all subsequent experiences are essentially the same.
We also quantified the separability of all pairs of count categories using a simple response threshold discrimination model . The area under the ROC curve remained high when discriminating between 1, 2, and 3 and nearly all other categories, but was considerably degraded for subsequent categories, further supporting the existence of four robust count categories.encodes frequency information about odor memories into four distinct categories along the novelty-familiarity axis.
Our proposed neural count sketch data structure has four properties: it provides counts that are stimulus-specific; it has a large storage capacity, that is, it requires only a few synapses per unique item; it offers robustness, that is, the ability to generalize counts across noisy versions of the same item; and it is fast and automatic, providing frequency estimates of inputs after two synapses of computation, requiring only tens to hundreds of milliseconds.
First, how might downstream mechanisms robustly read-out frequency estimates and use them to modify behavior? For the anti-Hebbian model, this would require grouping the firing rate of the 1-2-3-many counting neuron into four discrete categories. One option is to convert this continuous firing rate into a discrete representation .
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