An experimental prediction: granule cells can perform linearly non-separable computations

Abstract

Multiple studies show how dendrites might extend some neurons computational capacity. Past works most often focus on pyramidal cells which have an extended dendritic tree where voltage can vary independently; pyramidal neurons dendrites emit local spikes turning dendritic branches into local non-linear subunits. However, these studies leave a large fraction of the nervous system unexplored. Here, we study a neuron with a modest dendritic and non-spiking dendrites. Granule cells dendrites do not spike and these cells membrane voltage remain constant over the neuron. This study uses a conjunction of Boolean algebra and biophysical modelling to predict that Granule cells can perform linearly non-separable computations. In a previous study, we found a linearly non-separable Boolean function possible to implement without dendritic spikes, we coined this computation the feature binding problem. In the present work, we use the intrinsic saturation of synaptic conductance to implement this computation in a biophysical model. An integrate and fire, cannot perform such type of computations. Confirming our prediction would change how we understand the nervous system.