Abstract
Long-term information associated with neuronal memory resides in dendritic spines. However, spines can have a limited size due to metabolic and neuroanatomical constraints, which should effectively limit the amount of encoded information in excitatory synapses. This study investigates how much information can be stored in the sizes of dendritic spines, and whether is it optimal in any sense? It is shown here, using empirical data for several mammalian brains across different regions and physiological conditions, that dendritic spines nearly maximize entropy contained in their volumes and surface areas for a given mean size. This result is essentially independent of the type of a fitting distribution to size data, as both short- and heavy-tailed distributions yield similar nearly 100% information efficiency in the majority of cases, although heavy-tailed distributions slightly better fit the data. On average, the highest information is contained in spine volume, and the lowest in spine length or spine head diameter. Depending on a species and brain region, a typical spine can encode between 6.1 and 10.8 bits of information in its volume, and 3.1−8.1 bits in its surface area. Our results suggest a universality of entropy maximization in spine volumes and areas, which can be a new principle of memory storing in synapses.
Significance statement It is believed that memory in the brain is stored in the parts of excitatory synapses called dendritic spines. But how efficient is the memory capacity given synaptic size variability? Generally, the larger the synapse the more information can be packed in its structure. However, this process comes at some cost, as larger synapses use more metabolic energy and brain tissue. Thus, from a theoretical point of view, there exists a benefit-cost trade-off for storing long-term information in dendritic spines, with a unique optimal solution set by an average spine size. We show that volumes and areas of actual spines in different parts of the brain across different mammals are very close to that optimal theoretical solution for storing information. This means that synaptic information is maximally optimized regardless of brain size, region, and physiological condition, which is a remarkable result.
Competing Interest Statement
The authors have declared no competing interest.