Synapses on Axon Collaterals of Pyramidal Cells Are Spaced at Random Intervals: A Golgi Study in the Mouse Cerebral Cortex

Biol Cybern. 1994;71(1):1-12. doi: 10.1007/BF00198906.


In this study we investigated the arrangement of synapses on local axon collaterals of Golgi-stained pyramidal neurons in the mouse cerebral cortex. As synaptic markers we considered axonal swellings visible at high magnification under the light microscope. Such axonal swellings coincide with synaptic boutons, as has been demonstrated in a number of combined light and electron microscopic studies. These studies also indicated that, in most cases, one bouton corresponds precisely to one synapse. Golgi-impregnated axonal trees of 20 neocortical pyramidal neurons were drawn with a camera lucida. Axonal swellings were marked on the drawings. Most swellings were 'en passant'; occasionally, they were situated at the tip of short, spine-like processes. On axon collaterals, the average interval between swellings was 4.5 microns. On the axonal main stem, the swellings were always less densely packed than on the collaterals. Statistical analysis of the spatial distribution of the swellings did not reveal any special patterns. Instead, the arrangement of swellings on individual collaterals follows a Poisson distribution. Moreover, the same holds to a large extent for the entire collection of pyramidal cell collaterals. This suggests that a single Poisson process, characterized by only one rate parameter (number of synapses per unit length), describes most of the spatial distribution of synapses along pyramidal cell collaterals. These findings do not speak in favour of a pronounced target specificity of pyramidal neurons at the synaptic level. Instead, our results support a probabilistic model of cortical connectivity.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Axons / ultrastructure
  • Cerebral Cortex / ultrastructure*
  • Cybernetics
  • Mice
  • Models, Neurological
  • Pyramidal Cells / ultrastructure*
  • Staining and Labeling
  • Stochastic Processes
  • Synapses / ultrastructure