A quantitative analysis of the local connectivity between pyramidal neurons in layers 2/3 of the rat visual cortex.
This study provides a detailed quantitative estimate for local synaptic connectivity between neocortical pyramidal neurons. A new way of obtaining such an estimate is presented. In acute slices of the rat visual cortex, four layer 2 and four layer 3 pyramidal neurons were intracellularly injected with biocytin. Axonal and dendritic arborizations were three-dimensionally reconstructed with the aid of a computer-based camera lucida system. In a computer experiment, pairs of pre- and postsynaptic neurons were formed and potential synaptic contacts were calculated. For each pair, the calculations were carried out for a whole range of distances (0 to 500 microm) between the presynaptic and the postsynaptic neuron, in order to estimate cortical connectivity as a function of the spatial separation of neurons. It was also differentiated whether neurons were situated in the same or in different cortical layers. The data thus obtained was used to compute connection probabilities, the average number of contacts between neurons, the frequency of specific numbers of contacts and the total number of contacts a dendritic tree receives from the surrounding cortical volume. Connection probabilities ranged from 50% to 80% for directly adjacent neurons and from 0% to 15% for neurons 500 microm apart. In many cases, connections were mediated by one contact only. However, close neighbors made on average up to 3 contacts with each other. The question as to whether the method employed in this study yields a realistic estimate of synaptic connectivity is discussed. It is argued that the results can be used as a detailed blueprint for building artificial neural networks with a cortex-like architecture.
Hellwig
B
B
Max-Planck-Institut für biologische Kybernetik, Spemannstr. 38, D-72076 Tübingen, Germany. hellwigb@nz11.ukl.uni-freiburg.de
D000818:Animals; D001369:Axons / Q000502:physiology; D002452:Cell Count; D048429:Cell Size; D003198:Computer Simulation; D003712:Dendrites / Q000502:physiology; D007091:Image Processing, Computer-Assisted; D066298:In Vitro Techniques; D008239:Lysine / Q000031:analogs & derivatives; D008959:Models, Neurological; D009415:Nerve Net / Q000166:cytology* / Q000254:growth & development*; D016571:Neural Networks, Computer; D011336:Probability; D017966:Pyramidal Cells / Q000166:cytology* / Q000254:growth & development; D051381:Rats; D011917:Rats, Inbred Lew; D013569:Synapses / Q000502:physiology; D014793:Visual Cortex / Q000166:cytology* / Q000254:growth & development*
C013411:biocytin; D008239:Lysine