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A Phenomenological Theory of Spatially Structured Local Synaptic Connectivity.
- Source :
- PLoS Computational Biology; Jun2005, Vol. 1 Issue 1, p74-85, 12p, 3 Diagrams, 1 Graph
- Publication Year :
- 2005
-
Abstract
- The structure of local synaptic circuits is the key to understanding cortical function and how neuronal functional modules such as cortical columns are formed. The central problem in deciphering cortical microcircuits is the quantification of synaptic connectivity between neuron pairs. I present a theoretical model that accounts for the axon and dendrite morphologies of pre- and postsynaptic cells and provides the average number of synaptic contacts formed between them as a function of their relative locations in three-dimensional space. An important aspect of the current approach is the representation of a complex structure of an axonal/dendritic arbor as a superposition of basic structures--synaptic clouds. Each cloud has three structural parameters that can be directly estimated from two-dimensional drawings of the underlying arbor. Using empirical data available in literature, I applied this theory to three morphologically different types of cell pairs. I found that, within a wide range of cell separations, the theory is in very good agreement with empirical data on (i) axonal-dendritic contacts of pyramidal cells and (ii) somatic synapses formed by the axons of inhibitory interneurons. Since for many types of neurons plane arborization drawings are available from literature, this theory can provide a practical means for quantitatively deriving local synaptic circuits based on the actual observed densities of specific types of neurons and their morphologies. It can also have significant implications for computational models of cortical networks by making it possible to wire up simulated neural networks in a realistic fashion. [ABSTRACT FROM AUTHOR]
- Subjects :
- NEURAL circuitry
ELECTROPHYSIOLOGY
SYNAPSES
NERVE endings
BIOINFORMATICS
Subjects
Details
- Language :
- English
- ISSN :
- 1553734X
- Volume :
- 1
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- PLoS Computational Biology
- Publication Type :
- Academic Journal
- Accession number :
- 17987805
- Full Text :
- https://doi.org/10.1371/journal.pcbi.0010011