Connectionists: paper by Haeusler and Maass on detailed cortical microcircuit models

[Stefan Haeusler]

The paper

"A statistical analysis of information processing properties of 
lamina-specific cortical microcircuit models"

by
Stefan Haeusler, and Wolfgang Maass

(to appear in Cerebral Cortex, 2006)

is now available at
http://cercor.oxfordjournals.org/cgi/reprint/bhj132


ABSTRACT:
A major challenge for computational neuroscience is to understand the 
computational function of lamina-specific synaptic connection patterns 
in stereotypical cortical microcircuits. Previous work on this problem 
had focused on hypothesized specific computational roles of individual 
layers and connections between layers, and had tested these hypotheses 
through simulations of abstract neural network models. We approach this 
problem by studying instead the dynamical system defined by more 
realistic cortical microcircuit models as a whole, and by investigating 
the in fluence which its laminar structure has on the transmission and 
fusion of information within this dynamical system. The circuit models 
that we examine consist of Hodgkin-Huxley neurons with dynamic synapses, 
based on detailed data from [Thomson et al., 2002, Markram et al., 1998] 
and [Gupta et al., 2000]. We investigate to what extent this cortical 
microcircuit template supports the accumulation and fusion of 
information contained in generic spike inputs into layer 4 and layers 
2/3, and how well it makes this information accessible to projection 
neurons in layers 2/3 and layer 5. We exhibit specific computational 
advantages of such data-based lamina-specific cortical microcircuit 
model by comparing its performance with various types of control models 
that have the same components and the same global statistics of neurons 
and synaptic connections, but are missing the lamina-specific structure 
of real cortical microcircuits. We conclude that computer simulations of 
detailed lamina-specific cortical microcircuit models provide new 
insight into computational consequences of anatomical and physiological 
data.


Stefan Haeusler