INM Seminar: Talk by Prof. Henry Kennedy

18th June 2014 11:30 AM
18th June 2014 12:30 PM
Bldg. 15.22, Room 3009, first-floor


Stem cell and Brain Research Institute, INSERM U846, Bron, France


Université de Lyon, Université Lyon I, Lyon, France

The Brain in Space

Surprisingly little is known about the statistics of cortical networks due to an absence of investigation of their weighted and spatial properties. Using brain-wide retrograde tracing

experiments in macaque, we are generating a consistent database of between area connections with projection densities, and distances. The network is neither a sparse small-world

graph nor scale-free (Markov et al., 2013). Local connectivity accounts for 80% of labeled neurons, meaning that cortex is heavily involved in local function (Markov et al., 2011).

Importantly link weights, are highly characteristic across animals, follow a heavy-tailed lognormal distribution over 6 orders of magnitude, and decay exponentially with distance

(Markov et al., 2014a).

The statistical properties of the cortex will give insight into the nature of the processing mode of the cortex (Markov and Kennedy, 2013). We are making a weighted network

analysis, this reveals a trade off between local and global efficiencies. An important finding is that a distance rule predicts the binary features, the global and local communication

efficiencies as well as the clustered topography of the graph (Ercsey-Ravasz et al., 2013). These findings underline the importance of weight-based hierarchical layering in

cortical architecture and hierarchical processing, and point to the need to consider the embedded properties of the cortcx (Markov and Kennedy, 2013, Markov et al., 2014b).


Ercsey-Ravasz M, Markov NT, Lamy C, Van Essen DC, Knoblauch K, Toroczkai Z, Kennedy H (2013) A predictive network model of cerebral cortical connectivity based on a distance rule. Neuron 80:184-197.

Markov NT, Ercsey-Ravasz M, Van Essen DC, Knoblauch K, Toroczkai Z, Kennedy H (2013) Cortical high-density counter-stream architectures. Science 342:1238406.

Markov NT, Ercsey-Ravasz MM, Ribeiro Gomes AR, Lamy C, Magrou L, Vezoli J, Misery P, Falchier A, Quilodran R, Gariel MA, Sallet J, Gamanut R, Huissoud C, Clavagnier S, Giroud P, Sappey-Marinier D,

Barone P, Dehay C, Toroczkai Z, Knoblauch K, Van Essen DC, Kennedy H (2014a) A weighted and directed interareal connectivity matrix for macaque cerebral cortex. Cerebral Cortex 24:17-36.

Markov NT, Kennedy H (2013) The importance of being hierarchical. Curr Opin Neurobiol 23:187-194.

Markov NT, Misery P, Falchier A, Lamy C, Vezoli J, Quilodran R, Gariel MA, Giroud P, Ercsey-Ravasz M, Pilaz LJ, Huissoud C, Barone P, Dehay C, Toroczkai Z, Van Essen DC, Kennedy H, Knoblauch K (2011)

Weight Consistency Specifies Regularities of Macaque Cortical Networks. Cerebral Cortex 21:1254-1272.

Markov NT, Vezoli J, Chameau P, Falchier A, Quilodran R, Huissoud C, Lamy C, Misery P, Giroud P, Barone P, Dehay C, Ullman S, Knoblauch K, Kennedy H (2014b) Anatomy of Hierarchy: Feedforward and

feedback pathways in macaque visual cortex. Journal of Comparative Neurology 522:225-259.






Prof. Dr. Markus Diesmann

Institute of Neuroscience and Medicine (INM-6)

Computational and Systems Neuroscience

Institute for Advanced Simulation (IAS-6)

Theoretical Neuroscience


Last Modified: 24.03.2023