A quantitative analysis of the local connectivity between pyramidal neurons in layers 2/3 of the rat visual cortex

By B. Hellwig. 2000

344 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

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Citations in the corpus

Structural Synaptic Plasticity Has High Memory Capacity and Can Explain Graded Amnesia, Catastrophic Forgetting, and the Spacing Effect (2014, background): “A key to understanding the role of structural plasticity in memory has to do with the observation that the brain, even its most densely connected local circuits, is far from being fully connected [24,25].”
The small world of the cerebral cortex (2007, background): “Detailed anatomical studies demonstrate an exponential or Gaussian fall-off of connection probability with increasing spatial separation of two neurons (Braitenberg and Schüz, 1998; Hellwig, 2000; Liley and Wright, 1994).”
Scene segmentation by spike synchronization in reciprocally connected visual areas. II. Global assemblies and synchronization on larger space and time scales (2002, background): “that local connections of pyramidal neurons extend only a few millimeters (Braitenberg and Schu¨ z 1991; Hellwig 2000 ), our model should rather be interpreted as the bidirectional connection of two small patches of cortex.”
Synchronization and pattern separation in spiking associative memories and visual cortical areas (2004, background): “Actually the probability of a connection between two neurons decreases rather continuously with the distance of the neurons [3, 27, 65].”
Memory Capacities for Synaptic and Structural Plasticity (2010, background): “Figure 3 presents results for a network with n = 100,000 neurons, a number that corresponds roughly to the number of neurons below 1 square millimeter of cortex surface (Braitenberg & Schüz, 1991; Hellwig, 2000).”
Complex brain networks: graph theoretical analysis of structural and functional systems (2009, background): “Neurons and brain regions that are spatially close have a relatively high probability of being connected, whereas connections between spatially remote neurons or brain regions are less likel”
The brainstem reticular formation is a small-world, not scale-free, network (2006, methodology): “In contrast, McCulloch and colleague's RF model (Kilmer et al. 1969) used a distancedependent distribution (William Kilmer, personal communication ), typical of models of neural connectivity (Hellwig 2000).”
Graph analysis of the human connectome: Promise, progress, and pitfalls (2013, positive): “Suppose that structural connectivity drops off inversely with inter-node distance (Hellwig, 2000; Kaiser and Hilgetag, 2004; Freeman and Breakspear, 2007; Alexander-Bloch, AC C EP TE D M AN U SC R IP T Vertes, et al., 2012), that is where α indices the strength of this effect ranging from 0 (no…”
Optimal Hierarchical Modular Topologies for Producing Limited Sustained Activation of Neural Networks (2009, positive): “The constraint of a constant average node degree was motivated by comparative studies showing largely constant numbers of connections per neuron across many species (Hellwig, 2000; Schüz and Braitenberg, 2002; Binzegger et al., 2004; Striedter, 2004; Changizi and Shimojo, 2005; Schüz et al., 2006).”
Nonoptimal Component Placement, but Short Processing Paths, due to Long-Distance Projections in Neural Systems (2006, background): “A trend toward wiring minimization is apparent in the distributions of projection lengths for various neural systems, which show that most neuronal projections are short [9–12].”