Bio-inspired hierarchical polymer fiber-carbon nanotube adhesives

Hierarchical pillar arrays consisting of micrometer-sized polymer setae covered by carbon nanotubes are engineered to deliver the role of spatulae, mimicking the fibrillar adhesive surfaces of geckos. These biomimetic structures conform well and achieve better attachment to rough surfaces, providing a new platform for a variety of applications. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Correlations strike back (again): The case of associative memory retrieval

It has long been recognised that statistical dependencies in neuronal activity need to be taken into account when decoding stimuli encoded in a neural population. Less studied, though equally pernicious, is the need to take account of dependencies between synaptic weights when decoding patterns previously encoded in an auto-associative memory. We show that activity-dependent learning generically produces such correlations, and failing to take them into account in the dynamics of memory retrieval leads to catastrophically poor recall. We derive optimal network dynamics for recall in the face of synaptic correlations caused by a range of synaptic plasticity rules. These dynamics involve well-studied circuit motifs, such as forms of feedback inhibition and experimentally observed dendritic nonlinearities. We therefore show how addressing the problem of synaptic correlations leads to a novel functional account of key biophysical features of the neural substrate.