Generation of multi-layered protein bodies in for the encapsulation of vaccine antigens.

The ability of plants to assemble particulate structures such as virus-like particles and protein storage organelles allows the direct bioencapsulation of recombinant proteins during the manufacturing process, which holds promise for the development of new drug delivery vehicles. Storage organelles found in plants such as protein bodies (PBs) have been successfully used as tools for accumulation and encapsulation of recombinant proteins. The fusion of sequences derived from 27-kDa-γ-zein, a major storage protein of maize, with a protein of interest leads to the incorporation of the chimeric protein into the stable and protected environment inside newly induced PBs.

Nonlinear Dendritic Coincidence Detection for Supervised Learning.

Cortical pyramidal neurons have a complex dendritic anatomy, whose function is an active research field. In particular, the segregation between its soma and the apical dendritic tree is believed to play an active role in processing feed-forward sensory information and top-down or feedback signals. In this work, we use a simple two-compartment model accounting for the nonlinear interactions between basal and apical input streams and show that standard unsupervised Hebbian learning rules in the basal compartment allow the neuron to align the feed-forward basal input with the top-down target signal received by the apical compartment.

Local Homeostatic Regulation of the Spectral Radius of Echo-State Networks.

Recurrent cortical networks provide reservoirs of states that are thought to play a crucial role for sequential information processing in the brain. However, classical reservoir computing requires manual adjustments of global network parameters, particularly of the spectral radius of the recurrent synaptic weight matrix. It is hence not clear if the spectral radius is accessible to biological neural networks.