Complexity without chaos: Plasticity within random recurrent networks generates robust timing and motor control

It is widely accepted that the complex dynamics characteristic of recurrent neural circuits contributes in a fundamental manner to brain function. Progress has been slow in understanding and exploiting the computational power of recurrent dynamics for two main reasons: nonlinear recurrent networks often exhibit chaotic behavior and most known learning rules do not work in robust fashion in recurrent networks. Here we address both these problems by demonstrating how random recurrent networks (RRN) that initially exhibit chaotic dynamics can be tuned through a supervised learning rule to generate locally stable neural patterns of activity that are both complex and robust to noise. The outcome is a novel neural network regime that exhibits both transiently stable and chaotic trajectories. We further show that the recurrent learning rule dramatically increases the ability of RRNs to generate complex spatiotemporal motor patterns, and accounts for recent experimental data showing a decrease in neural variability in response to stimulus onset

Genome wide analysis of gene expression changes in skin from patients with type 2 diabetes

Non-healing chronic ulcers are a serious complication of diabetes and are a major healthcare problem. While a host of treatments have been explored to heal or prevent these ulcers from forming, these treatments have not been found to be consistently effective in clinical trials. An understanding of the changes in gene expression in the skin of diabetic patients may provide insight into the processes and mechanisms that precede the formation of non-healing ulcers. In this study, we investigated genome wide changes in gene expression in skin between patients with type 2 diabetes and non-diabetic patients using next generation sequencing. We compared the gene expression in skin samples taken from 27 patients (13 with type 2 diabetes and 14 non-diabetic). This information may be useful in identifying the causal factors and potential therapeutic targets for the prevention and treatment of diabetic related diseases