An automated calibration system for in vivo neural network study

A feedback control system promises improvements in neuroprosthetic performance and clinical therapies. As the experiments move to chronic, and require longer monitoring time, manual control would be difficult. The calibration process must continuously be repeated due to the variation of the electrophysiological state of the neural network in space and time. To increase the efficiency of these processes, we have developed an automated calibration system. The system is validated in vivo in the hippocampus region. The stimulation voltage threshold is determined through an automatic process. The effects of pulse repetition are observed and realized in ‘real-time’.

[1]  G.A. Clark,et al.  Automated Stimulus-Response Mapping of High-Electrode-Count Neural Implants , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[2]  Steve M. Potter,et al.  A Low-Cost Multielectrode System for Data Acquisition Enabling Real-Time Closed-Loop Processing with Rapid Recovery from Stimulation Artifacts , 2009, Front. Neuroeng..

[3]  Jose M. Carmena,et al.  A System for Neural Recording and Closed-Loop Intracortical Microstimulation in Awake Rodents , 2009, IEEE Transactions on Biomedical Engineering.

[4]  Jeroen Lammertyn,et al.  A hybrid stimulation artifact reduction scheme for microfabricated deep brain stimulation and recording probes , 2008 .

[5]  Georges Gielen,et al.  Modeling, Design and Implementation of In Vivo and In Vitro Neuron-Chip Interfaces , 2011 .

[6]  Steve M. Potter,et al.  Controlling Bursting in Cortical Cultures with Closed-Loop Multi-Electrode Stimulation , 2005, The Journal of Neuroscience.

[7]  Steve M. Potter,et al.  Effective parameters for stimulation of dissociated cultures using multi-electrode arrays , 2004, Journal of Neuroscience Methods.

[8]  Daryl R Kipke,et al.  Development of Closed-Loop Neural Interface Technology in a Rat Model: Combining Motor Cortex Operant Conditioning With Visual Cortex Microstimulation , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[9]  Wolfgang Eberle,et al.  Planar 2D-Array Neural Probe for Deep Brain Stimulation and Recording (DBSR) , 2009 .