Cyber-Workstation for Computational Neuroscience

Abstract

A Cyber-Workstation (CW) to study in vivo, real-time interactions between computational models and large-scale brain subsystems during behavioral experiments has been designed and implemented. The design philosophy seeks to directly link the in vivo neurophysiology laboratory with scalable computing resources to enable more sophisticated computational neuroscience investigation. The architecture designed here allows scientists to develop new models and integrate them with existing models (e.g. recursive least-squares regressor) by specifying appropriate connections in a block-diagram. Then, adaptive middleware transparently implements these user specifications using the full power of remote grid-computing hardware. In effect, the middleware deploys an on-demand and flexible neuroscience research test-bed to provide the neurophysiology laboratory extensive computational power from an outside source. The CW consolidates distributed software and hardware resources to support time-critical and/or resource-demanding computing during data collection from behaving animals. This power and flexibility is important as experimental and theoretical neuroscience evolves based on insights gained from data-intensive experiments, new technologies and engineering methodologies. This paper describes briefly the computational infrastructure and its most relevant components. Each component is discussed within a systematic process of setting up an in vivo, neuroscience experiment. Furthermore, a co-adaptive brain machine interface is implemented on the CW to illustrate how this integrated computational and experimental platform can be used to study systems neurophysiology and learning in a behavior task. We believe this implementation is also the first remote execution and adaptation of a brain-machine interface.

Towards a real-time interface between a biomimetic model of sensorimotor cortex and a robotic arm

“…The monkey will receive visual and sensory feedback and then modulate its brain state, affecting the signals it sends to the sensorimotor cortex model. This type of system will be a new form of brain-machine interface where the robotic and biological sides are both learning to work together (Digiovanna et al, 2010; Sanchez et al, 2012). …”

Use of a Bayesian maximum-likelihood classifier to generate training data for brain–machine interfaces

“…For BMI devices to translate to real clinical applications, decoding algorithms need to be based on assumptions that are easily met outside of an experimental setting [17,18]. Given present technological limitations, a low number of potentially unstable neuronal units must be assumed from day to day [19], driving a need for decoding algorithms which (a) are not dependent upon a large number of neurons for control, (b) are adaptable to alternative sources of neuronal input such as local field potentials (LFPs), and (c) require only marginal training data for day-to-day calibrations.…”

Computer modeling of ischemic stroke

“…Such a system can be greatly improved by providing haptic feedback to the system to close the sensorimotor loop. One interesting aspect of BMI is the occurrence of co-adaptation when a plastic artificial system must keep adapting to a brain which is itself adapting as it learns to use the artificial system (Digiovanna et al, 2010; Li et al, 2015). …”