Abstract
A critical advance for brain–machine interfaces is the establishment of bi-directional communications between the nervous system and external devices. However, the signals generated by a population of neurons are expected to depend in a complex way upon poorly understood neural dynamics. We report a new technique for the identification of the dynamics of a neural population engaged in a bi-directional interaction with an external device. We placed in vitro preparations from the lamprey brainstem in a closed-loop interaction with simulated dynamical devices having different numbers of degrees of freedom. We used the observed behaviors of this composite system to assess how many independent parameters − or state variables − determine at each instant the output of the neural system. This information, known as the dynamical dimension of a system, allows predicting future behaviors based on the present state and the future inputs. A relevant novelty in this approach is the possibility to assess a computational property – the dynamical dimension of a neuronal population – through a simple experimental technique based on the bi-directional interaction with simulated dynamical devices. We present a set of results that demonstrate the possibility of obtaining stable and reliable measures of the dynamical dimension of a neural preparation.
Highlights
We report a new approach to identifying the dynamical properties of neural tissue engaged in a bi-directional interaction with an external device
One can distinguish between (i) motor brain–machine interface (BMI), where a direct communication pathway is established from the nervous system to an external device (Donoghue, 2008; Hochberg et al, 2006); (ii) sensory BMIs, where a direct communication pathway is established from an external device, e.g. an artificial retina, to the nervous system (Counter, 2008; Dowling, 2005; Mokwa, 2007); and (iii) bidirectional BMIs, where a bi-directional direct communication is established between the nervous system and an external device (Bakkum et al, 2007; Karniel et al, 2005; Martinoia et al, 2004)
This paper describes a methodology for applying dynamical systems theory to the study of properties of neural tissue
Summary
We report a new approach to identifying the dynamical properties of neural tissue engaged in a bi-directional interaction with an external device. Population spike trains extracted in real time from extracellular multiunit recording were transformed into a control signal used to drive the external system. In engineering terms, this is called a closed-loop configuration (Bakkum et al, 2008; Chao et al, 2008; Novellino et al, 2007), as the sensory consequences of the control signals are fed-back to the control system to generate new commands. From a mathematical standpoint, when the external input is maintained constant, such a closed-loop system approximates an “autonomous system” whose dynamics are entirely self-contained and do not depend explicitly upon time. This is only an approximation, as external influences, such as the fluctuation of temperature in the room, are most likely to affect the system’s behavior
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