System identification of the nonlinear dynamics in the thalamocortical circuit in response to patterned thalamic microstimulation in vivo.
| Autor: | Millard DC; Department of Biomedical Engineering, Georgia Institute of Technology/Emory University, Atlanta, GA 30332, USA., Wang Q, Gollnick CA, Stanley GB |
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| Jazyk: | angličtina |
| Zdroj: | Journal of neural engineering [J Neural Eng] 2013 Dec; Vol. 10 (6), pp. 066011. Date of Electronic Publication: 2013 Oct 25. |
| DOI: | 10.1088/1741-2560/10/6/066011 |
| Abstrakt: | Objective: Nonlinear system identification approaches were used to develop a dynamical model of the network level response to patterns of microstimulation in vivo. Approach: The thalamocortical circuit of the rodent vibrissa pathway was the model system, with voltage sensitive dye imaging capturing the cortical response to patterns of stimulation delivered from a single electrode in the ventral posteromedial thalamus. The results of simple paired stimulus experiments formed the basis for the development of a phenomenological model explicitly containing nonlinear elements observed experimentally. The phenomenological model was fit using datasets obtained with impulse train inputs, Poisson-distributed in time and uniformly varying in amplitude. Main Results: The phenomenological model explained 58% of the variance in the cortical response to out of sample patterns of thalamic microstimulation. Furthermore, while fit on trial-averaged data, the phenomenological model reproduced single trial response properties when simulated with noise added into the system during stimulus presentation. The simulations indicate that the single trial response properties were dependent on the relative sensitivity of the static nonlinearities in the two stages of the model, and ultimately suggest that electrical stimulation activates local circuitry through linear recruitment, but that this activity propagates in a highly nonlinear fashion to downstream targets. Significance: The development of nonlinear dynamical models of neural circuitry will guide information delivery for sensory prosthesis applications, and more generally reveal properties of population coding within neural circuits. |
| Databáze: | MEDLINE |
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