Computer-generated holography is an emerging technology for stimulation of neuronal populations with light patterns. A holographic photo-stimulation system may be designed as a powerful research tool or a compact neural interface medical device, such as an optical retinal prosthesis. We present here an overview of the main design issues including the choice of holographic device, field-of-view, resolution, physical size, generation of two- and three-dimensional patterns and their diffraction efficiency, choice of algorithms and computational effort. The performance and characteristics of a holographic pattern stimulation system with kHz frame rates are demonstrated using experimental recordings from isolated retinas.
Neuroprosthetic retinal interfaces depend upon the ability to bypass the damaged photoreceptor layer and directly activate populations of retinal ganglion cells (RGCs). To date, the preferred approach to this task largely relies on electrode array implants. We are currently pursuing two alternative methods for light-based direct activation of the RGCs. The first method is based on applying caged glutamate over the retina and uncaging it locally to obtain RGC excitation. The second method is to artificially cause RGCs to express Channelrhodopsin II (ChR2), a light-gated cation channel. In addition to being non-contact, optical techniques lend themselves relatively easily to a variety of technologies for achieving patterned stimulation with high temporal and spatial resolution. Using the Texas Instruments Digital Light Processing (DLP - DMD) technology, we have developed an optical stimulation system capable of controlled, large-scale, flexible stimulation of the retinal tissue with high temporal accuracy. In preliminary studies, we are performing patterned photo-stimulation experiments using samples of caged fluorescent probes and in rat retinas that were virally transfected with ChR2.
The correlation structure of neural activity is believed to play a major role in the encoding and possibly the decoding of information in neural populations. Recently, several methods were developed for exactly controlling the correlation structure of multi-channel synthetic spike trains (Brette, 2009; Krumin and Shoham, 2009; Macke et al., 2009; Gutnisky and Josic, 2010; Tchumatchenko et al., 2010) and, in a related work, correlation-based analysis of spike trains was used for blind identification of single-neuron models (Krumin et al., 2010), for identifying compact auto-regressive models for multi-channel spike trains, and for facilitating their causal network analysis (Krumin and Shoham, 2010). However, the diversity of correlation structures that can be explained by the feed-forward, non-recurrent, generative models used in these studies is limited. Hence, methods based on such models occasionally fail when analyzing correlation structures that are observed in neural activity. Here, we extend this framework by deriving closed-form expressions for the correlation structure of a more powerful multivariate self- and mutually exciting Hawkes model class that is driven by exogenous non-negative inputs. We demonstrate that the resulting Linear–Non-linear-Hawkes (LNH) framework is capable of capturing the dynamics of spike trains with a generally richer and more biologically relevant multi-correlation structure, and can be used to accurately estimate the Hawkes kernels or the correlation structure of external inputs in both simulated and real spike trains (recorded from visually stimulated mouse retinal ganglion cells). We conclude by discussing the method's limitations and the broader significance of strengthening the links between neural spike train analysis and classical system identification.
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