Assessing the efficacy of visual prostheses by decoding ms-LFPs: application to retinal implants.

Visual prostheses are brain-computer interfaces that are implanted in early processing stages of the visual system of blind patients. In an effort to induce light sensations, visual prostheses inject, via arrays of stimulating electrodes, spatiotemporal trains of current pulses which excite the adjacent neural tissue. Human experiments with current state-of-the art retinal prostheses have revealed that, although visual percepts can be elicited by electrical stimulation, these percepts are not closely related to the spatial patterns of stimulation. One of the main reasons for this failure is that present methods of prosthetic stimulation result in non-specific activation of multiple retinal pathways. Recent evidence, however, suggests that the specificity of neural activation can be increased by manipulations of the spatiotemporal parameters of stimulation. Before these notions are evaluated in human experiments, which are subjective and prone to patient fatigue and frustration, it is imperative that they are assessed in animal models using cortical recordings. Toward this end, we have developed a computational method for analyzing the cortical multi-site local field potential (ms-LFP) evoked in response to electrical stimulation of a site presynaptic to where LFPs are recorded. This method applies a nonlinear decoding technique on the recorded ms-LFP signal to quantify the information transmitted downstream from the stimulation site. Validation of this method using an implant attached to the epiretinal surface of cats and ms-LFP recordings from layer 4 of cat primary visual cortex, demonstrates that the spatial origin, the duration and the amplitude of injected current pulses can all be decoded simultaneously from single-trial ms-LFP responses. Our findings indicate that the developed method is a highly sensitive probe for characterizing the efficacy of visual prosthetic stimulation.

Spatial and temporal receptive fields of geniculate and cortical cells and directional selectivity.

The spatio-temporal receptive fields (RFs) of cells in the macaque monkey lateral geniculate nucleus (LGN) and striate cortex (V1) have been examined and two distinct sub-populations of non-directional V1 cells have been found: those with a slow largely monophasic temporal RF, and those with a fast very biphasic temporal response. These two sub-populations are in temporal quadrature, the fast biphasic cells crossing over from one response phase to the reverse just as the slow monophasic cells reach their peak response. The two sub-populations also differ in the spatial phases of their RFs. A principal components analysis of the spatio-temporal RFs of directional V1 cells shows that their RFs could be constructed by a linear combination of two components, one of which has the temporal and spatial characteristics of a fast biphasic cell, and the other the temporal and spatial characteristics of a slow monophasic cell. Magnocellular LGN cells are fast and biphasic and lead the fast-biphasic V1 subpopulation by 7 ms; parvocellular LGN cells are slow and largely monophasic and lead the slow monophasic V1 sub-population by 12 ms. We suggest that directional V1 cells get inputs in the approximate temporal and spatial quadrature required for motion detection by combining signals from the two non-directional cortical sub-populations which have been identified, and that these sub-populations have their origins in magno and parvo LGN cells, respectively.

Some transformations of color information from lateral geniculate nucleus to striate cortex.

We have recorded the responses of single cells in the lateral geniculate nucleus (LGN) and striate cortex of the macaque monkey. The response characteristics of neurons at these successive visual processing levels were examined with isoluminant gratings, cone-isolating gratings, and luminance-varying gratings. The main findings were: (i) Whereas almost all parvo- and konio-cellular LGN cells are of just two opponent-cell types, either differencing the L and M cones (L(o) and M(o) cells), or the S vs. L + M cones (S(o) cells), relatively few striate cortex simple cells show chromatic responses along these two cardinal LGN axes. Rather, most are shifted away from these LGN chromatic axes as a result of combining the outputs (or the transformed outputs) of S(o) with those of L(o) and/or M(o) cells. (ii) LGN cells on average process color information linearly, exhibiting sinusoidal changes in firing rate to isoluminant stimuli that vary sinusoidally in cone contrast as a function of color angle. Some striate cortex simple cells also give linear responses, but most show an expansive response nonlinearity, resulting in narrower chromatic tuning on average at this level. (iii) There are many more +S(o) than -S(o) LGN cells, but at the striate cortex level -S(o) input to simple cells is as common as +S(o) input. (iv) Overall, the contribution of the S-opponent path is doubled at the level of the striate cortex, relative to that at the LGN.

Spatial-frequency organization in primate striate cortex.

We measured the spatial-frequency tuning of cells at regular intervals along tangential probes through the monkey striate cortex and correlated the recording sites with the cortical cytochrome oxidase (CytOx) patterns to address three questions with regard to the cortical spatial-frequency organization. (i) Is there a periodic anatomical arrangement of cells tuned to different spatial-frequency ranges? We found there is, because the spatial-frequency tuning of cells along tangential probes changed systematically, varying from a low frequency to a middle range to high frequencies and back again repeatedly over distances of about 0.6-0.7 mm. (ii) Are there just two populations of cells, low-frequency and high-frequency units, at a given eccentricity (perhaps corresponding to the magno- and parvocellular geniculate pathways) or is there a continuum of spatial-frequency peaks? We found a continuum of peak tuning. Most cells are tuned to intermediate spatial frequencies and form a unimodal rather than a bimodal distribution of cell peaks. Furthermore, the cells with different peak frequencies were found to be continuously and smoothly distributed across a module. (iii) What is the relation between the physiological spatial-frequency organization and the regions of high CytOx concentration ("blobs")? We found a systematic correlation between the topographical variation in spatial-frequency tuning and the modular CytOx pattern, which also varied continuously in density. Low-frequency cells are at the center of the blobs, and cells tuned to increasingly higher spatial frequencies are at increasing radial distances.