Cortical visual prostheses are intended to restore vision by targeted electrical stimulation of the visual cortex. The perception of spots of light, called phosphenes, resulting from microstimulation of the visual pathway, suggests the possibility of creating meaningful percept made of phosphenes. However, to date electrical stimulation of V1 has still not resulted in perception of phosphenated images that goes beyond punctate spots of light. In this review, we summarize the clinical and experimental progress that has been made in generating phosphenes and modulating their associated perceptual characteristics in human and macaque primary visual cortex (V1). We focus specifically on the effects of different microstimulation parameters on perception and we analyse key challenges facing the generation of meaningful artificial percepts. Finally, we propose solutions to these challenges based on the application of supervised learning of population codes for spatial stimulation of visual cortex.

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A main challenge for the development of cortical visual prostheses is to spatially localize individual spots of light, called phosphenes, by assigning appropriate stimulating parameters to implanted electrodes. Imitating the natural responses to phosphene-like stimuli at different positions can help in designing a systematic procedure to determine these parameters. The key characteristic of such a system is the ability to discriminate between responses to different positions in the visual field. While most previous prosthetic devices have targeted the primary visual cortex, the extrastriate cortex has the advantage of covering a large part of the visual field with a smaller amount of cortical tissue, providing the possibility of a more compact implant. Here, we studied how well ensembles of Multiunit activity (MUA) and Local Field Potentials (LFPs) responses from extrastriate cortical visual area V4 of a behaving macaque monkey can discriminate between two-dimensional spatial positions. We found that despite the large receptive field sizes in V4, the combined responses from multiple sites, whether MUA or LFP, has the capability for fine and coarse discrimination of positions. We identified a selection procedure that could significantly increase the discrimination performance while reducing the required number of electrodes. Analysis of noise correlation in MUA and LFP responses showed that noise correlations in LFP responses carry more information about the spatial positions. Overall, these findings suggest that spatial positions could be localized with patterned stimulation in extrastriate area V4. reconstruction of the natural responses of visual cortex to phosphene-like stimuli at different positions in the visual field can assist at designing a systematic procedure to control the position of multiple phosphenes by tuning the stimulation parameters. The key characteristic of such a system then, would be its capability to discriminate between responses to phosphene-like visual stimuli at different spatial positions.Multielectrode recordings are necessary to obtain both the spatial and temporal sampling properties required for the representation of position [13,14]. However, the majority of previous multielectrode recordings and stimulation studies in the visual cortex of monkeys have been performed in V1 [6,8,[15][16][17][18][19][20][21][22][23], which is quite large relative to standard recording arrays. Therefore, a multielectrode array in V1 can often sample only a tiny region of visual space. In contrast, extrastriate visual areas generally contain retinotopic maps that are physically smaller, while the receptive fields are much larger than those in the primary visual cortex (V1) [24][25][26]. This provides the opportunity to sample a larger region of visual space, albeit with reduced spatial resolution, using standard devices such as Utah arrays [27,28]. In particular, extrastriate area V4 offers an opportunity to recover the location of static visual stimuli, as it contains a retinotopic map of visual spa...

This paper presents a new unified computational-experimental approach to study the role of the synaptic activity on the activity of neurons in the small neuronal networks (NNs). In a neuronal tissue/organ, this question is investigated with higher complexities by recording action potentials from population of neurons in order to find the relationship between connectivity and the recorded activities. In this approach, we study the dynamics of very small cortical neuronal networks, which can be experimentally synthesized on chip with constrained connectivity. Multi-compartmental Hodgkin-Huxley model is used in NEURON software to reproduce cells by extracting the experimental data from the synthesized NNs. We thereafter demonstrate how the type of synaptic activity affects the network response to specific spike train using the simulation results.

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