A machine learning framework to optimize optic nerve electrical stimulation for vision restoration

Romeni, Simone; Zoccolan, Davide; Micera, Silvestro

doi:10.1016/j.patter.2021.100286

Cited by 7 publications

(9 citation statements)

References 41 publications

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“…Its electrical stimulation may thus allow subjects to experience visual perception throughout their visual field using a limited number of electrodes [6].…”

Section: Introductionmentioning

confidence: 99%

“…The stimulation protocol of neural prostheses needs to be optimized to be effective, and current research is focused on finding the best strategy to do it. The stimulation protocol is the combination of all the parameters involved in the delivery of the electric stimuli, namely the active sites of the electrode, current intensity, frequency, and pulse width [6]. Optimizing such a stimulation protocol means choosing the optimal combination of current parameters and active electrode sites that elicits the desired effect.…”

Section: Introductionmentioning

confidence: 99%

“…In the case of an optic nerve visual prosthesis, an optimization strategy could be to perform closed-loop stimulation relying on the feedback provided by the visual cortex. This means that the stimulation parameters could be continually adapted, for example, by exploiting genetic algorithms that maximize the similarity between the response elicited in the visual cortex by the electrical stimulation of the optic nerve and the target cortical activation [6]. Recent studies performed on rabbits proved that intraneural optic nerve stimulation can selectively activate the visual cortex [4,8], but further animal experiments are now necessary to 1) assess the possibility of univocally associating visual inputs and cortical activation patterns and 2) understand which are the activation patterns that have to be resembled to make sure the animal is experiencing a certain visual perception.…”

Section: Introductionmentioning

confidence: 99%

“…Successful classification of evoked cortical responses might pave the way for the long-term goal of the current study. This aims, in the future, to drive the cortical activation towards patterns that are as similar as possible to the ones evoked by the visual stimulus that must be replicated [6], as schematized in figure 1.…”

Section: Introductionmentioning

confidence: 99%

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Convolutional neural network classifies visual stimuli from cortical response recorded with wide-field imaging in mice

Luca

Moccia²,

Lupori³

et al. 2023

J. Neural Eng.

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Objective The optic nerve is a good location for a visual neuroprosthesis. It can be targeted when a subject cannot receive a retinal prosthesis and it is less invasive than a cortical implant. The effectiveness of an electrical neuroprosthesis depends on the combination of the stimulation parameters which must be optimized, and an optimization strategy might be performing closed-loop stimulation using the evoked cortical response as feedback. However, it is necessary to identify target cortical activation patterns and to associate the cortical activity with the visual stimuli present in the visual field of the subjects. Visual stimuli decoding should be performed on large areas of the visual cortex, and with a method as translational as possible to shift the study to human subjects in the future. The aim of this work is to develop an algorithm that meets these requirements and can be leveraged to automatically associate a cortical activation pattern with the visual stimulus that generated it. Approach Three mice were presented with 10 different visual stimuli, and their primary visual cortex response was recorded using wide-field calcium imaging. Our decoding algorithm relies on a convolutional neural network (CNN), trained to classify the visual stimuli from the correspondent wide-field images. Several experiments were performed to identify the best training strategy and investigate the possibility of generalization. Main results The best classification accuracy was 75.38%±4.77%, obtained pre-training the CNN on the MNIST digits dataset and fine-tuning it on our dataset. Generalization was possible pre-training the CNN to classify Mouse 1 dataset and fine-tuning it on Mouse 2 and Mouse 3, with accuracies of 64.14%±10.81% and 51.53%±6.48% respectively. Significance The combination of wide-field calcium imaging and CNNs can be used to classify the cortical responses to simple visual stimuli and might be a viable alternative to existing decoding methodologies. It also allows us to consider the cortical activation as reliable feedback in future optic nerve stimulation experiments.

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“…Its electrical stimulation may thus allow subjects to experience visual perception throughout their visual field using a limited number of electrodes [6].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Convolutional neural network classifies visual stimuli from cortical response recorded with wide-field imaging in mice

Luca

Moccia²,

Lupori³

et al. 2023

J. Neural Eng.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this issue of Patterns, Romeni et al propose a method to optimize optic nerve stimulation parameters for vision restoration using an artificial brain network. 1 By performing in silico experiments, they found that their stimulation framework achieves results comparable to natural vision. Such work highlights the potential of neurotechnology informed by artificial models of the brain and suggests that artificial neural networks may substantially aid the development of bidirectional brain-computer interfaces (BCIs) restoring both perception and action.…”

mentioning

confidence: 99%