Decoding methods for neural prostheses: where have we reached?

Li, Zheng

doi:10.3389/fnsys.2014.00129

Cited by 19 publications

(12 citation statements)

References 52 publications

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“…Many classification algorithms exist for this type of analyses (Brown et al, 2004; Haxby et al, 2014; Li, 2014), however, Bayesian decoding has emerged as a favorite since it theoretically provides the best possible classification given certain assumptions (Zhang et al, 1998). Without knowing how the brain decodes itself, the Bayesian approach is therefore a reasonable starting place to determine what could be represented.…”

Section: What the Ensemble Code Reveals About The Nature Of Neural Rementioning

confidence: 99%

Representation of memories in the cortical–hippocampal system: Results from the application of population similarity analyses

McKenzie

Keene

Farovik

et al. 2016

Neurobiology of Learning and Memory

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Here we consider the value of neural population analysis as an approach to understanding how information is represented in the hippocampus and cortical areas and how these areas might interact as a brain system to support memory. We argue that models based on sparse coding of different individual features by single neurons in these areas (e.g., place cells, grid cells) are inadequate to capture the complexity of experience represented within this system. By contrast, population analyses of neurons with denser coding and mixed selectivity reveal new and important insights into the organization of memories. Furthermore, comparisons of the organization of information in interconnected areas suggest a model of hippocampal-cortical interactions that mediates the fundamental features of memory.

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Section: What the Ensemble Code Reveals About The Nature Of Neural Rementioning

confidence: 99%

Representation of memories in the cortical–hippocampal system: Results from the application of population similarity analyses

McKenzie

Keene

Farovik

et al. 2016

Neurobiology of Learning and Memory

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“…BMIs record the neural activity from motor cortical areas, use a mathematical transform termed the “decoder” to convert this activity into control commands for an external device, and provide visual feedback of the generated movement to the subject ( Fig 1A ). Various decoders such as linear regression, population vector, and Kalman filters (KF) have been used in real-time BMIs [ 29 ]. Once a decoding model is selected, its parameters need to be estimated for each subject.…”

Section: Introductionmentioning

confidence: 99%

Robust Brain-Machine Interface Design Using Optimal Feedback Control Modeling and Adaptive Point Process Filtering

2016

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Much progress has been made in brain-machine interfaces (BMI) using decoders such as Kalman filters and finding their parameters with closed-loop decoder adaptation (CLDA). However, current decoders do not model the spikes directly, and hence may limit the processing time-scale of BMI control and adaptation. Moreover, while specialized CLDA techniques for intention estimation and assisted training exist, a unified and systematic CLDA framework that generalizes across different setups is lacking. Here we develop a novel closed-loop BMI training architecture that allows for processing, control, and adaptation using spike events, enables robust control and extends to various tasks. Moreover, we develop a unified control-theoretic CLDA framework within which intention estimation, assisted training, and adaptation are performed. The architecture incorporates an infinite-horizon optimal feedback-control (OFC) model of the brain’s behavior in closed-loop BMI control, and a point process model of spikes. The OFC model infers the user’s motor intention during CLDA—a process termed intention estimation. OFC is also used to design an autonomous and dynamic assisted training technique. The point process model allows for neural processing, control and decoder adaptation with every spike event and at a faster time-scale than current decoders; it also enables dynamic spike-event-based parameter adaptation unlike current CLDA methods that use batch-based adaptation on much slower adaptation time-scales. We conducted closed-loop experiments in a non-human primate over tens of days to dissociate the effects of these novel CLDA components. The OFC intention estimation improved BMI performance compared with current intention estimation techniques. OFC assisted training allowed the subject to consistently achieve proficient control. Spike-event-based adaptation resulted in faster and more consistent performance convergence compared with batch-based methods, and was robust to parameter initialization. Finally, the architecture extended control to tasks beyond those used for CLDA training. These results have significant implications towards the development of clinically-viable neuroprosthetics.

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“…To compare the similarity between spiking patterns, the traditional way focuses on how to compute the distance between two spike trains in general [177,178], and in the context of the retinal prosthesis [179]. Another way of doing this is to using decoding models for the purpose of better performance of neuroprosthesis [180,101,10]. Ideally, similar to the other neuroprostheses, where a closed-loop device can be employed to decode neuronal signal to control stimulus, the signal delivered by a retinal prosthesis should be able to reconstruct the original stimuli, i.e., dynamic visual scenes projected into the retina.…”

Section: Discussionmentioning

confidence: 99%

Toward the Next Generation of Retinal Neuroprosthesis: Visual Computation with Spikes

Liu

Jia

et al. 2020

Engineering

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Neuroprosthesis, as one type of precision medicine device, is aiming for manipulating neuronal signals of the brain in a closed-loop fashion, together with receiving stimulus from the environment and controlling some part of our brain/body. In terms of vision, incoming information can be processed by the brain in millisecond interval. The retina computes visual scenes and then sends its output as neuronal spikes to the cortex for further computation. Therefore, the neuronal signal of interest for retinal neuroprosthesis is spike. Closed-loop computation in neuroprosthesis includes two stages: encoding stimulus to neuronal signal, and decoding it into stimulus. Here we review some of the recent progress about visual computation models that use spikes for analyzing natural scenes, including static images and dynamic movies. We hypothesize that for a better understanding of computational principles in the retina, one needs a hypercircuit view of the retina, in which different functional network motifs revealed in the cortex neuronal network should be taken into consideration for the retina. Different building blocks of the retina, including a diversity of cell types and synaptic connections, either chemical synapses or electrical synapses (gap junctions), make the retina an ideal neuronal network to adapt the computational techniques developed in artificial intelligence for modeling of encoding/decoding visual scenes. Altogether, one needs a systems approach of visual computation with spikes to advance the next generation of retinal neuroprosthesis as an artificial visual system.

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Decoding methods for neural prostheses: where have we reached?

Cited by 19 publications

References 52 publications

Representation of memories in the cortical–hippocampal system: Results from the application of population similarity analyses

Representation of memories in the cortical–hippocampal system: Results from the application of population similarity analyses

Robust Brain-Machine Interface Design Using Optimal Feedback Control Modeling and Adaptive Point Process Filtering

Toward the Next Generation of Retinal Neuroprosthesis: Visual Computation with Spikes

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