Toward Building Hybrid Biological/in silico Neural Networks for Motor Neuroprosthetic Control

Kocatürk, Mehmet; Gülçür, Halil Özcan; Canbeyli, Reşit

doi:10.3389/fnbot.2015.00008

Cited by 26 publications

(21 citation statements)

References 68 publications

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“…Even though the obtained results are preliminary, they will certainly contribute to the development of future brain-computer interface applications using non-invasive technique [61–69]. For example, the movements of the different fingers could be used to formulate commands for control of external devices such as robot arms or wheelchairs [70,71]. Moreover, the method could be extended to 3D fNIRS imaging based on each local brain region.…”

Section: Discussionmentioning

confidence: 99%

Bundled-Optode Method in Functional Near-Infrared Spectroscopy

2016

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In this paper, a theory for detection of the absolute concentrations of oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR) from hemodynamic responses using a bundled-optode configuration in functional near-infrared spectroscopy (fNIRS) is proposed. The proposed method is then applied to the identification of two fingers (i.e., little and thumb) during their flexion and extension. This experiment involves a continuous-wave-type dual-wavelength (760 and 830 nm) fNIRS and five healthy male subjects. The active brain locations of two finger movements are identified based on the analysis of the t- and p-values of the averaged HbOs, which are quite distinctive. Our experimental results, furthermore, revealed that the hemodynamic responses of two-finger movements are different: The mean, peak, and time-to-peak of little finger movements are higher than those of thumb movements. It is noteworthy that the developed method can be extended to 3-dimensional fNIRS imaging.

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Section: Discussionmentioning

confidence: 99%

Bundled-Optode Method in Functional Near-Infrared Spectroscopy

2016

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“…Embedding biomimetic brain models in neuroprosthetic systems has the potential to significantly improve their performance [7–9]. In our paradigm, biological brain circuits interact directly with biomimetic brain simulations, thereby employing biological mechanisms of co-adaptation and learning to achieve a functional task in a biological manner.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis

Durá-Bernal¹,

Neymotin²,

Kerr³

et al. 2017

IBM J. Res. & Dev.

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Biomimetic simulation permits neuroscientists to better understand the complex neuronal dynamics of the brain. Embedding a biomimetic simulation in a closed-loop neuroprosthesis, which can read and write signals from the brain, will permit applications for amelioration of motor, psychiatric, and memory-related brain disorders. Biomimetic neuroprostheses require real-time adaptation to changes in the external environment, thus constituting an example of a dynamic data-driven application system. As model fidelity increases, so does the number of parameters and the complexity of finding appropriate parameter configurations. Instead of adapting synaptic weights via machine learning, we employed major biological learning methods: spike-timing dependent plasticity and reinforcement learning. We optimized the learning metaparameters using evolutionary algorithms, which were implemented in parallel and which used an island model approach to obtain sufficient speed. We employed these methods to train a cortical spiking model to utilize macaque brain activity, indicating a selected target, to drive a virtual musculoskeletal arm with realistic anatomical and biomechanical properties to reach to that target. The optimized system was able to reproduce macaque data from a comparable experimental motor task. These techniques can be used to efficiently tune the parameters of multiscale systems, linking realistic neuronal dynamics to behavior, and thus providing a useful tool for neuroscience and neuroprosthetics.

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“…The recording was made using a neuroprosthesis design environment [17] from the primary motor cortex (area M1) of a rat during lever-pressing in response to visual stimuli. The data segment that is used here is about 12.6 s long and was recorded with a sampling rate of 40 kHz.…”

Section: Application On Real Datamentioning

confidence: 99%

Truncation thresholds: a pair of spike detection thresholds computed using truncated probability distributions

Okatan¹,

Kocatürk²

2017

Turk J Elec Eng & Comp Sci

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Abstract:We describe a method for computing a pair of spike detection thresholds, called 'truncation thresholds', using truncated probability distributions, for extracellular recordings. In existing methods the threshold is usually set to a multiple of an estimate of the standard deviation of the noise in the recording, with the multiplication factor being chosen between 3 and 5 according to the researcher's preferences. Our method has the following advantages over these methods. First, because the standard deviation is usually estimated from the entire recording, which includes the spikes, it increases with firing rate. By contrast, truncation thresholds decrease in absolute value with increasing firing rate, thereby capturing more of the signal. Second, the parameters of the selected noise distribution are estimated more accurately by maximum likelihood fitting of the truncated distribution to the data delimited by the truncation thresholds. Third, the computation of the truncation thresholds is completely data-driven. It does not involve a userdefined multiplication factor. Fourth, methods that use a threshold that is proportional to the estimated standard deviation of the noise assume that the noise distribution is symmetrical around the mean. By contrast, truncation thresholds are not linked to each other by an assumption of symmetry about some axis. Fifth, existing methods do not verify that subthreshold data obey a noise distribution. Truncation thresholds, however, are defined by the fact that the distribution of the data they delimit is statistically indistinguishable, according to the Kolmogorov-Smirnov test, from a selected distribution, truncated at those thresholds. Application of the method is illustrated using recordings from cortical area M1 in awake behaving rats, as well as in simulated recordings. Source code and executables of a software suite that computes the truncation thresholds are provided for the case when the noise distribution is modeled as truncated normal.

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Toward Building Hybrid Biological/in silico Neural Networks for Motor Neuroprosthetic Control

Cited by 26 publications

References 68 publications

Bundled-Optode Method in Functional Near-Infrared Spectroscopy

Bundled-Optode Method in Functional Near-Infrared Spectroscopy

Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis

Truncation thresholds: a pair of spike detection thresholds computed using truncated probability distributions

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