scholarly journals Predicting proprioceptive cortical anatomy and neural coding with topographic autoencoders

2021 ◽  
Author(s):  
Kyle P Blum ◽  
Max D Grogan ◽  
Yufei Wu ◽  
Alex J Harston ◽  
Lee E Miller ◽  
...  
Keyword(s):  
Neural Coding ◽  
Receptive Fields ◽  
Humanoid Robots ◽  
Large Set ◽  
Electrode Arrays ◽  
Movement Data ◽  
Computer Interfaces ◽  
Variational Autoencoder ◽  
Natural Movement ◽  
Cortical Map

Proprioception is one of the least understood senses yet fundamental for the control of movement. Even basic questions of how limb pose is represented in the somatosensory cortex are unclear. We developed a variational autoencoder with topographic lateral connectivity (topo-VAE) to compute a putative cortical map from a large set of natural movement data. Although not fitted to neural data, our model reproduces two sets of observations from monkey centre-out reaching: 1. The shape and velocity dependence of proprioceptive receptive fields in hand-centered coordinates despite the model having no knowledge of arm kinematics or hand coordinate systems. 2. The distribution of neuronal preferred directions (PDs) recorded from multi-electrode arrays. The model makes several testable predictions: 1. Encoding across the cortex has a blob-and-pinwheel-type geometry PDs. 2. Few neurons will encode just a single joint. Topo-VAE provides a principled basis for understanding of sensorimotor representations, and the theoretical basis of neural manifolds, with application the restoration of sensory feedback in brain-computer interfaces and the control of humanoid robots.

Component analysis reveals sharp tuning of the local field potential in the guinea pig auditory cortex

2013 ◽  
Vol 109 (1) ◽  
pp. 261-272 ◽  
Author(s):  
Alain de Cheveigné ◽  
Jean-Marc Edeline ◽  
Quentin Gaucher ◽  
Boris Gourévitch
Keyword(s):  
Auditory Cortex ◽  
Local Field ◽  
Receptive Fields ◽  
Field Potential ◽  
Component Analysis ◽  
Electrode Arrays ◽  
Linear Combinations ◽  
Spectrotemporal Receptive Fields ◽  
Analysis Technique ◽  
Channel Electrode

Local field potentials (LFPs) recorded in the auditory cortex of mammals are known to reveal weakly selective and often multimodal spectrotemporal receptive fields in contrast to spiking activity. This may in part reflect the wider “listening sphere” of LFPs relative to spikes due to the greater current spread at low than high frequencies. We recorded LFPs and spikes from auditory cortex of guinea pigs using 16-channel electrode arrays. LFPs were processed by a component analysis technique that produces optimally tuned linear combinations of electrode signals. Linear combinations of LFPs were found to have sharply tuned responses, closer to spike-related tuning. The existence of a sharply tuned component implies that a cortical neuron (or group of neurons) capable of forming a linear combination of its inputs has access to that information. Linear combinations of signals from electrode arrays reveal information latent in the subspace spanned by multichannel LFP recordings and are justified by the fact that the observations themselves are linear combinations of neural sources.

scholarly journals Pattern recognition and bionic manipulator driving by surface electromyography signals using convolutional neural network

2018 ◽  
Vol 15 (5) ◽  
pp. 172988141880213 ◽  
Author(s):  
Yuanfang Wan ◽  
Zishan Han ◽  
Jun Zhong ◽  
Guohua Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Surface Electromyography ◽  
Hot Spot ◽  
Hand Movement ◽  
Ease Of Use ◽  
Electrode Arrays ◽  
Small Surface ◽  
Movement Data ◽  
Electromyography Signals

With the development of robotics, intelligent neuroprosthesis for amputees is more concerned. Research of robot controlling based on electrocardiogram, electromyography, and electroencephalogram is a hot spot. In medical research, electrode arrays are commonly used as sensors for surface electromyograms. Although these sensors collect more accurate data and sampling at higher frequencies, they have no advantage in terms of portability and ease of use. In recent years, there are also some small surface electromyography sensors for research. The portability of the sensor and the calculation speed of the calculation method directly affect the development of the bionic prosthesis. A consumer-grade surface electromyography device is selected as surface electromyography sensor in this study. We first proposed a data structure to convert raw surface electromyography signals from an array structure into a matrix structure (we called it surface electromyography graph). Then, a convolutional neural network was used to classify it. Discrete surface electromyography signals recorded from three persons 14 gestures (widely used in other research to evaluate the performance of classifier) have been applied to train the classifier and we get an accuracy of 97.27%. The impacts of different components used in convolutional neural network were tested with this data, and subsequently, the best results were selected to build the classifier used in this article. The NinaPro database 5 (one of the biggest surface electromyography data sets) was also used to evaluate our method, which comprises of hand movement data of 10 intact subjects with two myo armbands as sensors, and the classification accuracy increased by 13.76% on average when using double myo armbands and increased by 18.92% on average when using single myo armband. In order to driving the robot hand (bionic manipulator), a group of continuous surface electromyography signals was recorded to train the classifier, and an accuracy of 91.72% was acquired. We also used the same method to collect a set of surface electromyography data from a disabled with hand lost, then classified it using the abovementioned network and achieved an accuracy of 89.37%. Finally, the classifier was deployed to the microcontroller to drive the bionic manipulator, and the full video URL is given in the conclusion, with both the healthy man and the disabled tested with the bionic manipulator. The abovementioned results suggest that this method will help to facilitate the development and application of surface electromyography neuroprosthesis.

scholarly journals Sequence Transfer Learning for Neural Decoding

2017 ◽  
Author(s):  
Venkatesh Elango ◽  
Aashish N Patel ◽  
Kai J Miller ◽  
Vikash Gilja
Keyword(s):  
Transfer Learning ◽  
Affine Transformation ◽  
Linear Models ◽  
Short Term Memory ◽  
Training Data ◽  
Neural Decoding ◽  
Electrode Arrays ◽  
Neural Data ◽  
Computer Interfaces ◽  
Finger Flexion

AbstractA fundamental challenge in designing brain-computer interfaces (BCIs) is decoding behavior from time-varying neural oscillations. in typical applications, decoders are constructed for individual subjects and with limited data leading to restrictions on the types of models that can be utilized. currently, the best performing decoders are typically linear models capable of utilizing rigid timing constraints with limited training data. Here we demonstrate the use of Long Short-Term Memory (LSTM) networks to take advantage of the temporal information present in sequential neural data collected from subjects implanted with electrocorticographic (ECoG) electrode arrays performing a finger flexion task. our constructed models are capable of achieving accuracies that are comparable to existing techniques while also being robust to variation in sample data size. Moreover, we utilize the LSTM networks and an affine transformation layer to construct a novel architecture for transfer learning. We demonstrate that in scenarios where only the affine transform is learned for a new subject, it is possible to achieve results comparable to existing state-of-the-art techniques. The notable advantage is the increased stability of the model during training on novel subjects. Relaxing the constraint of only training the affine transformation, we establish our model as capable of exceeding performance of current models across all training data sizes. Overall, this work demonstrates that LSTMS are a versatile model that can accurately capture temporal patterns in neural data and can provide a foundation for transfer learning in neural decoding.

scholarly journals Longevity and reliability of chronic unit recordings using the Utah, intracortical multi-electrode arrays

2021 ◽  
Author(s):  
Caleb Scheffer Sponheim ◽  
Vasileios Papadourakis ◽  
Jennifer Collinger ◽  
John Downey ◽  
Jeffrey M Weiss ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Rhesus Macaques ◽  
Human Subjects ◽  
Electrode Arrays ◽  
Computer Interfaces ◽  
Somatosensory Cortices ◽  
Large Numbers ◽  
Electrode Length ◽  
Human Participants

Abstract Objective. Microelectrode arrays are standard tools for conducting chronic electrophysiological experiments, allowing researchers to simultaneously record from large numbers of neurons. Specifically, Utah electrode arrays (UEAs) have been utilized by scientists in many species, including rodents, rhesus macaques, marmosets, and human participants. The field of clinical human brain-computer interfaces currently relies on the UEA as a number of research groups have FDA clearance for this device through the investigational device exemption pathway. Despite its widespread usage in systems neuroscience, few studies have comprehensively evaluated the reliability and signal quality of the Utah array over long periods of time in a large dataset. Approach. We collected and analyzed over 6000 recorded datasets from various cortical areas spanning almost 9 years of experiments, totaling 17 rhesus macaques (Macaca Mulatta) and 2 human subjects, and 55 separate microelectrode Utah arrays. The scale of this dataset allowed us to evaluate the average life of these arrays, based primarily on the signal-to-noise ratio of each electrode over time. Main Results. Using implants in primary motor, premotor, prefrontal, and somatosensory cortices, we found that the average lifespan of available recordings from UEAs was 622 days, although we provide several examples of these UEAs lasting over 1000 days and one up to 9 years; human implants were also shown to last longer than non-human primate implants. We also found that electrode length did not affect longevity and quality, but iridium oxide metallization on the electrode tip exhibited superior yield as compared to platinum metallization.

Properties of Correlated Neural Activity Clusters in Cat Auditory Cortex Resemble Those of Neural Assemblies

2006 ◽  
Vol 96 (2) ◽  
pp. 746-764 ◽  
Author(s):  
Jos J. Eggermont
Keyword(s):  
Auditory Cortex ◽  
Spike Activity ◽  
Correlation Matrix ◽  
Cross Correlation ◽  
Receptive Fields ◽  
Primary Auditory Cortex ◽  
Simultaneous Recording ◽  
Electrode Arrays ◽  
Tone Stimulus ◽  
Neural Assemblies

Spiking activity was recorded from cat auditory cortex using multi-electrode arrays. Cross-correlograms were calculated for spikes recorded on separate microelectrodes. The pair-wise cross-correlation matrix was constructed for the peak values of the correlograms. Hierarchical clustering was performed on the cross-correlation matrix for six stimulus conditions. These were silence, three multi-tone stimulus ensembles with different spectral densities, low-pass amplitude-modulated noise, and Poisson-distributed click trains that each lasted 15 min. The resulting neuron clusters reflect patches in cortex of up to several mm2 in size that expand and contract in response to different stimuli. Cluster positions and size were very similar for spontaneous activity and multi-tone stimulus-evoked activity but differed between those conditions and the noise and click stimuli. Cluster size was significantly larger in posterior auditory field (PAF) compared with primary auditory cortex (AI), whereas the fraction of common spikes (within a 10-ms window) across all electrode activity participating in a cluster was significantly higher in AI compared with PAF. Clusters crossed area boundaries in <5% of the cases were simultaneous recording were made in AI and PAF. Clusters are therefore similar to but not synonymous with the traditional view of neural assemblies. Common-spike spectrotemporal receptive fields (STRFs) were obtained for common-spike activity and all-spike activity within a cluster. Common-spike STRFs had higher signal-to-noise ratio than all-spike STRFs and showed generally spectral and temporal sharpening. The coincident and noncoincident output of the clusters could potentially act in parallel and may serve different modes of stimulus coding.

Wavelet-Like Receptive Fields Emerges by Non-Linear Minimization of Neuron Error

2003 ◽  
Vol 13 (02) ◽  
pp. 87-91
Author(s):  
Allan Kardec Barros ◽  
Andrzej Cichocki ◽  
Noboru Ohnishi
Keyword(s):  
Neural Coding ◽  
Single Neuron ◽  
Receptive Fields ◽  
Natural Images ◽  
Squared Error ◽  
Redundancy Reduction ◽  
One Step ◽  
The One ◽  
Mutually Independent ◽  
Large Research

Redundancy reduction as a form of neural coding has been since the early sixties a topic of large research interest. A number of strategies has been proposed, but the one which is attracting most attention recently assumes that this coding is carried out so that the output signals are mutually independent. In this work we go one step further and suggest an strategy to deal also with non-orthogonal signals (i.e., ''dependent'' signals). Moreover, instead of working with the usual squared error, we design a neuron where the non-linearity is operating on the error. It is computationally more economic and, importantly, the permutation/scaling problem10 is avoided. The framework is given with a biological background, as we avocate throughout the manuscript that the algorithm fits well the single neuron and redundancy reduction doctrine.5 Moreover, we show that wavelet-like receptive fields emerges from natural images processed by this algorithm.

scholarly journals Scene statistics and noise determine the relative arrangement of receptive field mosaics

2021 ◽  
Vol 118 (39) ◽  
pp. e2105115118
Author(s):  
Na Young Jun ◽  
Greg D. Field ◽  
John Pearson
Keyword(s):  
Neural Coding ◽  
Coding Theory ◽  
Receptive Fields ◽  
Optimal Arrangement ◽  
Efficient Coding ◽  
Natural Stimuli ◽  
Signal Stimulus ◽  
Theoretical Predictions ◽  
On And Off Pathways ◽  
Contrast Response

Many sensory systems utilize parallel ON and OFF pathways that signal stimulus increments and decrements, respectively. These pathways consist of ensembles or grids of ON and OFF detectors spanning sensory space. Yet, encoding by opponent pathways raises a question: How should grids of ON and OFF detectors be arranged to optimally encode natural stimuli? We investigated this question using a model of the retina guided by efficient coding theory. Specifically, we optimized spatial receptive fields and contrast response functions to encode natural images given noise and constrained firing rates. We find that the optimal arrangement of ON and OFF receptive fields exhibits a transition between aligned and antialigned grids. The preferred phase depends on detector noise and the statistical structure of the natural stimuli. These results reveal that noise and stimulus statistics produce qualitative shifts in neural coding strategies and provide theoretical predictions for the configuration of opponent pathways in the nervous system.

scholarly journals An intrinsic neural eye tracker in primary visual cortex

2018 ◽  
Author(s):  
Adam P. Morris ◽  
Bart Krekelberg
Keyword(s):  
Visual Cortex ◽  
Eye Movements ◽  
Primary Visual Cortex ◽  
Visual Information ◽  
Moving Objects ◽  
Receptive Fields ◽  
Gaze Direction ◽  
Eye Tracker ◽  
Electrode Arrays ◽  
Full Field

SummaryHumans and other primates rely on eye movements to explore visual scenes and to track moving objects. As a result, the image that is projected onto the retina – and propagated throughout the visual cortical hierarchy – is almost constantly changing and makes little sense without taking into account the momentary direction of gaze. How is this achieved in the visual system? Here we show that in primary visual cortex (V1), the earliest stage of cortical vision, neural representations carry an embedded “eye tracker” that signals the direction of gaze associated with each image. Using chronically implanted multi-electrode arrays, we recorded the activity of neurons in V1 during tasks requiring fast (exploratory) and slow (pursuit) eye movements. Neurons were stimulated with flickering, full-field luminance noise at all times. As in previous studies 1-4, we observed neurons that were sensitive to gaze direction during fixation, despite comparable stimulation of their receptive fields. We trained a decoder to translate neural activity into metric estimates of (stationary) gaze direction. This decoded signal not only tracked the eye accurately during fixation, but also during fast and slow eye movements, even though the decoder had not been exposed to data from these behavioural states. Moreover, this signal lagged the real eye by approximately the time it took for new visual information to travel from the retina to cortex. Using simulations, we show that this V1 eye position signal could be used to take into account the sensory consequences of eye movements and map the fleeting positions of objects on the retina onto their stable position in the world.

scholarly journals Real-time classification of experience-related ensemble spiking patterns for closed-loop applications

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Davide Ciliberti ◽  
Frédéric Michon ◽  
Fabian Kloosterman
Keyword(s):  
Closed Loop ◽  
Neural Circuits ◽  
Electrode Arrays ◽  
High Count ◽  
Population Activity ◽  
Online Identification ◽  
Computer Interfaces ◽  
Neural Assembly ◽  
Neuronal Patterns

Communication in neural circuits across the cortex is thought to be mediated by spontaneous temporally organized patterns of population activity lasting ~50 –200 ms. Closed-loop manipulations have the unique power to reveal direct and causal links between such patterns and their contribution to cognition. Current brain–computer interfaces, however, are not designed to interpret multi-neuronal spiking patterns at the millisecond timescale. To bridge this gap, we developed a system for classifying ensemble patterns in a closed-loop setting and demonstrated its application in the online identification of hippocampal neuronal replay sequences in the rat. Our system decodes multi-neuronal patterns at 10 ms resolution, identifies within 50 ms experience-related patterns with over 70% sensitivity and specificity, and classifies their content with 95% accuracy. This technology scales to high-count electrode arrays and will help to shed new light on the contribution of internally generated neural activity to coordinated neural assembly interactions and cognition.

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