scholarly journals Deep Multi-State Dynamic Recurrent Neural Networks Operating on Wavelet Based Neural Features for Robust Brain Machine Interfaces

2019 ◽  
Author(s):  
Benyamin Haghi ◽  
Spencer Kellis ◽  
Sahil Shah ◽  
Maitreyi Ashok ◽  
Luke Bashford ◽  
...  
Keyword(s):  
Neural Network ◽  
Posterior Parietal Cortex ◽  
Ground Truth ◽  
Open Loop ◽  
Neural Data ◽  
State Dynamic ◽  
History Of ◽  
Posterior Parietal ◽  
Machine Interface ◽  
Memory Accesses

AbstractWe present a new deep multi-state Dynamic Recurrent Neural Network (DRNN) architecture for Brain Machine Interface (BMI) applications. Our DRNN is used to predict Cartesian representation of a computer cursor movement kinematics from open-loop neural data recorded from the posterior parietal cortex (PPC) of a human subject in a BMI system. We design the algorithm to achieve a reasonable trade-off between performance and robustness, and we constrain memory usage in favor of future hardware implementation. We feed the predictions of the network back to the input to improve prediction performance and robustness. We apply a scheduled sampling approach to the model in order to solve a statistical distribution mismatch between the ground truth and predictions. Additionally, we configure a small DRNN to operate with a short history of input, reducing the required buffering of input data and number of memory accesses. This configuration lowers the expected power consumption in a neural network accelerator. Operating on wavelet-based neural features, we show that the average performance of DRNN surpasses other state-of-the-art methods in the literature on both single- and multi-day data recorded over 43 days. Results show that multi-state DRNN has the potential to model the nonlinear relationships between the neural data and kinematics for robust BMIs.

Transcranial Magnetic Stimulation Disrupts Eye-Hand Interactions in the Posterior Parietal Cortex

2000 ◽  
Vol 84 (3) ◽  
pp. 1677-1680 ◽  
Author(s):  
Paul Van Donkelaar ◽  
Ji-Hang Lee ◽  
Anthony S. Drew
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Parietal Cortex ◽  
Magnetic Stimulation ◽  
Posterior Parietal Cortex ◽  
Hand Movement ◽  
Open Loop ◽  
Neurophysiological Studies ◽  
Posterior Parietal ◽  
Hand Coordination

Recent neurophysiological studies have started to shed some light on the cortical areas that contribute to eye-hand coordination. In the present study we investigated the role of the posterior parietal cortex (PPC) in this process in normal, healthy subjects. This was accomplished by delivering single pulses of transcranial magnetic stimulation (TMS) over the PPC to transiently disrupt the putative contribution of this area to the processing of information related to eye-hand coordination. Subjects made open-loop pointing movements accompanied by saccades of the same required amplitude or by saccades that were substantially larger. Without TMS the hand movement amplitude was influenced by the amplitude of the corresponding saccade; hand movements accompanied by larger saccades were larger than those accompanied by smaller saccades. When TMS was applied over the left PPC just prior to the onset of the saccade, a marked reduction in the saccadic influence on manual motor output was observed. TMS delivered at earlier or later periods during the response had no effect. Taken together, these data suggest that the PPC integrates signals related to saccade amplitude with limb movement information just prior to the onset of the saccade.

A Neural Network Model of Cortical Activity during Reaching

1993 ◽  
Vol 5 (1) ◽  
pp. 14-33 ◽  
Author(s):  
Ronald Kettner ◽  
Joanne Marcario ◽  
Nicholas Port
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Cortical Neurons ◽  
Posterior Parietal Cortex ◽  
Large Population ◽  
Association Cortex ◽  
Preferred Direction ◽  
The Neural Network ◽  
Posterior Parietal

A neural network model that produces many of the directional and spatial response properties that have been observed for cortical neurons in monkeys moving toward targets in space is described. These include motor cortex units with broad tuning in a single preferred direction, approximately linear variation in activity for different hold positions, and approximate invariance in preferred direction for different starting points in space. Association cortex units in the model are sometimes irregular and reminiscent of neurons observed in visually responsive brain areas such as the posterior parietal cortex. The model is also compatible with population analyses performed on motor cortical neurons. Across network units, the distribution of preferred directions is uniformly distributed in directional space, and the degree of tuning and response magnitude vary from unit to unit. A population code used to predict accurately the direction of arm movements from a large population of coarsely tuned individual neurons allows predictions using a simulated population of unit responses obtained from the neural network model. This code works for different starting locations in space using the same parameters.

scholarly journals From thought to action: The brain–machine interface in posterior parietal cortex

2019 ◽  
Vol 116 (52) ◽  
pp. 26274-26279 ◽  
Author(s):  
Richard A. Andersen ◽  
Tyson Aflalo ◽  
Spencer Kellis
Keyword(s):  
Parietal Cortex ◽  
Posterior Parietal Cortex ◽  
The Body ◽  
Assistive Device ◽  
Brain Machine Interface ◽  
Wide Range ◽  
Cord Injury ◽  
Posterior Parietal ◽  
Machine Interface ◽  
The Brain

A dramatic example of translational monkey research is the development of neural prosthetics for assisting paralyzed patients. A neuroprosthesis consists of implanted electrodes that can record the intended movement of a paralyzed part of the body, a computer algorithm that decodes the intended movement, and an assistive device such as a robot limb or computer that is controlled by these intended movement signals. This type of neuroprosthetic system is also referred to as a brain–machine interface (BMI) since it interfaces the brain with an external machine. In this review, we will concentrate on BMIs in which microelectrode recording arrays are implanted in the posterior parietal cortex (PPC), a high-level cortical area in both humans and monkeys that represents intentions to move. This review will first discuss the basic science research performed in healthy monkeys that established PPC as a good source of intention signals. Next, it will describe the first PPC implants in human patients with tetraplegia from spinal cord injury. From these patients the goals of movements could be quickly decoded, and the rich number of action variables found in PPC indicates that it is an appropriate BMI site for a very wide range of neuroprosthetic applications. We will discuss research on learning to use BMIs in monkeys and humans and the advances that are still needed, requiring both monkey and human research to enable BMIs to be readily available in the clinic.

Brain Imaging in Catatonia: Current Findings and a Pathophysiologic Model

CNS Spectrums ◽  
2000 ◽  
Vol 5 (7) ◽  
pp. 34-46 ◽  
Author(s):  
Georg Northoff
Keyword(s):  
Neural Network ◽  
Basal Ganglia ◽  
Brain Imaging ◽  
Posterior Parietal Cortex ◽  
Subjective Experiences ◽  
Behavioral Alterations ◽  
Subcortical Structures ◽  
Posterior Parietal ◽  
Clinical Phenomenology ◽  
In The Beginning

AbstractKarl Ludwig Kahlbaum originally described catatonia as a psychomotor disease that encompassed motor, affective, and behavioral symptoms. In the beginning of the 20th century, catatonia was considered to be the motoric manifestation of schizophrenia; therefore, neuropathologic research mostly focused on neuroanatomic substrates (ie, the basal ganglia underlying the generation of movements). Even though some alterations were found in basal ganglia, the findings in these subcortical structures are not consistent. Recently, there has been a reemergence of interest into researching catatonia. Brain imaging studies have shown major and specific alterations in a right hemispheric neural network that includes the medial and lateral orbitofrontal and posterior parietal cortex. This neural network may be abnormally modulated by altered functional interactions between γ-aminobutyric acid (GABA)-ergic and glutamatergic transmission. This may account for the interrelationship among motor, emotional, and behavioral alterations observed in both clinical phenomenology and the subjective experiences of patients with catatonia. Such functional interrelationships should be explored in further detail in catatonia, which may also serve as a paradigmatic model for the investigation of psychomotor and brain function in general.

Posterior parietal cortex mediates encoding processes in change blindness

2009 ◽  
Author(s):  
Philip Tseng ◽  
Cassidy Sterling ◽  
Adam Cooper ◽  
Bruce Bridgeman ◽  
Neil G. Muggleton ◽  
...  
Keyword(s):  
Parietal Cortex ◽  
Posterior Parietal Cortex ◽  
Change Blindness ◽  
Posterior Parietal

The Grasp Cell Hypothesis: Near-miss effect points to common neurological machinery in posterior parietal cortex for controllable objects and concepts

2018 ◽  
Author(s):  
Imogen M Kruse
Keyword(s):  
Parietal Cortex ◽  
Posterior Parietal Cortex ◽  
Near Miss ◽  
Illusion Of Control ◽  
Gambling Behaviour ◽  
Drug Seeking ◽  
Spatial Influence ◽  
Reward Probability ◽  
Posterior Parietal ◽  
Action Value

The near-miss effect in gambling behaviour occurs when an outcome which is close to a win outcome invigorates gambling behaviour notwithstanding lack of associated reward. In this paper I postulate that the processing of concepts which are deemed controllable is rooted in neurological machinery located in the posterior parietal cortex specialised for the processing of objects which are immediately actionable or controllable because they are within reach. I theorise that the use of a common machinery facilitates spatial influence on the perception of concepts such that the win outcome which is 'almost complete' is perceived as being 'almost within reach'. The perceived realisability of the win increases subjective reward probability and the associated expected action value which impacts decision-making and behaviour. This novel hypothesis is the first to offer a neurological model which can comprehensively explain many empirical findings associated with the near-miss effect as well as other gambling phenomena such as the ‘illusion of control’. Furthermore, when extended to other compulsive behaviours such as drug addiction, the model can offer an explanation for continued drug-seeking following devaluation and for the increase in cravings in response to perceived opportunity to self-administer, neither of which can be explained by simple reinforcement models alone. This paper therefore provides an innovative and unifying perspective for the study and treatment of behavioural and substance addictions.

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