Biomedical subjects

Johan Wessberg

Publications and source records attributed to Johan Wessberg.

4 recordsLinked to original sources

PubMed2005

Interpreting spatial and temporal neural activity through a recurrent neural network brain-machine interface.

We propose the use of optimized brain-machine interface (BMI) models for interpreting the spatial and temporal neural activity generated in motor tasks. In this study, a nonlinear dynamical neural network is trained to predict the hand position of primates from neural recordings in a reaching task paradigm. We first develop a method to reveal the role attributed by the model to the sampled motor, premotor, and parietal cortices in generating hand movements. Next, using the trained model weights, we derive a temporal sensitivity measure to asses how the model utilized the sampled cortices and neurons in real-time during BMI testing.

Algorithms↗

PubMed2003

Receptive field properties of unmyelinated tactile afferents in the human skin.

We recorded, with the microneurography technique, single-unit impulses from nine cutaneous mechanoreceptive afferents with conduction velocities in the C range and receptive fields in the hairy skin of the forearm. The units responded with high impulse rates to light touch and had low monofilament thresholds. The geography of receptive fields was explored with a scanning method: a lightweight probe with a small and rounded tip was made to scan the field area in a series of closely adjacent tracks while single-unit activity was recorded. The fields of the nine units varied considerably in size as well as complexity. The individual field consisted of one to nine small responsive spots distributed over an area of 1-35 mm(2) when explored with a moving indentation of 5 mN. The fields were roughly round or oval in shape with no preferred orientation. The size of the response differed between individual sensitive spots in a field, suggesting a highly nonuniform terminal organization. The properties of the fields seem consistent with a role of tactile C afferents to provide information about pleasant touch and skin-to-skin contacts to central structures controlling emotions and affiliative behavior.

Adult↗

PubMedDate not supplied

Divide-and-conquer approach for brain machine interfaces: nonlinear mixture of competitive linear models.

This paper proposes a divide-and-conquer strategy for designing brain machine interfaces. A nonlinear combination of competitively trained local linear models (experts) is used to identify the mapping from neuronal activity in cortical areas associated with arm movement to the hand position of a primate. The proposed architecture and the training algorithm are described in detail and numerical performance comparisons with alternative linear and nonlinear modeling approaches, including time-delay neural networks and recursive multilayer perceptrons, are presented. This new strategy allows training the local linear models using normalized LMS and using a relatively smaller nonlinear network to efficiently combine the predictions of the linear experts. This leads to savings in computational requirements, while the performance is still similar to a large fully nonlinear network.

Artificial Intelligence↗

PubMedDate not supplied

Optimizing a linear algorithm for real-time robotic control using chronic cortical ensemble recordings in monkeys.

Previous work in our laboratory has demonstrated that a simple linear model can be used to translate cortical neuronal activity into real-time motor control commands that allow a robot arm to mimic the intended hand movements of trained primates. Here, we describe the results of a comprehensive analysis of the contribution of single cortical neurons to this linear model. Key to the operation of this model was the observation that a large percentage of cortical neurons located in both frontal and parietal cortical areas are tuned for hand position. In most neurons, hand position tuning was time-dependent, varying continuously during a 1-sec period before hand movement onset. The relevance of this physiological finding was demonstrated by showing that maximum contribution of individual neurons to the linear model was only achieved when optimal parameters for the impulse response functions describing time-varying neuronal position tuning were selected. Optimal parameters included impulse response functions with 1.0- to 1.4-sec time length and 50- to 100-msec bins. Although reliable generalization and long-term predictions (60-90 min) could be achieved after 10-min training sessions, we noticed that the model performance degraded over long periods. Part of this degradation was accounted by the observation that neuronal position tuning varied significantly throughout the duration (60-90 min) of a recording session. Altogether, these results indicate that the experimental paradigm described here may be useful not only to investigate aspects of neural population coding, but it may also provide a test bed for the development of clinically useful cortical prosthetic devices aimed at restoring motor functions in severely paralyzed patients.

Action Potentials↗