Extracellular chronic recordings have been used as important evidence in neuroscientific studies to unveil the fundamental neural network mechanisms in the brain. Spike detection is the first step in the analysis of recorded neural waveforms to decipher useful information and provide useful signals for brain-machine interface applications. The process of spike detection is to extract action potentials from the recordings, which are often compounded with noise from different sources. This study proposes a new detection algorithm that leverages a technique from wavelet-based image edge detection. It utilizes the correlation between wavelet coefficients at different sampling scales to create a robust spike detector. The algorithm has one tuning parameter, which potentially reduces the subjectivity of detection results. Both artificial benchmark data sets and real neural recordings are used to evaluate the detection performance of the proposed algorithm. Compared with other detection algorithms, the proposed method has a comparable or better detection performance. In this letter, we also demonstrate its potential for real-time implementation.

Some insights on the convergence of the weight values of the self-organizing map (SOM) to a stationary state in the case of discrete input are provided. The convergence result is obtained by applying the Robbins-Monro algorithm and is applicable to input-output maps of any dimension.

Most neural network applications rely on the fundamental approximation property of feedforward networks. Supervised learning is a means of implementing this approximate mapping. In a realistic problem setting, a mechanism is needed to devise this learning process based on available data, which encompasses choosing an appropriate set of parameters in order to avoid overfitting, using an efficient learning algorithm measured by computation and memory complexities, ensuring the accuracy of the training procedure as measured by the training error, and testing and cross-validation for generalization. We develop a comprehensive supervised learning algorithm to address these issues. The algorithm combines training and pruning into one procedure by utilizing a common observation of Jacobian rank deficiency in feedforward networks. The algorithm not only reduces the training time and overall complexity but also achieves training accuracy and generalization capabilities comparable to more standard approaches. Extensive simulation results are provided to demonstrate the effectiveness of the algorithm.

The population vector method has been developed to combine the simultaneous direction-related activities of a population of motor cortical neurons to predict the trajectory of the arm movement. In this article, we consider a self-organizing model of a neural representation of the arm trajectory based on neuronal discharge rates. A self-organizing feature map (SOFM) is used to select the optimal set of weights in the model to determine the contribution of an individual neuron to an overall movement representation. The correspondence between movement directions and discharge patterns of the motor cortical neurons is established in the output map. The topology-preserving property of the SOFM is used to analyze the recorded data of a behaving monkey. The data used in this analysis were taken while the monkey was tracing spirals and doing center→out movements. The arm trajectory could be well predicted using such a statistical model based on the motor cortex neuronal firing information. The SOFM method is compared with the population vector method, which extracts information related to trajectory by assuming that each cell has a fixed preferred direction during the task. This implies that these cells are acting along lines labeled only for direction. However, extradirectional information is carried in these cell responses. The SOFM has the capability of extracting not only direction-related information but also other parameters that are consistently represented in the activity of the recorded population of cells.