Adam S. Dickey

Single-Unit Stability Using Chronically Implanted Multielectrode Arrays

The use of chronic intracortical multielectrode arrays has become increasingly prevalent in neurophysiological experiments. However, it is not obvious whether neuronal signals obtained over multiple recording sessions come from the same or different neurons. Here, we develop a criterion to assess single-unit stability by measuring the similarity of 1) average spike waveforms and 2) interspike interval histograms (ISIHs). Neuronal activity was recorded from four Utah arrays implanted in primary motor and premotor cortices in three rhesus macaque monkeys during 10 recording sessions over a 15- to 17-day period. A unit was defined as stable through a given day if the stability criterion was satisfied on all recordings leading up to that day. We found that 57% of the original units were stable through 7 days, 43% were stable through 10 days, and 39% were stable through 15 days. Moreover, stable units were more likely to remain stable in subsequent recording sessions (i.e., 89% of the neurons that were stable through four sessions remained stable on the fifth). Using both waveform and ISIH data instead of just waveforms improved performance by reducing the number of false positives. We also demonstrate that this method can be used to track neurons across days, even during adaptation to a visuomotor rotation. Identifying a stable subset of neurons should allow the study of long-term learning effects across days and has practical implications for pooling of behavioral data across days and for increasing the effectiveness of brain-machine interfaces.

Heterogeneous neural coding of corrective movements in motor cortex.

During a reach, neural activity recorded from motor cortex is typically thought to linearly encode the observed movement. However, it has also been reported that during a double-step reaching paradigm, neural coding of the original movement is replaced by that of the corrective movement. Here, we use neural data recorded from multi-electrode arrays implanted in the motor and premotor cortices of rhesus macaques to directly compare these two hypotheses. We show that while a majority of neurons display linear encoding of movement during a double-step, a minority display a dramatic drop in firing rate that is predicted by the replacement hypothesis. Neural activity in the subpopulation showing replacement is more likely to lag the observed movement, and may therefore be involved in the monitoring of the sensory consequences of a motor command.

Ultra-long term stability of single units using chronically implanted multielectrode arrays.

Recordings from chronically implanted multielectrode arrays have become prevalent in both neuroscience and neural engineering experiments. To date, however, the extent to which populations of single-units remain stable over long periods of time has not been well characterized. In this study, neural activity was recorded from a Utah multielectrode array implanted in the primary motor cortex of a rhesus macaque during 18 recording sessions spanning nine months. We found that 67% of the units were stable through the first 15 days, 31% of units were stable through 47 days, 21% of units were stable through 106 days, and 8% of units were stable over 9 months. Thus not only were units stable over a timescale of several months, but units stable over 2 months were more likely to remain stable in the next 2 months.

Chapter 31 – Decision Theory

In this chapter, you will learn how to implement progressively more comprehensive mathematical models of decision making using MATLAB®. The exploration of decision models will introduce solving partial differential equations as finite differences, focusing on the diffusion equation. A simple model accounting for perceptual decisions and corresponding activity in cortical areas LIP and MT will be discussed.

Functional Magnetic Imaging

This chapter focuses on functional magnetic imaging (fMRI) as a fundamental noninvasive tool in understanding brain functioning in humans. It describes the basic physics behind structural and functional magnetic resonance imaging, and after that it explains the major experimental paradigms used in fMRI research and the kinds of data that are collected in an fMRI experiment. Functional magnetic resonance imaging has emerged as the dominant form of noninvasive functional imaging in humans. Although it is a relatively young technology that began in the early 1990s, it now plays a major role in many subfields of psychology, cognitive science, and neuroscience. It is even creeping up in other disciplines such as sociology and economics. As of the beginning of 2008, a quick online search of articles on PubMed revealed over 180,000 papers that reference the use of fMRI. Some have criticized fMRI as a scientific tool, claiming that it is little more than modern phrenology. Finally, using existing fMRI data from a simple finger-tapping task, it shows how to analyze and visualize the data to come up with a statistical parametric map of activation in the brain.

Frequency Analysis Part II

This chapter defines the most common method of decomposing a time series into frequency components, Fourier analysis. It also describes the MATLAB implementation of the Fast Fourier Transform (FFT), an efficient algorithm for calculating Fourier transformations and application to the analysis of human speech sounds. With a few special tricks, a faster algorithm, the FFT, that scales in N log N time can be formulated. One of these tricks involves taking advantage of datasets exactly 2N elements long. The increase in processing speed has made the FFT ubiquitous in signal processing. The chapter illustrates the project that defines Fourier decomposition to analyze vowel sounds produced by human speakers. The human vocal tract has multiple cavities in which speech sounds resonate. As such, most sounds have multiple strong frequency components. In classifying speech sounds, the lowest strong frequency band is termed the first formant. The next highest is termed the second formant, and so on. Vowels lend themselves to a particularly simple characterization through their formants. Typically, vowel sounds have distinguishable first and second formants.

Neural Networks Part I

This chapter deals with neural networks using Neural Networks Toolbox built into the MATLAB® software to address a particular problem. The major goal is to become familiar with the general concept of unsupervised neural networks and how they may relate to certain forms of synaptic plasticity in the nervous system. Neural networks have assumed a central role in a variety of fields. They are actually quite abstract computing structures. In fact, they are sometimes referred to as artificial neural networks. The nature of this role is fundamentally dualistic. On the one hand, neural networks can provide powerful models of elementary processes in the brain, including processes of plasticity and learning. On the other hand, they provide solutions to a broad range of specific problems in applied engineering, such as speech recognition, financial forecasting, or object classification.

Chapter 10 – Signal Detection Theory

This chapter will mostly concern the use of signal detection theory to analyze data generated in psychophysical—and hypothetical neurophysiological—experiments. We will do this in MATLAB®.

Psychophysics with GUIs

This chapter pursues dual goals. First, we want to build on the data collection within a psychophysical paradigm approach. Second, and more importantly, this chapter will introduce the concept of a graphical user interface (GUI) within MATLAB® and demonstrate its gainful use.

Chapter 37 – Neural Networks Part II: Supervised Learning

This chapter has two primary goals. The first goal is to be introduced to the concept of supervised learning and how it may relate to synaptic plasticity in the nervous system, particularly in the cerebellum. The second goal is to learn to implement single-layer and multi-layer neural network architectures using supervised learning rules to solve particular problems.