This project has two components that span basic neuroscience and engineering methods. On the neuroscience side, we are interested in the neural mechanisms that underlie sensorimotor integration during associative and perceptual learning. Of particular interest are neural mechanisms underlying working memory. This mechanism is thought to play an indispensable role in integrating multi sensory information from early processing levels to guide motor behavior. We specifically focus on those mechanisms that can be assessed by analyzing the coordinated activity patterns of many - simultaneously observed - neurons. We are also interested in assessing causal relationships between Local Field Potentials (LFPs) and spiking activity of individual neurons during the recall process, and how these relationships change as a a function of learning.
Techniques: We use extracellular recording techniques with micro electrode arrays implanted in selected brain areas of awake behaving animals. Neural data recorded with these arrays have to undergo a sequence of processing steps prior to interpreting the information they contain. Along this realm, the engineering aspects of this project focus on developing new techniques for spike sorting and spike train analysis. This is useful in long-term chronic recording experiments in awake behaving subjects, where non-stationarity in action potential waveform shapes and neuronal firing patterns are typically observed. For spike sorting, we examine the temporal, spectral, and spatial properties of the action potential waveforms to resolve multiple single unit responses (e.g. complex overlapping spikes when two or more cells are synchronized). For spike train analysis, we focus on graphical models from statistical signal processing and machine learning literature. These are particularly useful to track the dynamics of neuronal connectivity and quantify the degree of plasticity in cortical circuits during learning and memory formation, or as a result of sensor deprivation or brain injury.