Extracellular electrophysiology with close-packed recording sites: spike sorting and characterization

Advances in recording technologies now allow us to record populations of neurons simultaneously, data necessary to understand the network dynamics of the brain. Extracellular probes are fabricated with ever greater numbers of recording sites to capture the activity of increasing numbers of neurons. However, the utility of this extracellular data is limited by an initial analysis step, spike sorting, that extracts the activity patterns of individual neurons from the extracellular traces. Commonly used spike sorting methods require manual processing that limits their scalability, and errors can bias downstream analyses. Leveraging the replication of the activity from a single neuron on nearby recording sites, we designed a spike sorting method consisting of three primary steps: (1) a blind source separation algorithm to estimate the underlying source components, (2) a spike detection algorithm to find the set of spikes from each component best separated from background activity and (3) a classifier to evaluate if a set of spikes came from one individual neuron. To assess the accuracy of our method, we simulated multi-electrode array data that encompass many of the realistic variations and the sources of noise in in vivo neural data. Our method was able to extract individual simulated neurons in an automated fashion without any errors in spike assignment. Further, the number of neurons extracted increased as we increased recording site count and density. To evaluate our method in vivo, we performed both extracellular recording with our close-packed probes and a co-localized patch clamp recording, directly measuring one neuron’s ground truth set of spikes. Using this in vivo data we found that when our spike sorting method extracted the patched neuron, the spike assignment error rates were at the low end of reported error rates, and that our errors were frequently the result of failed spike detection during bursts where spike amplitude decreased into the noise. We used our in vivo data to characterize the extracellular recordings of burst activity and more generally what an extracellular electrode records. With this knowledge, we updated our spike detector to capture more burst spikes and improved our classifier based on our characterizations

The Measurement of Information Transmitted by a Neural Population: Promises and Challenges

All brain functions require the coordinated activity of many neurons, and therefore there is considerable interest in estimating the amount of information that the discharge of a neural population transmits to its targets. In the past, such estimates had presented a significant challenge for populations of more than a few neurons, but we have recently described a novel method for providing such estimates for populations of essentially arbitrary size. Here, we explore the influence of some important aspects of the neuronal population discharge on such estimates. In particular, we investigate the roles of mean firing rate and of the degree and nature of correlations among neurons. The results provide constraints on the applicability of our new method and should help neuroscientists determine whether such an application is appropriate for their data

Uncovering representations of sleep-associated hippocampal ensemble spike activity

Pyramidal neurons in the rodent hippocampus exhibit spatial tuning during spatial navigation, and they are reactivated in specific temporal order during sharp-wave ripples observed in quiet wakefulness or slow wave sleep. However, analyzing representations of sleep-associated hippocampal ensemble spike activity remains a great challenge. In contrast to wake, during sleep there is a complete absence of animal behavior, and the ensemble spike activity is sparse (low occurrence) and fragmental in time. To examine important issues encountered in sleep data analysis, we constructed synthetic sleep-like hippocampal spike data (short epochs, sparse and sporadic firing, compressed timescale) for detailed investigations. Based upon two Bayesian population-decoding methods (one receptive field-based, and the other not), we systematically investigated their representation power and detection reliability. Notably, the receptive-field-free decoding method was found to be well-tuned for hippocampal ensemble spike data in slow wave sleep (SWS), even in the absence of prior behavioral measure or ground truth. Our results showed that in addition to the sample length, bin size, and firing rate, number of active hippocampal pyramidal neurons are critical for reliable representation of the space as well as for detection of spatiotemporal reactivated patterns in SWS or quiet wakefulness.Collaborative Research in Computational Neuroscience (Award IIS-1307645)United States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N00014-10-1-0936)National Institutes of Health (U.S.) (Grant TR01-GM10498

PRINCIPLES OF INFORMATION PROCESSING IN NEURONAL AVALANCHES

How the brain processes information is poorly understood. It has been suggested that the imbalance of excitation and inhibition (E/I) can significantly affect information processing in the brain. Neuronal avalanches, a type of spontaneous activity recently discovered, have been ubiquitously observed in vitro and in vivo when the cortical network is in the E/I balanced state. In this dissertation, I experimentally demonstrate that several properties regarding information processing in the cortex, i.e. the entropy of spontaneous activity, the information transmission between stimulus and response, the diversity of synchronized states and the discrimination of external stimuli, are optimized when the cortical network is in the E/I balanced state, exhibiting neuronal avalanche dynamics. These experimental studies not only support the hypothesis that the cortex operates in the critical state, but also suggest that criticality is a potential principle of information processing in the cortex. Further, we study the interaction structure in population neuronal dynamics, and discovered a special structure of higher order interactions that are inherent in the neuronal dynamics

ELECTROPHYSIOLOGY OF BASAL GANGLIA (BG) CIRCUITRY AND DYSTONIA AS A MODEL OF MOTOR CONTROL DYSFUNCTION

The basal ganglia (BG) is a complex set of heavily interconnected nuclei located in the central part of the brain that receives inputs from the several areas of the cortex and projects via the thalamus back to the prefrontal and motor cortical areas. Despite playing a significant part in multiple brain functions, the physiology of the BG and associated disorders like dystonia remain poorly understood. Dystonia is a devastating condition characterized by ineffective, twisting movements, prolonged co-contractions and contorted postures. Evidences suggest that it occurs due to abnormal discharge patterning in BG-thalamocortocal (BGTC) circuitry. The central purpose of this study was to understand the electrophysiology of BGTC circuitry and its role in motor control and dystonia. Toward this goal, an advanced multi-target multi-unit recording and analysis system was utilized, which allows simultaneous collection and analysis of multiple neuronal units from multiple brain nuclei. Over the cause of this work, neuronal data from the globus pallidus (GP), subthalamic nucleus (STN), entopenduncular nucleus (EP), pallidal receiving thalamus (VL) and motor cortex (MC) was collected from normal, lesioned and dystonic rats under awake, head restrained conditions. The results have shown that the neuronal population in BG nuclei (GP, STN and EP) were characterized by a dichotomy of firing patterns in normal rats which remains preserved in dystonic rats. Unlike normals, neurons in dystonic rat exhibit reduced mean firing rate, increased irregularity and burstiness at resting state. The chaotic changes that occurs in BG leads to inadequate hyperpolarization levels within the VL thalamic neurons resulting in a shift from the normal bursting mode to an abnormal tonic firing pattern. During movement, the dystonic EP generates abnormally synchronized and elongated burst duration which further corrupts the VL motor signals. It was finally concluded that the loss of specificity and temporal misalignment between motor neurons leads to corrupted signaling to the muscles resulting in dystonic behavior. Furthermore, this study reveals the importance of EP output in controlling firing modes occurring in the VL thalamus

Information Carried by Population Spike Times in the Whisker Sensory Cortex can be Decoded Without Knowledge of Stimulus Time

Computational analyses have revealed that precisely timed spikes emitted by somatosensory cortical neuronal populations encode basic stimulus features in the rat's whisker sensory system. Efficient spike time based decoding schemes both for the spatial location of a stimulus and for the kinetic features of complex whisker movements have been defined. To date, these decoding schemes have been based upon spike times referenced to an external temporal frame – the time of the stimulus itself. Such schemes are limited by the requirement of precise knowledge of the stimulus time signal, and it is not clear whether stimulus times are known to rats making sensory judgments. Here, we first review studies of the information obtained from spike timing referenced to the stimulus time. Then we explore new methods for extracting spike train information independently of any external temporal reference frame. These proposed methods are based on the detection of stimulus-dependent differences in the firing time within a neuronal population. We apply them to a data set using single-whisker stimulation in anesthetized rats and find that stimulus site can be decoded based on the millisecond-range relative differences in spike times even without knowledge of stimulus time. If spike counts alone are measured over tens or hundreds of milliseconds rather than milliseconds, such decoders are much less effective. These results suggest that decoding schemes based on millisecond-precise spike times are likely to subserve robust and information-rich transmission of information in the somatosensory system

Estimating the Amount of Information Conveyed by a Population of Neurons

Recent advances in electrophysiological recording technology have allowed for the collection of data from large populations of neurons simultaneously. Yet despite these advances, methods for the estimation of the amount of information conveyed by multiple neurons have been stymied by the “curse of dimensionality”–as the number of included neurons increases, so too does the dimensionality of the data necessary for such measurements, leading to an exponential and, therefore, intractible increase in the amounts of data required for valid measurements. Here we put forth a novel method for the estimation of the amount of information transmitted by the discharge of a large population of neurons, a method which exploits the little-known fact that (under certain constraints) the Fourier coefficients of variables such as neural spike trains follow a Gaussian distribution. This fact enables an accurate measure of information even with limited data. The method, which we call the Fourier Method, is presented in detail, tested for robustness, and its application is demonstrated with both simulated and real spike trains. ii