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

Modeling and Analysis of Neural Spike Trains

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Low-rank graphical models and Bayesian inference in the statistical analysis of noisy neural data

We develop new methods of Bayesian inference, largely in the context of analysis of neuroscience data. The work is broken into several parts. In the first part, we introduce a novel class of joint probability distributions in which exact inference is tractable. Previously it has been difficult to find general constructions for models in which efficient exact inference is possible, outside of certain classical cases. We identify a class of such models that are tractable owing to a certain "low-rank" structure in the potentials that couple neighboring variables. In the second part we develop methods to quantify and measure information loss in analysis of neuronal spike train data due to two types of noise, making use of the ideas developed in the first part. Information about neuronal identity or temporal resolution may be lost during spike detection and sorting, or precision of spike times may be corrupted by various effects. We quantify the information lost due to these effects for the relatively simple but sufficiently broad class of Markovian model neurons. We find that decoders that model the probability distribution of spike-neuron assignments significantly outperform decoders that use only the most likely spike assignments. We also apply the ideas of the low-rank models from the first section to defining a class of prior distributions over the space of stimuli (or other covariate) which, by conjugacy, preserve the tractability of inference. In the third part, we treat Bayesian methods for the estimation of sparse signals, with application to the locating of synapses in a dendritic tree. We develop a compartmentalized model of the dendritic tree. Building on previous work that applied and generalized ideas of least angle regression to obtain a fast Bayesian solution to the resulting estimation problem, we describe two other approaches to the same problem, one employing a horseshoe prior and the other using various spike-and-slab priors. In the last part, we revisit the low-rank models of the first section and apply them to the problem of inferring orientation selectivity maps from noisy observations of orientation preference. The relevant low-rank model exploits the self-conjugacy of the von Mises distribution on the circle. Because the orientation map model is loopy, we cannot do exact inference on the low-rank model by the forward backward algorithm, but block-wise Gibbs sampling by the forward backward algorithm speeds mixing. We explore another von Mises coupling potential Gibbs sampler that proves to effectively smooth noisily observed orientation maps

Point process modeling and estimation: advances in the analysis of dynamic neural spiking data

A common interest of scientists in many fields is to understand the relationship between the dynamics of a physical system and the occurrences of discrete events within such physical system. Seismologists study the connection between mechanical vibrations of the Earth and the occurrences of earthquakes so that future earthquakes can be better predicted. Astrophysicists study the association between the oscillating energy of celestial regions and the emission of photons to learn the Universe's various objects and their interactions. Neuroscientists study the link between behavior and the millisecond-timescale spike patterns of neurons to understand higher brain functions. Such relationships can often be formulated within the framework of state-space models with point process observations. The basic idea is that the dynamics of the physical systems are driven by the dynamics of some stochastic state variables and the discrete events we observe in an interval are noisy observations with distributions determined by the state variables. This thesis proposes several new methodological developments that advance the framework of state-space models with point process observations at the intersection of statistics and neuroscience. In particular, we develop new methods 1) to characterize the rhythmic spiking activity using history-dependent structure, 2) to model population spike activity using marked point process models, 3) to allow for real-time decision making, and 4) to take into account the need for dimensionality reduction for high-dimensional state and observation processes. We applied these methods to a novel problem of tracking rhythmic dynamics in the spiking of neurons in the subthalamic nucleus of Parkinson's patients with the goal of optimizing placement of deep brain stimulation electrodes. We developed a decoding algorithm that can make decision in real-time (for example, to stimulate the neurons or not) based on various sources of information present in population spiking data. Lastly, we proposed a general three-step paradigm that allows us to relate behavioral outcomes of various tasks to simultaneously recorded neural activity across multiple brain areas, which is a step towards closed-loop therapies for psychological diseases using real-time neural stimulation. These methods are suitable for real-time implementation for content-based feedback experiments

Pattern recognition of neural data: methods and algorithms for spike sorting and their optimal performance in prefrontal cortex recordings

Programa de Doctorado en NeurocienciasPattern recognition of neuronal discharges is the electrophysiological basis of the functional characterization of brain processes, so the implementation of a Spike Sorting algorithm is an essential step for the analysis of neural codes and neural interactions in a network or brain circuit. Extracted information from the neural action potential can be used to characterize neural activity events and correlate them during behavioral and cognitive processes, including different types of associative learning tasks. In particular, feature extraction is a critical step in the spike sorting procedure, which is prior to the clustering step and subsequent to the spike detection-identification step in a Spike Sorting algorithm. In the present doctoral thesis, the implementation of an automatic and unsupervised computational algorithm, called 'Unsupervised Automatic Algorithm', is proposed for the detection, identification and classification of the neural action potentials distributed across the electrophysiological recordings; and for clustering of these potentials in function of the shape, phase and distribution features, which are extracted from the first-order derivative of the potentials under study. For this, an efficient and unsupervised clustering method was developed, which integrate the K-means method with two clustering measures (validity and error indices) to verify both the cohesion-dispersion among neural spike during classification and the misclassification of clustering, respectively. In additions, this algorithm was implemented in a customized spike sorting software called VISSOR (Viability of Integrated Spike Sorting of Real Recordings). On the other hand, a supervised grouping method of neural activity profiles was performed to allow the recognition of specific patterns of neural discharges. Validity and effectiveness of these methods and algorithms were tested in this doctoral thesis by the classification of the detected action potentials from extracellular recordings of the rostro-medial prefrontal cortex of rabbits during the classical eyelid conditioning. After comparing the spike-sorting methods/algorithms proposed in this work with other methods also based on feature extraction of the action potentials, it was observed that this one had a better performance during the classification. That is, the methods/algorithms proposed here allowed obtaining: (1) the optimal number of clusters of neuronal spikes (according to the criterion of the maximum value of the cohesion-dispersion index) and (2) the optimal clustering of these spike-events (according to the criterion of the minimum value of the error index). The analytical implication of these results was that the feature extraction based on the shape, phase and distribution features of the action potential, together with the application of an alternative method of unsupervised classification with validity and error indices; guaranteed an efficient classification of neural events, especially for those detected from extracellular or multi-unitary recordings. Rabbits were conditioned with a delay paradigm consisting of a tone as conditioned stimulus. The conditioned stimulus started 50, 250, 500, 1000, or 2000 ms before and co-terminated with an air puff directed at the cornea as unconditioned stimulus. The results obtained indicated that the firing rate of each recorded neuron presented a single peak of activity with a frequency dependent on the inter-stimulus interval (i.e., ¿ 12 Hz for 250 ms, ¿ 6 Hz for 500 ms, and ¿ 3 Hz for 1000 ms). Interestingly, the recorded neurons from the rostro-medial prefrontal cortex presented their dominant firing peaks at three precise times evenly distributed with respect to conditioned stimulus start, and also depending on the duration of the inter-stimulus interval (only for intervals of 250, 500, and 1000 ms). No significant neural responses were recorded at very short (50 ms) or long (2000 ms) conditioned stimulus-unconditioned stimulus time intervals. Furthermore, the eyelid movements were recorded with the magnetic search coil technique and the electromyographic (EMG) activity of the orbicularis oculi muscle. Reflex and conditioned eyelid responses presented a dominant oscillatory frequency of ¿ 12 Hz. The experimental implication of these results is that the recorded neurons from the rostro-medial prefrontal cortex seem not to encode the oscillatory properties characterizing conditioned eyelid responses in rabbits. As a general experimental conclusion, it could be said that rostro-medial prefrontal cortex neurons are probably involved in the determination of CS-US intervals of an intermediate range (250-1000 ms).Universidad Pablo de Olavide. Departamento de Fisiología, Anatomía y Biología CelularPostprin