378 research outputs found
NeuSort: An Automatic Adaptive Spike Sorting Approach with Neuromorphic Models
Objective. Spike sorting, a critical step in neural data processing, aims to
classify spiking events from single electrode recordings based on different
waveforms. This study aims to develop a novel online spike sorter, NeuSort,
using neuromorphic models, with the ability to adaptively adjust to changes in
neural signals, including waveform deformations and the appearance of new
neurons. Approach. NeuSort leverages a neuromorphic model to emulate
template-matching processes. This model incorporates plasticity learning
mechanisms inspired by biological neural systems, facilitating real-time
adjustments to online parameters. Results. Experimental findings demonstrate
NeuSort's ability to track neuron activities amidst waveform deformations and
identify new neurons in real-time. NeuSort excels in handling non-stationary
neural signals, significantly enhancing its applicability for long-term spike
sorting tasks. Moreover, its implementation on neuromorphic chips guarantees
ultra-low energy consumption during computation. Significance. NeuSort caters
to the demand for real-time spike sorting in brain-machine interfaces through a
neuromorphic approach. Its unsupervised, automated spike sorting process makes
it a plug-and-play solution for online spike sorting
Bee gustatory neurons encode sugar concentration as a coherent temporal pattern of spiking
PhD ThesisIndividual peripheral gustatory neurons in insects encode stimulus category (e.g. sweet,
bitter) and concentration as a tonic rate of spiking that adapts with prolonged stimulation. While
individual chemosensory neurons have been shown to interact through mutual inhibition, this
interaction does not affect stimulus coding by the activated neuron. Here, I report the first
evidence of a coherent, temporal pattern of spiking produced by the interaction of the gustatory
receptor neurons (GRNs) within sensilla present on the mouthparts of bumblebees (Bombus
terrestris) that encodes information about sugar concentration. Stimulation of gustatory sensilla
with sucrose concentrations >10 mM elicited bursts of spikes riding on an oscillation in voltage
of ~20 Hz. The concentration response function of spiking and bursting was sugar-identity
specific, and only concentrations that produced bursting in the GRNs elicited the bee’s feeding
reflex. Bursting bee GRNs exhibited a low rate of adaptation (0.002 s adaptation after 1 s of
stimulation) compared to rates measured from other insect species’ GRNs. These data are the first
to show that primary chemosensory neurons encode stimulus features such as concentration as a
coherent temporal pattern of spiking produced as an interaction between two neurons. I propose
that 1) the silent period between bursts is driven by the spike after-hyperpolarization of one
neuron, which inhibits spiking of its neighboring neuron through an inhibitory lateral interaction,
and 2) bursting is a novel mechanism evolved to allow persistent high frequency spiking during
fluid consumption. Finally, I show that neural activity can be monitored from the bee’s central
nervous system, which allows future experiments to question the function of this coherent and
structured GRN activity in driving post-synaptic responses
Toward Building Hybrid Biological/in silico Neural Networks for Motor Neuroprosthetic Control
WOS: 000370402900001PubMed ID: 26321943In this article, we introduce the Bioinspired Neuroprosthetic Design Environment (BNDE) as a practical platform for the development of novel brain-machine interface (BMI) controllers, which are based on spiking model neurons. We built the BNDE around a hard real-time system so that it is capable of creating simulated synapses from extra-cellularly recorded neurons to model neurons. In order to evaluate the practicality of the BNDE for neuroprosthetic control experiments, a novel, adaptive BMI controller was developed and tested using real-time closed-loop simulations. The present controller consists of two in silico medium spiny neurons, which receive simulated synaptic inputs from recorded motor cortical neurons. In the closed-loop simulations, the recordings from the cortical neurons were imitated using an external, hardware-based neural signal synthesizer. By implementing a reward-modulated spike timing-dependent plasticity rule, the controller achieved perfect target reach accuracy for a two-target reaching task in one-dimensional space. The BNDE combines the flexibility of software-based spiking neural network (SNN) simulations with powerful online data visualization tools and is a low-cost, PC-based, and all-in-one solution for developing neurally inspired BMI controllers. We believe that the BNDE is the first implementation, which is capable of creating hybrid biological/in silico neural networks for motor neuroprosthetic control and utilizes multiple CPU cores for computationally intensive real-time SNN simulations.Bogazici University BAP Grants [10XD3]; Bogazici University Life Sciences and Technologies Research Center [09K120520]This research was supported by Bogazici University BAP Grants #10XD3 and Bogazici University Life Sciences and Technologies Research Center #09K120520
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Multi-electrode array recording and data analysis methods for molluscan central nervous systems
In this work the use of the central nervous system (CNS) of the aquatic
snail Lymnaea stagnalis on planar multi-electrode arrays (MEAs) was
developed and analysis methods for the data generated were created.
A variety of different combinations of configurations of tissue from the
Lymnaea CNS were explored to determine the signal characteristics
that could be recorded by sixty channel MEAs. In particular, the
suitability of the semi-intact system consisting of the lips, oesophagus,
CNS, and associated nerve connectives was developed for use on
the planar MEA. The recording target area of the dorsal surface of
the buccal ganglia was selected as being the most promising for study
and recordings of its component cells during fictive feeding behaviour
stimulated by sucrose were made. The data produced by this type of
experimentation is very high volume and so its analysis required the
development of a custom set of software tools. The goal of this tool
set is to find the signal from individual neurons in the data streams of
the electrodes of a planar MEA, to estimate their position, and then
to predict their causal connectivity. To produce such an analysis techniques
for noise filtration, neural spike detection, and group detection
of bursts of spikes were created to pre-process electrode data streams.
The Kohonen self-organising map (SOM) algorithm was adapted for
the purpose of separating detected spikes into data streams representing
the spike output of individual cells found in the target system. A
significant addition to SOM algorithm was developed by the concurrent
use of triangulation methods based on current source density
analysis to predict the position of individual cells based on their spike
output on more than one electrode. The likely functional connectivity
of individual neurons identified by the SOM technique were analysed
through the use of a statistical causality method known as Granger
causality/causal connectivity. This technique was used to produce a
map of the likely connectivity between neural sources
Model Based Automatic and Robust Spike Sorting for Large Volumes of Multi-channel Extracellular Data
abstract: Spike sorting is a critical step for single-unit-based analysis of neural activities extracellularly and simultaneously recorded using multi-channel electrodes. When dealing with recordings from very large numbers of neurons, existing methods, which are mostly semiautomatic in nature, become inadequate.
This dissertation aims at automating the spike sorting process. A high performance, automatic and computationally efficient spike detection and clustering system, namely, the M-Sorter2 is presented. The M-Sorter2 employs the modified multiscale correlation of wavelet coefficients (MCWC) for neural spike detection. At the center of the proposed M-Sorter2 are two automatic spike clustering methods. They share a common hierarchical agglomerative modeling (HAM) model search procedure to strategically form a sequence of mixture models, and a new model selection criterion called difference of model evidence (DoME) to automatically determine the number of clusters. The M-Sorter2 employs two methods differing by how they perform clustering to infer model parameters: one uses robust variational Bayes (RVB) and the other uses robust Expectation-Maximization (REM) for Student’s -mixture modeling. The M-Sorter2 is thus a significantly improved approach to sorting as an automatic procedure.
M-Sorter2 was evaluated and benchmarked with popular algorithms using simulated, artificial and real data with truth that are openly available to researchers. Simulated datasets with known statistical distributions were first used to illustrate how the clustering algorithms, namely REMHAM and RVBHAM, provide robust clustering results under commonly experienced performance degrading conditions, such as random initialization of parameters, high dimensionality of data, low signal-to-noise ratio (SNR), ambiguous clusters, and asymmetry in cluster sizes. For the artificial dataset from single-channel recordings, the proposed sorter outperformed Wave_Clus, Plexon’s Offline Sorter and Klusta in most of the comparison cases. For the real dataset from multi-channel electrodes, tetrodes and polytrodes, the proposed sorter outperformed all comparison algorithms in terms of false positive and false negative rates. The software package presented in this dissertation is available for open access.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201
Scalable software and models for large-scale extracellular recordings
The brain represents information about the world through the electrical activity of
populations of neurons. By placing an electrode near a neuron that is firing (spiking), it
is possible to detect the resulting extracellular action potential (EAP) that is transmitted
down an axon to other neurons. In this way, it is possible to monitor the communication
of a group of neurons to uncover how they encode and transmit information. As the
number of recorded neurons continues to increase, however, so do the data processing
and analysis challenges. It is crucial that scalable software and analysis tools are developed
and made available to the neuroscience community to keep up with the large
amounts of data that are already being gathered.
This thesis is composed of three pieces of work which I develop in order to better
process and analyze large-scale extracellular recordings. My work spans all stages of extracellular
analysis from the processing of raw electrical recordings to the development
of statistical models to reveal underlying structure in neural population activity.
In the first work, I focus on developing software to improve the comparison and adoption
of different computational approaches for spike sorting. When analyzing neural
recordings, most researchers are interested in the spiking activity of individual neurons,
which must be extracted from the raw electrical traces through a process called
spike sorting. Much development has been directed towards improving the performance
and automation of spike sorting. This continuous development, while essential,
has contributed to an over-saturation of new, incompatible tools that hinders rigorous
benchmarking and complicates reproducible analysis. To address these limitations, I
develop SpikeInterface, an open-source, Python framework designed to unify preexisting
spike sorting technologies into a single toolkit and to facilitate straightforward
benchmarking of different approaches. With this framework, I demonstrate that modern,
automated spike sorters have low agreement when analyzing the same dataset, i.e.
they find different numbers of neurons with different activity profiles; This result holds
true for a variety of simulated and real datasets. Also, I demonstrate that utilizing a
consensus-based approach to spike sorting, where the outputs of multiple spike sorters
are combined, can dramatically reduce the number of falsely detected neurons.
In the second work, I focus on developing an unsupervised machine learning approach
for determining the source location of individually detected spikes that are
recorded by high-density, microelectrode arrays. By localizing the source of individual
spikes, my method is able to determine the approximate position of the recorded neuriii
ons in relation to the microelectrode array. To allow my model to work with large-scale
datasets, I utilize deep neural networks, a family of machine learning algorithms that
can be trained to approximate complicated functions in a scalable fashion. I evaluate
my method on both simulated and real extracellular datasets, demonstrating that it is
more accurate than other commonly used methods. Also, I show that location estimates
for individual spikes can be utilized to improve the efficiency and accuracy of spike
sorting. After training, my method allows for localization of one million spikes in approximately
37 seconds on a TITAN X GPU, enabling real-time analysis of massive
extracellular datasets.
In my third and final presented work, I focus on developing an unsupervised machine
learning model that can uncover patterns of activity from neural populations
associated with a behaviour being performed. Specifically, I introduce Targeted Neural
Dynamical Modelling (TNDM), a statistical model that jointly models the neural activity
and any external behavioural variables. TNDM decomposes neural dynamics (i.e.
temporal activity patterns) into behaviourally relevant and behaviourally irrelevant dynamics;
the behaviourally relevant dynamics constitute all activity patterns required
to generate the behaviour of interest while behaviourally irrelevant dynamics may be
completely unrelated (e.g. other behavioural or brain states), or even related to behaviour
execution (e.g. dynamics that are associated with behaviour generally but are not
task specific). Again, I implement TNDM using a deep neural network to improve its
scalability and expressivity. On synthetic data and on real recordings from the premotor
(PMd) and primary motor cortex (M1) of a monkey performing a center-out reaching
task, I show that TNDM is able to extract low-dimensional neural dynamics that are
highly predictive of behaviour without sacrificing its fit to the neural data
Binocular Encoding in the Damselfly Pre-motor Target Tracking System.
Akin to all damselflies, Calopteryx (family Calopterygidae), commonly known as jewel wings or demoiselles, possess dichoptic (separated) eyes with overlapping visual fields of view. In contrast, many dragonfly species possess holoptic (dorsally fused) eyes with limited binocular overlap. We have here compared the neuronal correlates of target tracking between damselfly and dragonfly sister lineages and linked these changes in visual overlap to pre-motor neural adaptations. Although dragonflies attack prey dorsally, we show that demoiselles attack prey frontally. We identify demoiselle target-selective descending neurons (TSDNs) with matching frontal visual receptive fields, anatomically and functionally homologous to the dorsally positioned dragonfly TSDNs. By manipulating visual input using eyepatches and prisms, we show that moving target information at the pre-motor level depends on binocular summation in demoiselles. Consequently, demoiselles encode directional information in a binocularly fused frame of reference such that information of a target moving toward the midline in the left eye is fused with information of the target moving away from the midline in the right eye. This contrasts with dragonfly TSDNs, where receptive fields possess a sharp midline boundary, confining responses to a single visual hemifield in a sagittal frame of reference (i.e., relative to the midline). Our results indicate that, although TSDNs are conserved across Odonata, their neural inputs, and thus the upstream organization of the target tracking system, differ significantly and match divergence in eye design and predatory strategies. VIDEO ABSTRACT
Advances in point process modeling: feature selection, goodness-of-fit and novel applications
The research contained in this thesis extends multivariate marked point process modeling methods for neuroscience, generalizes goodness-of-fit techniques for the class of marked point processes, and introduces the use of a general history-dependent point process model to the domain of sleep apnea.
Our first project involves further development of a modeling tool for spiking data from neural populations using the theory of marked point processes. This marked point process model uses features of spike waveforms as marks in order to estimate a state variable of interest. We examine the informational content of geometric features as well as principal components of the waveforms at hippocampal place cell activity by comparing decoding accuracies of a rat's position along a track. We determined that there was additional information available beyond that contained in traditional geometric features used for decoding in practice.
The expanded use of this marked point process model in neuroscience necessitates corresponding goodness-of-fit protocols for the marked case. In our second project, we develop a generalized time-rescaling method for marked point processes that produces uniformly distributed spikes under a proper model. Once rescaled, the ground process then behaves as a Poisson process and can be analyzed using traditional point process goodness-of-fit methods. We demonstrate the method's ability to detect quality and manner of fit through both simulation and real neural data analysis.
In the final project, we introduce history-dependent point process modeling as a superior method for characterizing severe sleep apnea over the current clinical standard known as the apnea-hypopnea index (AHI). We analyze model fits using combinations of both clinical covariates and event observations themselves through functions of history. Ultimately, apnea onset times were consistently estimated with significantly higher accuracy when history was incorporated alongside sleep stage. We present this method to the clinical audience as a means to gain detailed information on patterns of apnea and to provide more customized diagnoses and treatment prescriptions.
These separate yet complementary projects extend existing point process modeling methods and further demonstrate their value in the neurosciences, sleep sciences, and beyond
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Statistical Machine Learning & Deep Neural Networks Applied to Neural Data Analysis
Computational neuroscience seeks to discover the underlying mechanisms by which neural activity is generated. With the recent advancement in neural data acquisition methods, the bottleneck of this pursuit is the analysis of ever-growing volume of neural data acquired in numerous labs from various experiments. These analyses can be broadly divided into two categories. First, extraction of high quality neuronal signals from noisy large scale recordings. Second, inference for statistical models aimed at explaining the neuronal signals and underlying processes that give rise to them. Conventionally, majority of the methodologies employed for this effort are based on statistics and signal processing. However, in recent years recruiting Artificial Neural Networks (ANN) for neural data analysis is gaining traction. This is due to their immense success in computer vision and natural language processing, and the stellar track record of ANN architectures generalizing to a wide variety of problems. In this work we investigate and improve upon statistical and ANN machine learning methods applied to multi-electrode array recordings and inference for dynamical systems that play critical roles in computational neuroscience.
In the first and second part of this thesis, we focus on spike sorting problem. The analysis of large-scale multi-neuronal spike train data is crucial for current and future of neuroscience research. However, this type of data is not available directly from recordings and require further processing to be converted into spike trains. Dense multi-electrode arrays (MEA) are standard methods for collecting such recordings. The processing needed to extract spike trains from these raw electrical signals is carried out by ``spike sorting'' algorithms. We introduce a robust and scalable MEA spike sorting pipeline YASS (Yet Another Spike Sorter) to address many challenges that are inherent to this task. We primarily pay attention to MEA data collected from the primate retina for important reasons such as the unique challenges and available side information that ultimately assist us in scoring different spike sorting pipelines. We also introduce a Neural Network architecture and an accompanying training scheme specifically devised to address the challenging task of deconvolution in MEA recordings.
In the last part, we shift our attention to inference for non-linear dynamics. Dynamical systems are the governing force behind many real world phenomena and temporally correlated data. Recently, a number of neural network architectures have been proposed to address inference for nonlinear dynamical systems. We introduce two different methods based on normalizing flows for posterior inference in latent non-linear dynamical systems. We also present gradient-based amortized posterior inference approaches using the auto-encoding variational Bayes framework that can be applied to a wide range of generative models with nonlinear dynamics. We call our method (FNF). FNF performs favorably against state-of-the-art inference methods in terms of accuracy of predictions and quality of uncovered codes and dynamics on synthetic data
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