Detecting multineuronal temporal patterns in parallel spike trains

We present a non-parametric and computationally efficient method that detects spatiotemporal firing patterns and pattern sequences in parallel spike trains and tests whether the observed numbers of repeating patterns and sequences on a given timescale are significantly different from those expected by chance. The method is generally applicable and uncovers coordinated activity with arbitrary precision by comparing it to appropriate surrogate data. The analysis of coherent patterns of spatially and temporally distributed spiking activity on various timescales enables the immediate tracking of diverse qualities of coordinated firing related to neuronal state changes and information processing. We apply the method to simulated data and multineuronal recordings from rat visual cortex and show that it reliably discriminates between data sets with random pattern occurrences and with additional exactly repeating spatiotemporal patterns and pattern sequences. Multineuronal cortical spiking activity appears to be precisely coordinated and exhibits a sequential organization beyond the cell assembly concept

A Semiparametric Bayesian Model for Detecting Synchrony Among Multiple Neurons

We propose a scalable semiparametric Bayesian model to capture dependencies among multiple neurons by detecting their co-firing (possibly with some lag time) patterns over time. After discretizing time so there is at most one spike at each interval, the resulting sequence of 1's (spike) and 0's (silence) for each neuron is modeled using the logistic function of a continuous latent variable with a Gaussian process prior. For multiple neurons, the corresponding marginal distributions are coupled to their joint probability distribution using a parametric copula model. The advantages of our approach are as follows: the nonparametric component (i.e., the Gaussian process model) provides a flexible framework for modeling the underlying firing rates; the parametric component (i.e., the copula model) allows us to make inference regarding both contemporaneous and lagged relationships among neurons; using the copula model, we construct multivariate probabilistic models by separating the modeling of univariate marginal distributions from the modeling of dependence structure among variables; our method is easy to implement using a computationally efficient sampling algorithm that can be easily extended to high dimensional problems. Using simulated data, we show that our approach could correctly capture temporal dependencies in firing rates and identify synchronous neurons. We also apply our model to spike train data obtained from prefrontal cortical areas in rat's brain

Unsupervised Detection of Cell-Assembly Sequences by Similarity-Based Clustering

Neurons which fire in a fixed temporal pattern (i.e., "cell assemblies") are hypothesized to be a fundamental unit of neural information processing. Several methods are available for the detection of cell assemblies without a time structure. However, the systematic detection of cell assemblies with time structure has been challenging, especially in large datasets, due to the lack of efficient methods for handling the time structure. Here, we show a method to detect a variety of cell-assembly activity patterns, recurring in noisy neural population activities at multiple timescales. The key innovation is the use of a computer science method to comparing strings ("edit similarity"), to group spikes into assemblies. We validated the method using artificial data and experimental data, which were previously recorded from the hippocampus of male Long-Evans rats and the prefrontal cortex of male Brown Norway/Fisher hybrid rats. From the hippocampus, we could simultaneously extract place-cell sequences occurring on different timescales during navigation and awake replay. From the prefrontal cortex, we could discover multiple spike sequences of neurons encoding different segments of a goal-directed task. Unlike conventional event-driven statistical approaches, our method detects cell assemblies without creating event-locked averages. Thus, the method offers a novel analytical tool for deciphering the neural code during arbitrary behavioral and mental processes

New statistical methods to derive functional connectivity from multiple spike trains

Analysis of functional connectivity of simultaneously recorded multiple spike trains is one of the major issues in the neuroscience. The progress of the statistical methods to the analysis of functional connectivity of multiple spike trains is relatively slow. In this thesis two statistical techniques are presented to the analysis of functional connectivity of multiple spike trains. The first method is known as the modified correlation grid (MCG). This method is based on the calculation of cross-correlation function of all possible pair-wise spike trains. The second technique is known as the Cox method. This method is based on the modulated renewal process (MRP). The original paper on the application of the Cox method (Borisyuk et al., 1985) to neuroscience data was used to analyse only pairs and triplets of spike trains. This method is further developed in this thesis to support simultaneously recorded of any possible set of multiple spike trains. A probabilistic model is developed to test the Cox method. This probabilistic model is based on the MRP. Due to the common probabilistic basis of the probabilistic model and the Cox method, the probabilistic model is a convenient technique to test the Cox method. A new technique based on a pair-wise analysis of Cox method known as the Cox metric is presented to find the groups of coupled spike trains. Another new technique known as motif analysis is introduced which is useful in identifying interconnections among the spike trains. This technique is based on the triplet-wise analysis of the Cox method. All these methods are applied to several sets of spike trains generated by the Enhanced Leaky and Integrate Fire (ELIF) model. The results suggest that these methods are successful for analysing functional connectivity of simultaneously recorded multiple spike trains. These methods are also applied to an experimental data recorded from cat’s visual cortex. The connection matrix derived from the experimental data by the Cox method is further applied to the graph theoretical methods

Information-geometric method for multiple neuronal spike data analysis

This dissertation explores a novel statistical technique—information geometric method for theory and its application in analysis of multiple neuronal spike data. The previous studies have indicated that information-geometric method provides a powerful tool of estimating neuronal interactions from observed spiking data. However, these studies were conducted based on simplified neural network structure, which has limitations in the real brain. We systematically extended the previous studies by using intensive mathematical analysis and numerical simulations of realistic and complex neural network. The studies show that information geometric approach provide robust estimation for the sum of the connection weights between neuronal pairs in a complex recurrent network, providing a way of investigating the underlying network structures from neuronal spike data.Alberta Innovates Technology Futures (SCH001),National Science Foundation(CRCNS-1010172),Alberta Innovates Health Solution

Bio-mimetic Spiking Neural Networks for unsupervised clustering of spatio-temporal data

Spiking neural networks aspire to mimic the brain more closely than traditional artificial neural networks. They are characterised by a spike-like activation function inspired by the shape of an action potential in biological neurons. Spiking networks remain a niche area of research, perform worse than the traditional artificial networks, and their real-world applications are limited. We hypothesised that neuroscience-inspired spiking neural networks with spike-timing-dependent plasticity demonstrate useful learning capabilities. Our objective was to identify features which play a vital role in information processing in the brain but are not commonly used in artificial networks, implement them in spiking networks without copying constraints that apply to living organisms, and to characterise their effect on data processing. The networks we created are not brain models; our approach can be labelled as artificial life. We performed a literature review and selected features such as local weight updates, neuronal sub-types, modularity, homeostasis and structural plasticity. We used the review as a guide for developing the consecutive iterations of the network, and eventually a whole evolutionary developmental system. We analysed the model’s performance on clustering of spatio-temporal data. Our results show that combining evolution and unsupervised learning leads to a faster convergence on the optimal solutions, better stability of fit solutions than each approach separately. The choice of fitness definition affects the network’s performance on fitness-related and unrelated tasks. We found that neuron type-specific weight homeostasis can be used to stabilise the networks, thus enabling longer training. We also demonstrated that networks with a rudimentary architecture can evolve developmental rules which improve their fitness. This interdisciplinary work provides contributions to three fields: it proposes novel artificial intelligence approaches, tests the possible role of the selected biological phenomena in information processing in the brain, and explores the evolution of learning in an artificial life system