Spike detection using the continuous wavelet transform

This paper combines wavelet transforms with basic detection theory to develop a new unsupervised method for robustly detecting and localizing spikes in noisy neural recordings. The method does not require the construction of templates, or the supervised setting of thresholds. We present extensive Monte Carlo simulations, based on actual extracellular recordings, to show that this technique surpasses other commonly used methods in a wide variety of recording conditions. We further demonstrate that falsely detected spikes corresponding to our method resemble actual spikes more than the false positives of other techniques such as amplitude thresholding. Moreover, the simplicity of the method allows for nearly real-time execution

Neural population coding: combining insights from microscopic and mass signals

Behavior relies on the distributed and coordinated activity of neural populations. Population activity can be measured using multi-neuron recordings and neuroimaging. Neural recordings reveal how the heterogeneity, sparseness, timing, and correlation of population activity shape information processing in local networks, whereas neuroimaging shows how long-range coupling and brain states impact on local activity and perception. To obtain an integrated perspective on neural information processing we need to combine knowledge from both levels of investigation. We review recent progress of how neural recordings, neuroimaging, and computational approaches begin to elucidate how interactions between local neural population activity and large-scale dynamics shape the structure and coding capacity of local information representations, make them state-dependent, and control distributed populations that collectively shape behavior

Sensory coding in supragranular cells of the vibrissal cortex in anesthetized and awake mice

Sensory perception entails reliable representation of the external stimuli as impulse activity of individual neurons (i.e. spikes) and neuronal populations in the sensory area. An ongoing challenge in neuroscience is to identify and characterize the features of the stimuli which are relevant to a specific sensory modality and neuronal strategies to effectively and efficiently encode those features. It is widely hypothesized that the neuronal populations employ “sparse coding” strategies to optimize the stimulus representations with a low energetic cost (i.e. low impulse activity). In the past two decades, a wealth of experimental evidence has supported this hypothesis by showing spatiotemporally sparse activity in sensory area. Despite numerous studies, the extent of sparse coding and its underlying mechanisms are not fully understood, especially in primary vibrissal somatosensory cortex (vS1), which is a key model system in sensory neuroscience. Importantly, it is not clear yet whether sparse activation of supragranular vS1 is due to insufficient synaptic input to the majority of the cells or the absence of effective stimulus features. In this thesis, first we asked how the choice of stimulus could affect the degree of sparseness and/or the overall fraction of the responsive vS1 neurons. We presented whisker deflections spanning a broad range of intensities, including “standard stimuli” and a high-velocity, “sharp” stimulus, which simulated the fast slip events that occur during whisker mediated object palpation. We used whole-cell and cell-attached recording and calcium imaging to characterize the neuronal responses to these stimuli. Consistent with previous literature, whole-cell recording revealed a sparse response to the standard range of velocities: although all recorded cells showed tuning to velocity in their postsynaptic potentials, only a small fraction produced stimulus-evoked spikes. In contrast, the sharp stimulus evoked reliable spiking in a large fraction of regular spiking neurons in the supragranular vS1. Spiking responses to the sharp stimulus were binary and precisely timed, with minimum trial-to-trial variability. Interestingly, we also observed that the sharp stimulus produced a consistent and significant reduction in action potential threshold. In the second step we asked whether the stimulus dependent sparse and dense activations we found in anesthetized condition would generalize to the awake condition. We employed cell-attached recordings in head-fixed awake mice to explore the degree of sparseness in awake cortex. Although, stimuli delivered by a piezo-electric actuator evoked significant response in a small fraction of regular spiking supragranular neurons (16%-29%), we observed that a majority of neurons (84%) were driven by manual probing of whiskers. Our results demonstrate that despite sparse activity, the majority of neurons in the superficial layers of vS1 contribute to coding by representing a specific feature of the tactile stimulus. Thesis outline: Chapter 1 provides a review of the current knowledge on sparse coding and an overview of the whisker-sensory pathway. Chapter 2 represents our published results regarding sparse and dense coding in vS1 of anesthetized mice (Ranjbar-Slamloo and Arabzadeh 2017). Chapter 3 represents our pending manuscript with results obtained with piezo and manual stimulation in awake mice. Finally, in Chapter 4 we discuss and conclude our findings in the context of the literature. The appendix provides unpublished results related to Chapter 2. This section is referenced in the final chapter for further discussion

Seeing sound: a new way to illustrate auditory objects and their neural correlates

This thesis develops a new method for time-frequency signal processing and examines the relevance of the new representation in studies of neural coding in songbirds. The method groups together associated regions of the time-frequency plane into objects defined by time-frequency contours. By combining information about structurally stable contour shapes over multiple time-scales and angles, a signal decomposition is produced that distributes resolution adaptively. As a result, distinct signal components are represented in their own most parsimonious forms.  Next, through neural recordings in singing birds, it was found that activity in song premotor cortex is significantly correlated with the objects defined by this new representation of sound. In this process, an automated way of finding sub-syllable acoustic transitions in birdsongs was first developed, and then increased spiking probability was found at the boundaries of these acoustic transitions. Finally, a new approach to study auditory cortical sequence processing more generally is proposed. In this approach, songbirds were trained to discriminate Morse-code-like sequences of clicks, and the neural correlates of this behavior were examined in primary and secondary auditory cortex. It was found that a distinct transformation of auditory responses to the sequences of clicks exists as information transferred from primary to secondary auditory areas. Neurons in secondary auditory areas respond asynchronously and selectively -- in a manner that depends on the temporal context of the click. This transformation from a temporal to a spatial representation of sound provides a possible basis for the songbird's natural ability to discriminate complex temporal sequences

Uncovering temporal structure in hippocampal output patterns.

Place cell activity of hippocampal pyramidal cells has been described as the cognitive substrate of spatial memory. Replay is observed during hippocampal sharp-wave-ripple-associated population burst events (PBEs) and is critical for consolidation and recall-guided behaviors. PBE activity has historically been analyzed as a phenomenon subordinate to the place code. Here, we use hidden Markov models to study PBEs observed in rats during exploration of both linear mazes and open fields. We demonstrate that estimated models are consistent with a spatial map of the environment, and can even decode animals' positions during behavior. Moreover, we demonstrate the model can be used to identify hippocampal replay without recourse to the place code, using only PBE model congruence. These results suggest that downstream regions may rely on PBEs to provide a substrate for memory. Additionally, by forming models independent of animal behavior, we lay the groundwork for studies of non-spatial memory

GPU-based implementation of real-time system for spiking neural networks

Real-time simulations of biological neural networks (BNNs) provide a natural platform for applications in a variety of fields: data classification and pattern recognition, prediction and estimation, signal processing, control and robotics, prosthetics, neurological and neuroscientific modeling. BNNs possess inherently parallel architecture and operate in continuous signal domain. Spiking neural networks (SNNs) are type of BNNs with reduced signal dynamic range: communication between neurons occurs by means of time-stamped events (spikes). SNNs allow reduction of algorithmic complexity and communication data size at a price of little loss in accuracy. Simulation of SNNs using traditional sequential computer architectures results in significant time penalty. This penalty prohibits application of SNNs in real-time systems. Graphical processing units (GPUs) are cost effective devices specifically designed to exploit parallel shared memory-based floating point operations applied not only to computer graphics, but also to scientific computations. This makes them an attractive solution for SNN simulation compared to that of FPGA, ASIC and cluster message passing computing systems. Successful implementations of GPU-based SNN simulations have been already reported. The contribution of this thesis is the development of a scalable GPU-based realtime system that provides initial framework for design and application of SNNs in various domains. The system delivers an interface that establishes communication with neurons in the network as well as visualizes the outcome produced by the network. Accuracy of the simulation is emphasized due to its importance in the systems that exploit spike time dependent plasticity, classical conditioning and learning. As a result, a small network of 3840 Izhikevich neurons implemented as a hybrid system with Parker-Sochacki numerical integration method achieves real time operation on GTX260 device. An application case study of the system modeling receptor layer of retina is reviewed

Neurons and circuits for odor processing in the piriform cortex

Increased understanding of the early stages of olfaction has lead to a renewed interest in the higher brain regions responsible for forming unified ‘odor images’ from the chemical components detected by the nose. The piriform cortex, which is one of the first cortical destinations of olfactory information in mammals, is a primitive paleocortex that is critical for the synthetic perception of odors. Here we review recent work that examines the cellular neurophysiology of the piriform cortex. Exciting new findings have revealed how the neurons and circuits of the piriform cortex process odor information, demonstrating that, despite its superficial simplicity, the piriform cortex is a remarkably subtle and intricate neural circuit

Population-level neural coding for higher cognition

Higher cognition encompasses advanced mental processes that enable complex thinking, decision-making, problem-solving, and abstract reasoning. These functions involve integrating information from multiple sensory modalities and organizing action plans based on the abstraction of past information. The neural activity underlying these functions is often complex, and the contribution of single neurons in supporting population-level representations of cognitive variables is not yet clear. In this thesis, I investigated the neural mechanisms underlying higher cognition in higher-order brain regions with single-neuron resolution in human and non-human primates performing working memory tasks. I aimed to understand how representations are arranged and how neurons contribute to the population code. In the first manuscript, I investigated the population-level neural coding for the maintenance of numbers in working memory within the parietal association cortex. By analyzing intra-operative intracranial micro-electrode array recording data, I uncovered distinct representations for numbers in both symbolic and nonsymbolic formats. In the second manuscript, I delved deeper into the neuronal organizing principles of population coding to address the ongoing debate surrounding memory maintenance mechanisms. I unveiled sparse structures in the neuronal implementation of representations and identified biologically meaningful components that can be directly communicated to downstream neurons. These components were linked to subpopulations of neurons with distinct physiological properties and temporal dynamics, enabling the active maintenance of working memory while resisting distraction. Lastly, using an artificial neural network model, I demonstrated that the sparse implementation of temporally modulated working memory representations is preferred in recurrently connected neural populations such as the prefrontal cortex. In summary, this thesis provides a comprehensive investigation of higher cognition in higher-order brain regions, focusing on working memory tasks involving numerical stimuli. By examining neural population coding and unveiling sparse structures in the neuronal implementation of representations, our findings contribute to a deeper understanding of the mechanisms underlying working memory and higher cognitive functions