How does the brain extract acoustic patterns? A behavioural and neural study

In complex auditory scenes the brain exploits statistical regularities to group sound elements into streams. Previous studies using tones that transition from being randomly drawn to regularly repeating, have highlighted a network of brain regions involved during this process of regularity detection, including auditory cortex (AC) and hippocampus (HPC; Barascud et al., 2016). In this thesis, I seek to understand how the neurons within AC and HPC detect and maintain a representation of deterministic acoustic regularity. I trained ferrets (n = 6) on a GO/NO-GO task to detect the transition from a random sequence of tones to a repeating pattern of tones, with increasing pattern lengths (3, 5 and 7). All animals performed significantly above chance, with longer reaction times and declining performance as the pattern length increased. During performance of the behavioural task, or passive listening, I recorded from primary and secondary fields of AC with multi-electrode arrays (behaving: n = 3), or AC and HPC using Neuropixels probes (behaving: n = 1; passive: n = 1). In the local field potential, I identified no differences in the evoked response between presentations of random or regular sequences. Instead, I observed significant increases in oscillatory power at the rate of the repeating pattern, and decreases at the tone presentation rate, during regularity. Neurons in AC, across the population, showed higher firing with more repetitions of the pattern and for shorter pattern lengths. Single-units within AC showed higher precision in their firing when responding to their best frequency during regularity. Neurons in AC and HPC both entrained to the pattern rate during presentation of the regular sequence when compared to the random sequence. Lastly, development of an optogenetic approach to inactivate AC in the ferret paves the way for future work to probe the causal involvement of these brain regions

The Kuramoto model: A simple paradigm for synchronization phenomena

Synchronization phenomena in large populations of interacting elements are the subject of intense research efforts in physical, biological, chemical, and social systems. A successful approach to the problem of synchronization consists of modeling each member of the population as a phase oscillator. In this review, synchronization is analyzed in one of the most representative models of coupled phase oscillators, the Kuramoto model. A rigorous mathematical treatment, specific numerical methods, and many variations and extensions of the original model that have appeared in the last few years are presented. Relevant applications of the model in different contexts are also included

Understanding the nervous system as an information processing machine: dense, nonspecific, canonical microcircuit architecture of inhibition in neocortex . . .

This thesis is the combination of two separate lines of work linked by one common goal: understanding the nervous system as an information-processing machine. David Marr (1982) put forth the idea that in order to fully understand an information-processing machine one must understand it at three separate levels. The computational goal of the system must be understood separately from the algorithm by which it is computed and the hardware in which it is computed. During my time as a graduate student I have been fortunate enough to work on two different levels in two very different systems. Chapter 1 focuses on the hardware of neural circuitry, specifically on how inhibitory interneurons connect to excitatory neurons. Chapter 2 focuses on the algorithmic problem of how flies could use gyroscopic sensors to calculate angular velocity

Physics-guided Machine Learning for Small Data Sets

In order to avoid costly machine breakdowns, proactive schedules are often put in place to substitute wear parts regularly. Currently, the contrary approach of Predictive Maintenance is receiving a lot of attention, as it promises needs-based maintenance. Currently, successful implementations are mainly found in highly standardized industries with a vast history of failure data. These conditions are not fulfilled for custom-built machines, namely here bottling machines. This thesis proposes an approach of combining machine learning with physical knowledge to compensate for missing error data. The approach is applied to bottle transport error cases in filling machines. First, a physical intuition for the machine and the possible error cases is obtained by creating an analytical physical model, avoiding the need for extensive numerical simulations. Second, errors are detected via one-shot semi-supervised anomaly detection, guided by the physical intuition to narrow down suitable algorithms. The one-shot setup involves a particularly short training phase, with only a single healthy sample. The results of the scoring process are anomaly probabilities that are calculated by comparing new samples with the training sample. Samples with high anomaly probabilities continue into the third step, the classification. The anomalous patterns are compared to error sketches, which are drawn by domain experts and enriched by physical knowledge. This approach has so far not been reported in literature. This thesis demonstrates that this strategy can pave the way to Predictive Maintenance for custom-built machines. It creates reliable results and allows transfer learning to similar machines naturally. It also allows feedback to domain experts in order to improve the machine construction

29th Annual Computational Neuroscience Meeting: CNS*2020

Meeting abstracts This publication was funded by OCNS. The Supplement Editors declare that they have no competing interests. Virtual | 18-22 July 202

Optimising the NAOMI adaptive optics real-time control system

This thesis describes the author's research in the field of Real-Time Control (RTC) for Adaptive Optics (AO) instrumentation. The research encompasses experiences and knowledge gained working in the area of RTC on astronomical instrumentation projects whilst at the Optical Science Laboratories (OSL), University College London (UCL), the Isaac Newton Groups of Telescopes (ING) and the Centre for Advanced Instrumentation (СfAI), Durham University. It begins by providing an extensive introduction to the field of Astronomical Adaptive Optics covering Image Correction Theory, Atmospheric Theory, Control Theory and Adaptive Optics Component Theory. The following chapter contains a review of the current state of world wide AO instruments and facilities. The Nasmyth Adaptive Optics Multi-purpose Instrument (NAOMI), the common user AO facility at the 4.2 William Herschel Telescope (WHT), is subsequently described. Results of NAOMI component characterisation experiments are detailed to provide a system understanding of the improvement optimisation could offer. The final chapter investigates how upgrading the RTCS could increase NAOMI'S spatial and temporal performance and examines the RTCS in the context of Extremely Large Telescope (ELT) class telescopes