Sound processing in the mouse auditory cortex: organization, modulation, and transformation

The auditory system begins with the cochlea, a frequency analyzer and signal amplifier with exquisite precision. As neural information travels towards higher brain regions, the encoding becomes less faithful to the sound waveform itself and more influenced by non-sensory factors such as top-down attentional modulation, local feedback modulation, and long-term changes caused by experience. At the level of auditory cortex (ACtx), such influences exhibit at multiple scales from single neurons to cortical columns to topographic maps, and are known to be linked with critical processes such as auditory perception, learning, and memory. How the ACtx integrates a wealth of diverse inputs while supporting adaptive and reliable sound representations is an important unsolved question in auditory neuroscience. This dissertation tackles this question using the mouse as an animal model. We begin by describing a detailed functional map of receptive fields within the mouse ACtx. Focusing on the frequency tuning properties, we demonstrated a robust tonotopic organization in the core ACtx fields (A1 and AAF) across cortical layers, neural signal types, and anesthetic states, confirming the columnar organization of basic sound processing in ACtx. We then studied the bottom-up input to ACtx columns by optogenetically activating the inferior colliculus (IC), and observed feedforward neuronal activity in the frequency-matched column, which also induced clear auditory percepts in behaving mice. Next, we used optogenetics to study layer 6 corticothalamic neurons (L6CT) that project heavily to the thalamus and upper layers of ACtx. We found that L6CT activation biases sound perception towards either enhanced detection or discrimination depending on its relative timing with respect to the sound, a process that may support dynamic filtering of auditory information. Finally, we optogenetically isolated cholinergic neurons in the basal forebrain (BF) that project to ACtx and studied their involvement in columnar ACtx plasticity during associative learning. In contrast to previous notions that BF just encodes reward and punishment, we observed clear auditory responses from the cholinergic neurons, which exhibited rapid learning-induced plasticity, suggesting that BF may provide a key instructive signal to drive adaptive plasticity in ACtx

Multi-objective evolutionary algorithms of spiking neural networks

Spiking neural network (SNN) is considered as the third generation of artificial neural networks. Although there are many models of SNN, Evolving Spiking Neural Network (ESNN) is widely used in many recent research works. Among the many important issues that need to be explored in ESNN are determining the optimal pre-synaptic neurons and parameters values for a given data set. Moreover, previous studies have not investigated the performance of the multi-objective approach with ESNN. In this study, the aim is to find the optimal pre-synaptic neurons and parameter values for ESNN simultaneously by proposing several integrations between ESNN and differential evolution (DE). The proposed algorithms applied to address these problems include DE with evolving spiking neural network (DE-ESNN) and DE for parameter tuning with evolving spiking neural network (DEPT-ESNN). This study also utilized the approach of multi-objective (MOO) with ESNN for better learning structure and classification accuracy. Harmony Search (HS) and memetic approach was used to improve the performance of MOO with ESNN. Consequently, Multi- Objective Differential Evolution with Evolving Spiking Neural Network (MODE-ESNN), Harmony Search Multi-Objective Differential Evolution with Evolving Spiking Neural Network (HSMODE-ESNN) and Memetic Harmony Search Multi-Objective Differential Evolution with Evolving Spiking Neural Network (MEHSMODE-ESNN) were applied to improve ESNN structure and accuracy rates. The hybrid methods were tested by using seven benchmark data sets from the machine learning repository. The performance was evaluated using different criteria such as accuracy (ACC), geometric mean (GM), sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and average site performance (ASP) using k-fold cross validation. Evaluation analysis shows that the proposed methods demonstrated better classification performance as compared to the standard ESNN especially in the case of imbalanced data sets. The findings revealed that the MEHSMODE-ESNN method statistically outperformed all the other methods using the different data sets and evaluation criteria. It is concluded that multi objective proposed methods have been evinced as the best proposed methods for most of the data sets used in this study. The findings have proven that the proposed algorithms attained the optimal presynaptic neurons and parameters values and MOO approach was applicable for the ESNN

Sound processing in the mouse auditory cortex: organization, modulation, and transformation

The auditory system begins with the cochlea, a frequency analyzer and signal amplifier with exquisite precision. As neural information travels towards higher brain regions, the encoding becomes less faithful to the sound waveform itself and more influenced by non-sensory factors such as top-down attentional modulation, local feedback modulation, and long-term changes caused by experience. At the level of auditory cortex (ACtx), such influences exhibit at multiple scales from single neurons to cortical columns to topographic maps, and are known to be linked with critical processes such as auditory perception, learning, and memory. How the ACtx integrates a wealth of diverse inputs while supporting adaptive and reliable sound representations is an important unsolved question in auditory neuroscience. This dissertation tackles this question using the mouse as an animal model. We begin by describing a detailed functional map of receptive fields within the mouse ACtx. Focusing on the frequency tuning properties, we demonstrated a robust tonotopic organization in the core ACtx fields (A1 and AAF) across cortical layers, neural signal types, and anesthetic states, confirming the columnar organization of basic sound processing in ACtx. We then studied the bottom-up input to ACtx columns by optogenetically activating the inferior colliculus (IC), and observed feedforward neuronal activity in the frequency-matched column, which also induced clear auditory percepts in behaving mice. Next, we used optogenetics to study layer 6 corticothalamic neurons (L6CT) that project heavily to the thalamus and upper layers of ACtx. We found that L6CT activation biases sound perception towards either enhanced detection or discrimination depending on its relative timing with respect to the sound, a process that may support dynamic filtering of auditory information. Finally, we optogenetically isolated cholinergic neurons in the basal forebrain (BF) that project to ACtx and studied their involvement in columnar ACtx plasticity during associative learning. In contrast to previous notions that BF just encodes reward and punishment, we observed clear auditory responses from the cholinergic neurons, which exhibited rapid learning-induced plasticity, suggesting that BF may provide a key instructive signal to drive adaptive plasticity in ACtx

Physiology, Psychoacoustics and Cognition in Normal and Impaired Hearing

​The International Symposium on Hearing is a prestigious, triennial gathering where world-class scientists present and discuss the most recent advances in the field of human and animal hearing research. The 2015 edition will particularly focus on integrative approaches linking physiological, psychophysical and cognitive aspects of normal and impaired hearing. Like previous editions, the proceedings will contain about 50 chapters ranging from basic to applied research, and of interest to neuroscientists, psychologists, audiologists, engineers, otolaryngologists, and artificial intelligence researchers.