Predictive coding and stochastic resonance as fundamental principles of auditory perception
University Hospital Erlangen（埃尔兰根大学医院）；University Erlangen-Nürnberg（埃尔兰根-纽伦堡大学）；Newcastle University（纽卡斯尔大学）；York University（约克大学）；University of California Irvine（加州大学欧文分校）；University College London（伦敦大学学院）
Achim Schilling, William Sedley, Richard Gerum, Claus Metzner, Konstantin Tziridis, Andreas Maier, Holger Schulze, Fan-Gang Zeng, Karl J. Friston, Patrick Krauss
备注：
arXiv admin note: substantial text overlap with arXiv:2010.01914
How is information processed in the brain during perception? Mechanistic insight is achieved only when experiments are employed to test formal or computational models. In analogy to lesion studies, phantom perception may serve as a vehicle to understand the fundamental processing principles underlying auditory perception. With a special focus on tinnitus -- as the prime example of auditory phantom perception -- we review recent work at the intersection of artificial intelligence, psychology, and neuroscience. In particular, we discuss why everyone with tinnitus suffers from hearing loss, but not everyone with hearing loss suffers from tinnitus. We argue that the increase of sensory precision due to Bayesian inference could be caused by intrinsic neural noise and lead to a prediction error in the cerebral cortex. Hence, two fundamental processing principles - being ubiquitous in the brain - provide the most explanatory power for the emergence of tinnitus: predictive coding as a top-down, and stochastic resonance as a complementary bottom-up mechanism. We conclude that both principles play a crucial role in healthy auditory perception.
University of Wherever（韦弗大学）；University of California, San Diego（加州大学圣迭戈分校）
Precortical neural systems encode information collected by the senses, but the driving principles of the encoding used have remained a subject of debate. We present a model of retinal coding that is based on three constraints: information preservation, minimization of the neural wiring, and response equalization. The resulting novel version of sparse principal components analysis successfully captures a number of known characteristics of the retinal coding system, such as center-surround receptive fields, color opponency channels, and spatiotemporal responses that correspond to magnocellular a…
Linear Non-Linear(LN) models are widely used to characterize the receptive fields of early-stage auditory processing. We apply the principle of efficient coding to the LN model of Spectro-Temporal Receptive Fields(STRFs) of the neurons in primary auditory cortex. The Efficient Coding Principle has been previously used to understand early visual receptive fields and linear STRFs in auditory processing. Efficient coding is realized by jointly optimizing the mutual information between stimuli and neural responses subjected to the metabolic cost of firing spikes. We compare the predictions of the …
Recent studies indicate that the neurons involved in a cognitive task aren't locally limited but span out to multiple human brain regions. We obtain network components and their locations for the task of listening to music. The recorded EEG data is modeled as a graph, and it is assumed that the overall activity is a contribution of several independent subnetworks. To identify these intrinsic cognitive subnetworks corresponding to music perception, we propose to decompose the whole brain graph-network into multiple subnetworks. We perform this decomposition to a group of brain networks by perfo…
Expectations can substantially influence perception. Predictive coding is a theory of sensory processing that aims to explain the neural mechanisms underlying the effect of expectations in sensory processing. Its main assumption is that sensory neurons encode prediction error with respect to expected sensory input. Neural populations encoding prediction error have been previously reported in the human auditory cortex (AC); however, most studies focused on the encoding of pure tones and induced expectations by stimulus repetition, potentially confounding prediction error with effects of neural …
We investigate the use of modularity as a quantifier of whole-brain functional networks. Brain networks are constructed from functional magnetic resonance imaging while subjects listened to auditory pieces that varied in emotivity and cultural familiarity. The results of our analysis reveal high and low modularity groups based on the network configuration during a subject's favorite song, and this classification can predict network reconfiguration during the other auditory pieces. In particular, subjects in the low modularity group show significant brain network reconfiguration during both fam…
Predictive coding is the leading algorithmic framework to understand how expectations shape our experience of reality. Its main tenet is that sensory neurons encode prediction error: the residuals between a generative model of the sensory world and the actual sensory input. However, it is yet unclear how this scheme generalises to the multi-level hierarchical architecture of sensory processing. Theoretical accounts of predictive coding agree that neurons computing prediction error and the generative model exist at all levels of the processing hierarchy. However, there is not a current consensu…
In order to gain a mechanistic understanding of how tinnitus emerges in the brain, we must build biologically plausible computational models that mimic both tinnitus development and perception, and test the tentative models with brain and behavioral experiments. With a special focus on tinnitus research, we review recent work at the intersection of artificial intelligence, psychology and neuroscience, indicating a new research agenda that follows the idea that experiments will yield theoretical insight only when employed to test brain-computational models. This view challenges the popular beli…
From Spikes to Speech: NeuroVoc -- A Biologically Plausible Vocoder Framework for Auditory Perception and Cochlear Implant Simulation
Leiden Institute of Advanced Computer Science（莱顿高级计算机科学研究所）；Department of Otorhinolaryngology（耳鼻喉科部门）；Leiden Institute for Brain and Cognition（莱顿脑认知研究所）；Bioelectronics group, EEMCS（生物电子组，EEMCS）
Pitch is the perceptual correlate of sound's periodicity and a fundamental property of the auditory sensation. The interaction of two or more pitches gives rise to a sensation that can be characterized by its degree of consonance or dissonance. In the current study, we investigated the neuromagnetic representations of consonant and dissonant musical dyads using a new model of cortical activity, in an effort to assess the possible involvement of pitch-specific neural mechanisms in consonance processing at early cortical stages. In the first step of the study, we developed a novel model of corti…
The subcortical sensory pathways are the fundamental channels for mapping the outside world to our minds. Sensory pathways efficiently transmit information by adapting neural responses to the local statistics of the sensory input. The longstanding mechanistic explanation for this adaptive behaviour is that neuronal habituation scales activity to the local statistics of the stimuli. An alternative account is that neural coding is directly driven by expectations of the sensory input. Here we used abstract rules to manipulate expectations independently of local stimulus statistics. The ultra-high…
Massachusetts Institute of Technology（麻省理工学院）；Harvard University（哈佛大学）；MIT-IBM Watson AI Lab（MIT-IBM沃森人工智能实验室）
Adversarial examples are often cited by neuroscientists and machine learning researchers as an example of how computational models diverge from biological sensory systems. Recent work has proposed adding biologically-inspired components to visual neural networks as a way to improve their adversarial robustness. One surprisingly effective component for reducing adversarial vulnerability is response stochasticity, like that exhibited by biological neurons. Here, using recently developed geometrical techniques from computational neuroscience, we investigate how adversarial perturbations influence…
Detecting absence: A dedicated prediction-error signal emerging in the auditory thalamus
Basque Center on Cognition, Brain and Language（巴斯克认知、大脑与语言中心）；Ikerbasque, Basque Foundation for Science（伊克尔巴斯克科学基金会）；Max Planck Institute for Human Cognitive and Brain Sciences（马克斯·普朗克人类认知与脑科学研究所）；Institut für Psychologie I, Universität zu Lübeck（吕贝克大学心理学第一研究所）；Chair of Cognitive and Clinical Neuroscience, Technische Universität Dresden（德累斯顿大学认知与临床神经科学教席）；Ruhr-Universität Bochum（波鸿鲁尔大学）
University of Sussex（萨塞克斯大学）；VERSES Research Lab（VERSES研究实验室）；University of Oxford（牛津大学）
Predictive coding is an influential model of cortical neural activity. It proposes that perceptual beliefs are furnished by sequentially minimising "prediction errors" - the differences between predicted and observed data. Implicit in this proposal is the idea that perception requires multiple cycles of neural activity. This is at odds with evidence that several aspects of visual perception - including complex forms of object recognition - arise from an initial "feedforward sweep" that occurs on fast timescales which preclude substantial recurrent activity. Here, we propose that the feedforwar…
This paper attempts to address the question that whether the present physical or mathematical theories are sufficient to understand the complexities of human brain when it interacts with the external environment in the form of an auditory stimulus.There have been efforts reporting that the introduction of ambiguity in visual stimuli causes effects which can't be explained classically.In this paper,it is investigated whether ambiguity in auditory stimuli can introduce any non-classical effects in human brain.Simple experiments were performed on normal subjects where they listened to an ambiguou…
Modern feedforward convolutional neural networks (CNNs) can now solve some computer vision tasks at super-human levels. However, these networks only roughly mimic human visual perception. One difference from human vision is that they do not appear to perceive illusory contours (e.g. Kanizsa squares) in the same way humans do. Physiological evidence from visual cortex suggests that the perception of illusory contours could involve feedback connections. Would recurrent feedback neural networks perceive illusory contours like humans? In this work we equip a deep feedforward convolutional network …
The diversity of cognitive deficits and neuropathological processes associated with dementias has encouraged divergence in pathophysiological explanations of disease. Here, we review an alternative framework that emphasises convergent critical features of pathophysiology, rather than the loss of memory centres or language centres, or singular neurotransmitter systems. Cognitive deficits are interpreted in the light of advances in normative accounts of brain function, based on predictive coding in hierarchical neural networks. The predicting coding rests on Bayesian integration of beliefs and s…
The mammalian brain is a metabolically expensive device, and evolutionary pressures have presumably driven it to make productive use of its resources. For sensory areas, this concept has been expressed more formally as an optimality principle: the brain maximizes the information that is encoded about relevant sensory variables, given available resources. Here, we develop this efficiency principle for encoding a sensory variable with a heterogeneous population of noisy neurons, each responding to a particular range of values. The accuracy with which the population represents any particular valu…
