scholarly journals Central auditory neurons display flexible feature recombination functions

2014 ◽  
Vol 111 (6) ◽  
pp. 1183-1189 ◽  
Author(s):  
Andrei S. Kozlov ◽  
Timothy Q. Gentner
Keyword(s):  
Auditory System ◽  
Neural Circuits ◽  
Neural Circuit ◽  
State Of The Art ◽  
Sensory Modality ◽  
Auditory Neurons ◽  
Natural Stimuli ◽  
Art Object ◽  
Divisive Normalization ◽  
Central Auditory Neurons

Recognition of natural stimuli requires a combination of selectivity and invariance. Classical neurobiological models achieve selectivity and invariance, respectively, by assigning to each cortical neuron either a computation equivalent to the logical “AND” or a computation equivalent to the logical “OR.” One powerful OR-like operation is the MAX function, which computes the maximum over input activities. The MAX function is frequently employed in computer vision to achieve invariance and considered a key operation in visual cortex. Here we explore the computations for selectivity and invariance in the auditory system of a songbird, using natural stimuli. We ask two related questions: does the MAX operation exist in auditory system? Is it implemented by specialized “MAX” neurons, as assumed in vision? By analyzing responses of individual neurons to combinations of stimuli we systematically sample the space of implemented feature recombination functions. Although we frequently observe the MAX function, we show that the same neurons that implement it also readily implement other operations, including the AND-like response. We then show that sensory adaptation, a ubiquitous property of neural circuits, causes transitions between these operations in individual neurons, violating the fixed neuron-to-computation mapping posited in the state-of-the-art object-recognition models. These transitions, however, accord with predictions of neural-circuit models incorporating divisive normalization and variable polynomial nonlinearities at the spike threshold. Because these biophysical properties are not tied to a particular sensory modality but are generic, the flexible neuron-to-computation mapping demonstrated in this study in the auditory system is likely a general property.

scholarly journals Central auditory neurons have composite receptive fields

2016 ◽  
Vol 113 (5) ◽  
pp. 1441-1446 ◽  
Author(s):  
Andrei S. Kozlov ◽  
Timothy Q. Gentner
Keyword(s):  
Neural Circuits ◽  
Receptive Fields ◽  
Learning Rule ◽  
Auditory Neurons ◽  
Olfactory Neurons ◽  
Unsupervised Neural Network ◽  
Divisive Normalization ◽  
Vocal Signals ◽  
Central Auditory Neurons ◽  
High Level

High-level neurons processing complex, behaviorally relevant signals are sensitive to conjunctions of features. Characterizing the receptive fields of such neurons is difficult with standard statistical tools, however, and the principles governing their organization remain poorly understood. Here, we demonstrate multiple distinct receptive-field features in individual high-level auditory neurons in a songbird, European starling, in response to natural vocal signals (songs). We then show that receptive fields with similar characteristics can be reproduced by an unsupervised neural network trained to represent starling songs with a single learning rule that enforces sparseness and divisive normalization. We conclude that central auditory neurons have composite receptive fields that can arise through a combination of sparseness and normalization in neural circuits. Our results, along with descriptions of random, discontinuous receptive fields in the central olfactory neurons in mammals and insects, suggest general principles of neural computation across sensory systems and animal classes.

Efferent Innervation to the Cochlea

2018 ◽  
pp. 58-94
Author(s):  
Ana Belén Elgoyhen ◽  
Carolina Wedemeyer ◽  
Mariano N. Di Guilmi
Keyword(s):  
Auditory System ◽  
Cochlear Nucleus ◽  
Medial Geniculate Body ◽  
Superior Olivary Complex ◽  
Auditory Neurons ◽  
Olivocochlear System ◽  
System P ◽  
Medial Geniculate ◽  
Central Auditory Neurons ◽  
The Brain

The auditory system consists of ascending and descending neuronal pathways. The best studied is the ascending pathway, whereby sounds that are transduced in the cochlea into electrical signals are sent to the brain via the auditory nerve. Before reaching the auditory cortex, auditory ascending information has several central relays: the cochlear nucleus and superior olivary complex in the brainstem, the lateral lemniscal nuclei and inferior colliculus in the midbrain, and the medial geniculate body in the thalamus. The function(s) of the descending corticofugal pathway is less well understood. It plays important roles in shaping or even creating the response properties of central auditory neurons and in the plasticity of the auditory system, such as reorganizing cochleotopic and computational maps. Corticofugal projections are present at different relays of the auditory system. This review focuses on the physiology and plasticity of the medial efferent olivocochlear system.

A State-of-the-Art Review

1977 ◽  
Vol 86 (6_suppl) ◽  
pp. 1-20 ◽  
Author(s):  
Juergen Tonndorf
Keyword(s):  
Auditory System ◽  
Functional State ◽  
Electronic Device ◽  
State Of The Art ◽  
Cochlear Nerve ◽  
Challenging Problem ◽  
Clinical Tests ◽  
Efficient Manner ◽  
Auditory Rehabilitation ◽  
Engineering Problems

Work on cochlear prostheses for the auditory rehabilitation of the profoundly deaf represents a challenging problem. Some early, but perhaps premature, surgical attempts have helped to bring the entire issue into focus. Systemic studies are now under way in many different places. Although the purely engineering problems as well as the surgical ones appear solvable at this time, the remaining unsolved problems lie in two areas: 1) the bioengineering interfacing, i.e., the search for methods needed to connect an engineering (electronic) device to the neural auditory system in an efficient manner; and 2) clinical tests for the assessment of the functional state of the cochlear nerve.

A Canonical Neural Circuit for Cortical Nonlinear Operations

2008 ◽  
Vol 20 (6) ◽  
pp. 1427-1451 ◽  
Author(s):  
Minjoon Kouh ◽  
Tomaso Poggio
Keyword(s):  
Neural Circuit ◽  
Neural Responses ◽  
Energy Models ◽  
Divisive Normalization ◽  
Primate Visual Cortex ◽  
Neurophysiological Data ◽  
Ventral Pathway ◽  
Response Profiles ◽  
Parameter Values ◽  
Good Agreement

A few distinct cortical operations have been postulated over the past few years, suggested by experimental data on nonlinear neural response across different areas in the cortex. Among these, the energy model proposes the summation of quadrature pairs following a squaring nonlinearity in order to explain phase invariance of complex V1 cells. The divisive normalization model assumes a gain-controlling, divisive inhibition to explain sigmoid-like response profiles within a pool of neurons. A gaussian-like operation hypothesizes a bell-shaped response tuned to a specific, optimal pattern of activation of the presynaptic inputs. A max-like operation assumes the selection and transmission of the most active response among a set of neural inputs. We propose that these distinct neural operations can be computed by the same canonical circuitry, involving divisive normalization and polynomial nonlinearities, for different parameter values within the circuit. Hence, this canonical circuit may provide a unifying framework for several circuit models, such as the divisive normalization and the energy models. As a case in point, we consider a feedforward hierarchical model of the ventral pathway of the primate visual cortex, which is built on a combination of the gaussian-like and max-like operations. We show that when the two operations are approximated by the circuit proposed here, the model is capable of generating selective and invariant neural responses and performing object recognition, in good agreement with neurophysiological data.

scholarly journals Oscillatory integration windows in neurons

2016 ◽  
Author(s):  
Nitin Gupta ◽  
Swikriti Saran Singh ◽  
Mark Stopfer
Keyword(s):  
Neural Circuits ◽  
Neural Circuit ◽  
Field Potential ◽  
Coincidence Detection ◽  
Uniform Structure ◽  
Detection Mechanism ◽  
Oscillation Frequencies ◽  
Local Field Potential Lfp ◽  
Oscillatory Cycle

AbstractOscillatory synchrony among neurons occurs in many species and brain areas, and has been proposed to help neural circuits process information. One hypothesis states that oscillatory input creates cyclic integration windows: specific times in each oscillatory cycle when postsynaptic neurons become especially responsive to inputs. With paired local field potential (LFP) and intracellular recordings and controlled stimulus manipulations we directly tested this idea in the locust olfactory system. We found that inputs arriving in Kenyon cells (KCs) sum most effectively in a preferred window of the oscillation cycle. With a computational model, we found that the non-uniform structure of noise in the membrane potential helps mediate this process. Further experiments performed in vivo demonstrated that integration windows can form in the absence of inhibition and at a broad range of oscillation frequencies. Our results reveal how a fundamental coincidence-detection mechanism in a neural circuit functions to decode temporally organized spiking.

scholarly journals Prominent Inhibitory Projections Guide Sensorimotor Computation: An Invertebrate Perspective

2019 ◽  
Author(s):  
Samantha Hughes ◽  
Tansu Celikel
Keyword(s):  
Motor Neurons ◽  
Neural Circuits ◽  
Neural Circuit ◽  
Sensory Information ◽  
Circuit Complexity ◽  
Inhibitory Circuits ◽  
Invertebrate Species ◽  
Gating Mechanism ◽  
Motor Actions ◽  
Sensorimotor Representations

From single-cell organisms to complex neural networks, all evolved to provide control solutions to generate context and goal-specific actions. Neural circuits performing sensorimotor computation to drive navigation employ inhibitory control as a gating mechanism, as they hierarchically transform (multi)sensory information into motor actions. Here, we focus on this literature to critically discuss the proposition that prominent inhibitory projections form sensorimotor circuits. After reviewing the neural circuits of navigation across various invertebrate species, we argue that with increased neural circuit complexity and the emergence of parallel computations inhibitory circuits acquire new functions. The contribution of inhibitory neurotransmission for navigation goes beyond shaping the communication that drives motor neurons, instead, include encoding of emergent sensorimotor representations. A mechanistic understanding of the neural circuits performing sensorimotor computations in invertebrates will unravel the minimum circuit requirements driving adaptive navigation.

scholarly journals Deep-Learning-Based Road Crack Detection Frameworks for Dashcam-captured Images under Different Illumination Conditions

2021 ◽  
Author(s):  
Da-Ren Chen ◽  
Wei-Min Chiu
Keyword(s):  
Object Detection ◽  
Large Scale ◽  
Crack Detection ◽  
State Of The Art ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
The Road ◽  
Art Object

Abstract Machine learning techniques have been used to increase detection accuracy of cracks in road surfaces. Most studies failed to consider variable illumination conditions on the target of interest (ToI), and only focus on detecting the presence or absence of road cracks. This paper proposes a new road crack detection method, IlumiCrack, which integrates Gaussian mixture models (GMM) and object detection CNN models. This work provides the following contributions: 1) For the first time, a large-scale road crack image dataset with a range of illumination conditions (e.g., day and night) is prepared using a dashcam. 2) Based on GMM, experimental evaluations on 2 to 4 levels of brightness are conducted for optimal classification. 3) the IlumiCrack framework is used to integrate state-of-the-art object detecting methods with CNN to classify the road crack images into eight types with high accuracy. Experimental results show that IlumiCrack outperforms the state-of-the-art R-CNN object detection frameworks.

scholarly journals Diagnosing state-of-the-art object proposal methods

2015 ◽  
Author(s):  
Hongyuan Zhu ◽  
Shijian Lu ◽  
Jianfei Cai ◽  
Guangqing Lee
Keyword(s):  
State Of The Art ◽  
Art Object ◽  
Object Proposal
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