scholarly journals Mathematical framework for place coding in the auditory system

2021 ◽  
Vol 17 (8) ◽  
pp. e1009251
Author(s):  
Alex D. Reyes
Keyword(s):  
Auditory System ◽  
Temporal Coding ◽  
Mathematical Framework ◽  
Complex Sounds ◽  
Coding Schemes ◽  
The Neural Network ◽  
Intensity Information ◽  
Network Simulations ◽  
Algebraic Operations ◽  
Acoustic Space

In the auditory system, tonotopy is postulated to be the substrate for a place code, where sound frequency is encoded by the location of the neurons that fire during the stimulus. Though conceptually simple, the computations that allow for the representation of intensity and complex sounds are poorly understood. Here, a mathematical framework is developed in order to define clearly the conditions that support a place code. To accommodate both frequency and intensity information, the neural network is described as a space with elements that represent individual neurons and clusters of neurons. A mapping is then constructed from acoustic space to neural space so that frequency and intensity are encoded, respectively, by the location and size of the clusters. Algebraic operations -addition and multiplication- are derived to elucidate the rules for representing, assembling, and modulating multi-frequency sound in networks. The resulting outcomes of these operations are consistent with network simulations as well as with electrophysiological and psychophysical data. The analyses show how both frequency and intensity can be encoded with a purely place code, without the need for rate or temporal coding schemes. The algebraic operations are used to describe loudness summation and suggest a mechanism for the critical band. The mathematical approach complements experimental and computational approaches and provides a foundation for interpreting data and constructing models.

scholarly journals Mathematical framework for place coding in the auditory system

2020 ◽  
Author(s):  
Alex D. Reyes
Keyword(s):  
Auditory System ◽  
Sensory Information ◽  
Network Models ◽  
Temporal Coding ◽  
Mathematical Framework ◽  
Mathematical Approach ◽  
Tone Frequency ◽  
Complex Sounds ◽  
Coding Schemes ◽  
Acoustic Space

AbstractIn the auditory system, tonotopy is postulated to be the substrate for a place code, where sound frequency is encoded by the location of the neurons that fire during the stimulus. Though conceptually simple, the computations that allow for the representation of intensity and complex sounds are poorly understood. Here, a mathematical framework is developed in order to define clearly the conditions that support a place code. To accommodate both frequency and intensity information, the neural network is described as a topological space with elements that represent individual neurons and clusters of neurons. A bijective mapping is then constructed from acoustic space to neural space so that frequency and intensity are encoded, respectively, by the location and size of the clusters. Algebraic operations -addition and multiplication- are derived to elucidate the rules for representing, assembling, and modulating multi-frequency sound in networks. The predicted outcomes of these operations are consistent with network simulations as well as with electrophysiological and psychophysical data. The analyses show that acoustic information can be encoded with a purely place code, without the need for rate or temporal coding schemes. The mathematical approach complements experimental and computational approaches and provides a foundation for interpreting data and constructing models.Author SummaryOne way of encoding sensory information in the brain is with a so-called place code. In the auditory system, tones of increasing frequencies activate sets of neurons at progressively different locations along an axis. The goal of this study is to elucidate the mathematical principles for representing tone frequency and intensity in neural networks. The rigorous, formal process ensures that the conditions for a place code and the associated computations are defined precisely. This mathematical approach offers new insights into experimental data and a framework for constructing network models.

Coding of Complex Sounds in the Auditory System

1986 ◽  
Vol 7 (01) ◽  
pp. 65-84
Author(s):  
Susan Shore
Keyword(s):  
Auditory System ◽  
Complex Sounds

Auditory processing of complex sounds: an overview

1992 ◽  
Vol 336 (1278) ◽  
pp. 295-306 ◽  
Keyword(s):  
Auditory System ◽  
Auditory Processing ◽  
Dynamic Range ◽  
Auditory Brainstem ◽  
Frequency Selectivity ◽  
Dorsal Cochlear Nucleus ◽  
Cochlear Nerve ◽  
Complex Sounds ◽  
Wide Range ◽  
Time Coding

The past 30 years has seen a remarkable development in our understanding of how the auditory system - particularly the peripheral system - processes complex sounds. Perhaps the most significant has been our understanding of the mechanisms underlying auditory frequency selectivity and their importance for normal and impaired auditory processing. Physiologically vulnerable cochlear filtering can account for many aspects of our normal and impaired psychophysical frequency selectivity with important consequences for the perception of complex sounds. For normal hearing, remarkable mechanisms in the organ of Corti, involving enhancement of mechanical tuning (in mammals probably by feedback of electro-mechanically generated energy from the hair cells), produce exquisite tuning, reflected in the tuning properties of cochlear nerve fibres. Recent comparisons of physiological (cochlear nerve) and psychophysical frequency selectivity in the same species indicate that the ear’s overall frequency selectivity can be accounted for by this cochlear filtering, at least in band width terms. Because this cochlear filtering is physiologically vulnerable, it deteriorates in deleterious conditions of the cochlea - hypoxia, disease, drugs, noise overexposure, mechanical disturbance - and is reflected in impaired psychophysical frequency selectivity. This is a fundamental feature of sensorineural hearing loss of cochlear origin, and is of diagnostic value. This cochlear filtering, particularly as reflected in the temporal patterns of cochlear fibres to complex sounds, is remarkably robust over a wide range of stimulus levels. Furthermore, cochlear filtering properties are a prime determinant of the ‘place’ and ‘time’ coding of frequency at the cochlear nerve level, both of which appear to be involved in pitch perception. The problem of how the place and time coding of complex sounds is effected over the ear’s remarkably wide dynamic range is briefly addressed. In the auditory brainstem, particularly the dorsal cochlear nucleus, are inhibitory mechanisms responsible for enhancing the spectral and temporal contrasts in complex sounds. These mechanisms are now being dissected neuropharmacologically. At the cortical level, mechanisms are evident that are capable of abstracting biologically relevant features of complex sounds. Fundamental studies of how the auditory system encodes and processes complex sounds are vital to promising recent applications in the diagnosis and rehabilitation of the hearing impaired.

Research on Underwater Polarization Image Segmentation Inspired by Biological Optic Nerve

2013 ◽  
Vol 347-350 ◽  
pp. 2178-2184
Author(s):  
Hui Bin Wang ◽  
Yu Rong Wu ◽  
Jie Shen ◽  
Zhe Chen
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Network Model ◽  
Neural Network Model ◽  
Underwater Image ◽  
The Neural Network ◽  
Intensity Information ◽  
Segmentation Methods ◽  
Polarization Image ◽  
Feedback Neural Network

Due to effects of the light by water and other particles, the quality of underwater image will degrade. The traditional underwater image segmentation methods based on intensity and spectrum have difficulty in determining boundary. Inspired by the visual system of mantis shrimps, this paper constructed a feedback neural network model, in which the parameters were optimized using machine learning method. Based on this model, we combine the polarization and intensity information to achieve the underwater polarization image segmentation. The results of experiment prove that the neural network model designed in this paper can improve the accuracy of underwater image segmentation.

scholarly journals GABAA receptors contribute more to rate than temporal coding in the IC of awake mice

2020 ◽  
Vol 123 (1) ◽  
pp. 134-148
Author(s):  
Boris Gourévitch ◽  
Elena J. Mahrt ◽  
Warren Bakay ◽  
Cameron Elde ◽  
Christine V. Portfors
Keyword(s):  
Firing Rate ◽  
Neuronal Response ◽  
Signal Transmission ◽  
Brain Structures ◽  
Temporal Coding ◽  
Model Organisms ◽  
Gabaergic Inhibition ◽  
Response Curves ◽  
Daily Lives ◽  
Complex Sounds

Speech is our most important form of communication, yet we have a poor understanding of how communication sounds are processed by the brain. Mice make great model organisms to study neural processing of communication sounds because of their rich repertoire of social vocalizations and because they have brain structures analogous to humans, such as the auditory midbrain nucleus inferior colliculus (IC). Although the combined roles of GABAergic and glycinergic inhibition on vocalization selectivity in the IC have been studied to a limited degree, the discrete contributions of GABAergic inhibition have only rarely been examined. In this study, we examined how GABAergic inhibition contributes to shaping responses to pure tones as well as selectivity to complex sounds in the IC of awake mice. In our set of long-latency neurons, we found that GABAergic inhibition extends the evoked firing rate range of IC neurons by lowering the baseline firing rate but maintaining the highest probability of firing rate. GABAergic inhibition also prevented IC neurons from bursting in a spontaneous state. Finally, we found that although GABAergic inhibition shaped the spectrotemporal response to vocalizations in a nonlinear fashion, it did not affect the neural code needed to discriminate vocalizations, based either on spiking patterns or on firing rate. Overall, our results emphasize that even if GABAergic inhibition generally decreases the firing rate, it does so while maintaining or extending the abilities of neurons in the IC to code the wide variety of sounds that mammals are exposed to in their daily lives. NEW & NOTEWORTHY GABAergic inhibition adds nonlinearity to neuronal response curves. This increases the neuronal range of evoked firing rate by reducing baseline firing. GABAergic inhibition prevents bursting responses from neurons in a spontaneous state, reducing noise in the temporal coding of the neuron. This could result in improved signal transmission to the cortex.

scholarly journals A Late Critical Period for Frequency Modulated Sweeps in the Mouse Auditory System

2019 ◽  
Vol 30 (4) ◽  
pp. 2586-2599 ◽  
Author(s):  
Stitipragyan Bhumika ◽  
Mari Nakamura ◽  
Patricia Valerio ◽  
Magdalena Solyga ◽  
Henrik Lindén ◽  
...  
Keyword(s):  
Auditory Cortex ◽  
Auditory System ◽  
Critical Period ◽  
Noise Exposure ◽  
Functional Organization ◽  
Time Windows ◽  
Immunohistochemical Analysis ◽  
Critical Periods ◽  
Complex Sounds

Abstract Neuronal circuits are shaped by experience during time windows of increased plasticity in postnatal development. In the auditory system, the critical period for the simplest sounds—pure frequency tones—is well defined. Critical periods for more complex sounds remain to be elucidated. We used in vivo electrophysiological recordings in the mouse auditory cortex to demonstrate that passive exposure to frequency modulated sweeps (FMS) from postnatal day 31 to 38 leads to long-term changes in the temporal representation of sweep directions. Immunohistochemical analysis revealed a decreased percentage of layer 4 parvalbumin-positive (PV+) cells during this critical period, paralleled with a transient increase in responses to FMS, but not to pure tones. Preventing the PV+ cell decrease with continuous white noise exposure delayed the critical period onset, suggesting a reduction in inhibition as a mechanism for this plasticity. Our findings shed new light on the dependence of plastic windows on stimulus complexity that persistently sculpt the functional organization of the auditory cortex.

scholarly journals Basal tree complexity shapes functional pathways in the prefrontal cortex

2017 ◽  
Vol 118 (4) ◽  
pp. 1970-1983 ◽  
Author(s):  
Athanasia Papoutsi ◽  
George Kastellakis ◽  
Panayiota Poirazi
Keyword(s):  
Prefrontal Cortex ◽  
Pyramidal Neurons ◽  
Morphological Diversity ◽  
Temporal Coding ◽  
Coding Schemes ◽  
Functional Types ◽  
Tree Morphology ◽  
Wide Range ◽  
Ionic Mechanisms ◽  
Functional Pathways

While the morphology of basal dendritic trees in cortical pyramidal neurons varies, the functional implications of this diversity are just starting to emerge. In layer 5 pyramidal neurons of the prefrontal cortex, for example, increased basal tree complexity determines the recruitment of these neurons into functional circuits. Here, we use a modeling approach to investigate whether and how the morphology of the basal tree mediates the functional output of neurons. We implemented 57 basal tree morphologies of layer 5 prefrontal pyramidal neurons of the rat and identified morphological types that were characterized by different response features, forming distinct functional types. These types were robust to a wide range of manipulations (distribution of active ionic mechanisms, NMDA conductance, somatic and apical tree morphology, or the number of activated synapses) and supported different temporal coding schemes at both the single neuron and the microcircuit level. We predict that the basal tree morphological diversity among neurons of the same class mediates their segregation into distinct functional pathways. Extension of our approach/findings to other cortical areas and/or layers or under pathological conditions may provide a generalized role of the basal trees for neuronal function. NEW & NOTEWORTHY Our results suggest that the segregation of neurons to different functional types based on their basal tree morphology is in large part independent of the distribution of active ionic mechanisms, NMDA conductance, somatic and apical tree morphology, and the number of activated synapses; different functional types support distinct temporal coding schemes. This can be exploited to create networks with diverse coding characteristics, thus contributing to the functional heterogeneity within the same layer and area.

scholarly journals Automatic substantia nigra segmentation in neuromelanin-sensitive MRI by deep neural network in patients with prodromal and manifest synucleinopathy

2019 ◽  
pp. S453-S458
Author(s):  
R. Krupička ◽  
S. Mareček ◽  
C. Malá ◽  
M. Lang ◽  
O. Klempíř ◽  
...  
Keyword(s):  
Neural Network ◽  
Substantia Nigra ◽  
Signal Intensity ◽  
Substantia Nigra Pars Compacta ◽  
The Neural Network ◽  
Intensity Information ◽  
Ideal Tool ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

Neuromelanin (NM) is a black pigment located in the brain in substantia nigra pars compacta (SN) and locus coeruleus. Its loss is directly connected to the loss of nerve cells in this part of the brain, which plays a role in Parkinson’s Disease. Magnetic resonance imaging (MRI) is an ideal tool to monitor the amount of NM in the brain in vivo. The aim of the study was the development of tools and methodology for the quantification of NM in a special neuromelanin-sensitive MRI images. The first approach was done by creating regions of interest, corresponding to the anatomical position of SN based on an anatomical atlas and determining signal intensity threshold. By linking the anatomical and signal intensity information, we were able to segment the SN. As a second approach, the neural network U-Net was used for the segmentation of SN. Subsequently, the volume characterizing the amount of NM in the SN region was calculated. To verify the method and the assumptions, data available from various patient groups were correlated. The main benefit of this approach is the observer-independency of quantification and facilitation of the image processing process and subsequent quantification compared to the manual approach. It is ideal for automatic processing many image sets in one batch.

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