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A 3D Convolutional neural Network to Model Retinal Ganglion Cell's Responses to Light Patterns in Mice

INTERNATIONAL JOURNAL OF neural SYSTEMS 2018年第10期28卷 1850043-1850043页

作者： Lozano, Antonio Soto-Sanchez, Cristina Garrigos, Javier Javier Martinez, J. Manuel Ferrandez, J. Fernandez, Eduardo Univ Politecn Cartagena Dept Elect Tecnol Comp & Proyectos Cartagena Spain Univ Miguel Hernandez Inst Bioingn Alicante Spain CIBER BBN Madrid Spain

Deep Learning offers flexible powerful tools that have advanced our understanding of the neural coding of neurosensory systems. In this work, a 3D Convolutional neural Network (3D CNN) is used to mimic the behavior of a population of mice retinal ganglion cells in response to different light patterns. For this purpose, we projected homogeneous RGB flashes and checkerboards stimuli with variable luminances and wavelength spectrum to mimic a more naturalistic stimuli environment onto the mouse retina. We also used white moving bars in order to localize the spatial position of the recorded cells. Then recorded spikes were smoothed with a Gaussian kernel and used as the output target when training a 3D CNN in a supervised way. To find a suitable model, two hyperparameter search stages were performed. In the first stage, a trial and error process allowed us to obtain a system that is able to fit the neurons firing rates. In the second stage, a systematic procedure was used to compare several gradient-based optimizers, loss functions and the model's convolutional layers number. We found that a three layered 3D CNN was able to predict the ganglion cells firing rates with high correlations and low prediction error, as measured with Mean Squared Error and Dynamic Time Warping in test sets. These models were either competitive or outperformed other models used already in neuroscience, as Feed Forward neural Networks and Linear-Nonlinear models. This methodology allowed us to capture the temporal dynamic response patterns in a robust way, even for neurons with high trial-to-trial variable spontaneous firing rates, when providing the peristimulus time histogram as an output to our model.

关键词： Retina modeling neural coding deep learning convolutional neural networks

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Modeling the generation of output by the cerebellar nuclei

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neural NETWORKS 2013年 47卷 112-119页

作者： Steuber, Volker Jaeger, Dieter Univ Hertfordshire Sci & Technol Res Inst Hatfield AL10 9AB Herts England Emory Univ Dept Biol Atlanta GA 30030 USA

Functional aspects of network integration in the cerebellar cortex have been studied experimentally and modeled in much detail ever since the early work by theoreticians such as Man, Albus and Braitenberg more than 40 years ago. In contrast, much less is known about cerebellar processing at the output stage, namely in the cerebellar nuclei (CN). Here, input from Purkinje cells converges to control CN neuron spiking via GABAergic inhibition, before the output from the CN reaches cerebellar targets such as the brainstem and the motor thalamus. In this article we review modeling studies that address how the CN may integrate cerebellar cortical inputs, and what kind of signals may be transmitted. Specific hypotheses in the literature contrast rate coding and temporal coding of information in the spiking output from the CN. One popular hypothesis states that post-inhibitory rebound spiking may be an important mechanism by which Purkinje cell inhibition is turned into CN output spiking, but this hypothesis remains controversial. Rate coding clearly does take place, but in what way it may be augmented by temporal codes remains to be more clearly established. Several candidate mechanisms distinct from rebound spiking are discussed, such as the significance of spike time correlations between Purkinje cell pools to determine CN spike timing, irregularity of Purkinje cell spiking as a determinant of CN firing rate, and shared brief pauses between Purkinje cell pools that may trigger individual CN spikes precisely. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Purkinje cell neural coding STD Rebound Compartmental model Dynamic clamp

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A stimulus-dependent spike threshold is an optimal neural coder

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2015年第JUNE期9卷 61页

作者： Jones, Douglas L. Johnson, Erik C. Ratnam, Rama Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA Univ Illinois Beckman Inst Adv Sci & Technol Urbana IL 61801 USA Univ Illinois Coordinated Sci Lab Urbana IL 61801 USA Illinois Singapore Pte Ltd Adv Digital Sci Ctr Singapore Singapore

A neural code based on sequences of spikes can consume a significant portion of the brain's energy budget. Thus, energy considerations would dictate that spiking activity be kept as low as possible. However, a high spike-rate improves the coding and representation of signals in spike trains, particularly in sensory systems. These are competing demands, and selective pressure has presumably worked to optimize coding by apportioning a minimum number of spikes so as to maximize coding fidelity. The mechanisms by which a neuron generates spikes while maintaining a fidelity criterion are not known. Here, we show that a signal-dependent neural threshold, similar to a dynamic or adapting threshold, optimizes the trade-off between spike generation (encoding) and fidelity (decoding). The threshold mimics a post-synaptic membrane (a low-pass filter) and serves as an internal decoder. Further, it sets the average firing rate (the energy constraint). The decoding process provides an internal copy of the coding error to the spike-generator which emits a spike when the error equals or exceeds a spike threshold. When optimized, the trade-off leads to a deterministic spike firing-rule that generates optimally timed spikes so as to maximize fidelity. The optimal coder is derived in closed-form in the limit of high spike-rates, when the signal can be approximated as a piece-wise constant signal. The predicted spike-times are close to those obtained experimentally in the primary electrosensory afferent neurons of weakly electric fish (Apteronotus leptorhynchus) and pyramidal neurons from the somatosensory cortex of the rat. We suggest that KCNQ/Kv7 channels (underlying the M-current) are good candidates for the decoder. They are widely coupled to metabolic processes and do not inactivate. We conclude that the neural threshold is optimized to generate an energy-efficient and high-fidelity neural code.

关键词： neural coding coding fidelity energy-efficient coding dynamic threshold spike-timing source coding spike-threshold

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Seeing via miniature eye movements: a dynamic hypothesis for vision

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2012年第2012期6卷 89-89页

作者： Ahissar, Ehud Arieli, Amos Weizmann Inst Sci Dept Neurobiol IL-76100 Rehovot Israel

During natural viewing, the eyes are never still. Even during fixation, miniature movements of the eyes move the retinal image across tens of foveal photoreceptors. Most theories of vision implicitly assume that the visual system ignores these movements and somehow overcomes the resulting smearing. However, evidence has accumulated to indicate that fixational eye movements cannot be ignored by the visual system if fine spatial details are to be resolved. We argue that the only way the visual system can achieve its high resolution given its fixational movements is by seeing via these movements. Seeing via eye movements also eliminates the instability of the image, which would be induced by them otherwise. Here we present a hypothesis for vision, in which coarse details are spatially encoded in gaze-related coordinates, and fine spatial details are temporally encoded in relative retinal coordinates. The temporal encoding presented here achieves its highest resolution by encoding along the elongated axes of simple-cell receptive fields and not across these axes as suggested by spatial models of vision. According to our hypothesis, fine details of shape are encoded by interreceptor temporal phases, texture by instantaneous intra-burst rates of individual receptors, and motion by inter-burst temporal frequencies. We further describe the ability of the visual system to readout the encoded information and recode it internally. We show how readingout of retinal signals can be facilitated by neuronalp haselocked loops( NPLLs), which lock to the retinal jitter;this locking enables recoding of motion information and temporal framing of shape and texture processing. A possible implementation of this locking-and-recoding process by specific thalamocortical loops is suggested. Overall it is suggested that high-acuity vision is based primarily on temporal mechanisms of the sort presented here and low-acuity vision is based primarily on spatial mechanisms.

关键词： active vision fixational eye movements neuronal phase-locked loop temporal coding neural coding simple cells thalamocortical loop feedback

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Speech Enhancement for Listeners With Hearing Loss Based on a Model for Vowel coding in the Auditory Midbrain

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2014年第7期61卷 2081-2091页

作者： Rao, Akshay Carney, Laurel H. Univ Rochester Dept Elect & Comp Engn Rochester NY 14627 USA

A novel signal-processing strategy is proposed to enhance speech for listeners with hearing loss. The strategy focuses on improving vowel perception based on a recent hypothesis for vowel coding in the auditory system. Traditionally, studies of neural vowel encoding have focused on the representation of formants (peaks in vowel spectra) in the discharge patterns of the population of auditory-nerve (AN) fibers. A recent hypothesis focuses instead on vowel encoding in the auditory midbrain, and suggests a robust representation of formants. AN fiber discharge rates are characterized by pitch-related fluctuations having frequency-dependent modulation depths. Fibers tuned to frequencies near formants exhibit weaker pitch-related fluctuations than those tuned to frequencies between formants. Many auditory midbrain neurons show tuning to amplitude modulation frequency in addition to audio frequency. According to the auditory midbrain vowel encoding hypothesis, the response map of a population of midbrain neurons tuned to modulations near voice pitch exhibits minima near formant frequencies, due to the lack of strong pitch-related fluctuations at their inputs. This representation is robust over the range of noise conditions in which speech intelligibility is also robust for normal-hearing listeners. Based on this hypothesis, a vowel-enhancement strategy has been proposed that aims to restore vowel encoding at the level of the auditory midbrain. The signal processing consists of pitch tracking, formant tracking, and formant enhancement. The novel formant-tracking method proposed here estimates the first two formant frequencies by modeling characteristics of the auditory periphery, such as saturated discharge rates of AN fibers and modulation tuning properties of auditory midbrain neurons. The formant enhancement stage aims to restore the representation of formants at the level of the midbrain by increasing the dominance of a single harmonic near each formant and saturating t

关键词： Auditory models formant detection formant estimation formant tracking hearing aids neural coding speech analysis

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Measuring the Performance of neural Models

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2016年第FEB期10卷 10页

作者： Schoppe, Oliver Harper, Nicol S. Willmore, Ben D. B. King, Andrew J. Schnupp, Jan W. H. Univ Oxford Dept Physiol Anat & Genet Oxford England Tech Univ Munich Bioinspired Informat Proc Garching Germany

Good metrics of the performance of a statistical or computational model are essential for model comparison and selection. Here, we address the design of performance metrics for models that aim to predict neural responses to sensory inputs. This is particularly difficult because the responses of sensory neurons are inherently variable, even in response to repeated presentations of identical stimuli. In this situation, standard metrics (such as the correlation coefficient) fail because they do not distinguish between explainable variance (the part of the neural response that is systematically dependent on the stimulus) and response variability (the part of the neural response that is not systematically dependent on the stimulus, and cannot be explained by modeling the stimulus-response relationship). As a result, models which perfectly describe the systematic stimulus-response relationship may appear to perform poorly. Two metrics have previously been proposed which account for this inherent variability: Signal Power Explained (SPE, Sahani and Linden, 2003), and the normalized correlation coefficient (CCnorm, Hsu et al., 2004). Here, we analyze these metrics, and show that they are intimately related. However, SPE has no lower bound, and we show that, even for good models, SPE can yield negative values that are difficult to interpret. CCnorm is better behaved in that it is effectively bounded between 1 and 1, and values below zero are very rare in practice and easy to interpret. However, it was hitherto not possible to calculate CCnorm directly;instead, it was estimated using imprecise and laborious resampling techniques. Here, we identify a new approach that can calculate CCnorm quickly and accurately. As a result, we argue that it is now a better choice of metric than SPE to accurately evaluate the performance of neural models.

关键词： sensory neuron receptive field signal power model selection statistical modeling neural coding

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The Influence of Mexican Hat Recurrent Connectivity on Noise Correlations and Stimulus Encoding

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2017年 11卷 34页

作者： Meyer, Robert Ladenbauer, Josef Obermayer, Klaus Tech Univ Berlin Dept Software Engn & Theoret Comp Sci Berlin Germany Bernstein Ctr Computat Neurosci Berlin Germany Ecole Normale Super Lab Neurosci Cognit Grp Neural Theory Paris France

Noise correlations are a common feature of neural responses and have been observed in many cortical areas across different species. These correlations can influence information processing by enhancing or diminishing the quality of the neural code, but the origin of these correlations is still a matter of controversy. In this computational study we explore the hypothesis that noise correlations are the result of local recurrent excitatory and inhibitory connections. We simulated two-dimensional networks of adaptive spiking neurons with local connection patterns following Gaussian kernels. Noise correlations decay with distance between neurons but are only observed if the range of excitatory connections is smaller than the range of inhibitory connections ("Mexican hat" connectivity) and if the connection strengths are sufficiently strong. These correlations arise from a moving blob-like structure of evoked activity, which is absent if inhibitory interactions have a smaller range ("inverse Mexican hat" connectivity). Spatially structured external inputs fixate these blobs to certain locations and thus effectively reduce noise correlations. We further investigated the influence of these network configurations on stimulus encoding. On the one hand, the observed correlations diminish information about a stimulus encoded by a network. On the other hand, correlated activity allows for more precise encoding of stimulus information if the decoder has only access to a limited amount of neurons.

关键词： noise correlations neural coding Mexican hat connectivity spiking neural networks pattern formation primary visual cortex(V1) simulation models stimulus encoding

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Neuronal coupling benefits the encoding of weak periodic signals in symbolic spike patterns

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COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION 2020年 82卷 105023-000页

作者： Masoliver, Maria Masoller, Cristina Univ Politecn Cataluna Dept Fis Edifici GaiaRambla de St Nebridi 22 Barcelona 08222 Spain

The biophysical mechanisms by which an input signal elicits a neuronal response are well known (sufficiently large inputs change the membrane potential of the neuron and generate electrical pulses, known as action potentials or spikes), yet, a good understanding of how neurons use these spikes to encode the signal information remains elusive. Recent theoretical studies have focused on how neurons encode a weak periodic signal (that by itself is unable to generate spikes) in a noisy environment, where stochastic electrical fluctuations that do not encode any information occur. Analyzing spike sequences generated by individual neurons and by two coupled neurons (that were simulated with the stochastic FitzHugh-Nagumo model), it has been found that the relative timing of the spikes can encode the signal information. Using a symbolic method to analyze the spike sequence, preferred and infrequent spike patterns were detected, whose probabilities vary with both, the amplitude and the frequency of the signal. To investigate if this encoding mechanism is plausible also for neuronal ensembles, here we analyze the activity of a group of neurons, when they all perceive a weak periodic signal. We find that, as in the case of one or two coupled neurons, the probabilities of the spike patterns, now computed from the spike sequences of all the neurons, depend on the signal's amplitude and period, and thus, the patterns' probabilities encode the information of the signal. We also find that the resonances with the period of the signal or with the noise level are more pronounced when a group of neurons perceive the signal, in comparison with when only one or two coupled neurons perceive it. Neuronal coupling is beneficial for signal encoding as a group of neurons is able to encode a small-amplitude signal, which could not be encoded when it is perceived by just one or two coupled neurons. Interestingly, we find that for a group of neurons, just a few connections with one another can

关键词： neural coding Excitability Spike train variability Neuronal noise Fitzhugh-Nagumo model Time series analysis Symbolic analysis Ordinal analysis

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An Integrative Account of neural Network Interaction: Neuro-Messenger Theory

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World Journal of Neuroscience 2021年第2期11卷 124-136页

作者： Tien-Wen Lee The Neurocognitive Institute (NCI) Clinical Research Foundation New Jersey US Department of Psychiatry Dajia Lee’s General Hospital Lee’s Medical Corporation Taichung Chinese Taipei

neural interaction is realized by information exchange. It seemed that the information amount does not keep constant and may be reduced during the travel between neural nodes. In addition, recent research of neural coding has suggested that neural information could be represented by parsimonious spiking pattern, named sparse coding. Based on the above observation, neuro-messenger theory (NMT) is proposed to explicate the communicative process between the source and the target neural nodes. Neuro-messenger is a sparse code which does not have to carry every detail of the dynamics in source node. Other formats of neural coding (e.g., temporal and rate coding) could be the precursors of neuro-messengers, and the repeated spatiotemporal patterns buried in the ongoing brain activities may be the circulated neuro-messengers from diverse origins. Referred to chaos/complexity theory, information can be recovered at target node where neuro-messenger serves as a facilitator to locate the trajectory at proper attractor, and hence the associated psychological entity. In contrast to conventional concepts of encoding and decoding, the processes of encoding in source node, issuing neuro-messengers, and recovering information at target node are summarized as “three-facet coding scheme”. The design of neuro-messenger enables the brain to utilize energy in an efficient and economical way. NMT may have substantial implication in several major psychiatric disorders. Some psychiatric conditions could be mediated by abnormal neuro-messengers that coerce the regional neuro-dynamics to delve into maladaptive attractors and hence the characteristic symptoms.

关键词： Auditory Hallucination Delusion neural coding Neuro-Messenger Obsessive-Compulsive Disorder Sparse coding

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A computational model for human visual motion perception system

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IFAC Proceedings Volumes 1999年第2期32卷 5117-5122页

作者： Huimin Lu Shouzhong Liu Meide Shi Xiuchun Wang Aike Guo Institute of Biophysics Academia Sinica Beijing 100101 P.R. China

Reichardt's elementary motion detectors are employed for detecting motion. Moving image-stationary background discrimination is realized by correlating the detected motion information with properly delayed visual afterimages. Fuzzy-set theory is used in a nonlinear integration algorithm for obtaining a moving object direction. Both hypothetical neural coding schemes of temporal coding and firing rate coding are used for representation of visual motion information. A biologically plausible electronic neural network setup has been built for real-time examination of proposed model. The simulation results could well explain the phenomenon of visual anti-camouflage. The proposed model may erect a bridge between natural vision and robot vision.

关键词： Human visual motion perception Fuzzy-set theory neural coding Real-time implementation

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