scholarly journals A mechanistically interpretable model of the retinal neural code for natural scenes with multiscale adaptive dynamics

2021 ◽  
Author(s):  
Xuehao Ding ◽  
Dongsoo Lee ◽  
Satchel Grant ◽  
Heike Stein ◽  
Lane McIntosh ◽  
...  
Keyword(s):  
Adaptive Dynamics ◽  
Selective Adaptation ◽  
Neural Code ◽  
Natural Scenes ◽  
Uniform Field ◽  
Retinal Pathways ◽  
Wide Range ◽  
Spatiotemporal Parameters ◽  
Synaptic Dynamics ◽  
Synaptic Parameters

The visual system processes stimuli over a wide range of spatiotemporal scales, with individual neurons receiving input from tens of thousands of neurons whose dynamics range from milliseconds to tens of seconds. This poses a challenge to create models that both accurately capture visual computations and are mechanistically interpretable. Here we present a model of salamander retinal ganglion cell spiking responses recorded with a multielectrode array that captures natural scene responses and slow adaptive dynamics. The model consists of a three-layer convolutional neural network (CNN) modified to include local recurrent synaptic dynamics taken from a linear-nonlinear-kinetic (LNK) model \cite{ozuysal2012linking}. We presented alternating natural scenes and uniform field white noise stimuli designed to engage slow contrast adaptation. To overcome difficulties fitting slow and fast dynamics together, we first optimized all fast spatiotemporal parameters, then separately optimized recurrent slow synaptic parameters. The resulting full model reproduces a wide range of retinal computations and is mechanistically interpretable, having internal units that correspond to retinal interneurons with biophysically modeled synapses. This model allows us to study the contribution of model units to any retinal computation, and examine how long-term adaptation changes the retinal neural code for natural scenes through selective adaptation of retinal pathways.

Effects of Auditory Stimulus Context on the Representation of Frequency in the Gerbil Inferior Colliculus

2001 ◽  
Vol 86 (3) ◽  
pp. 1113-1130 ◽  
Author(s):  
B. J. Malone ◽  
M. N. Semple
Keyword(s):  
Inferior Colliculus ◽  
Duty Cycle ◽  
Selective Adaptation ◽  
Stimulus Context ◽  
Effective Stimulus ◽  
Spatial Stimulus ◽  
Interaural Phase ◽  
Wide Range ◽  
Conditioned Responses ◽  
Frequency Changes

Prior studies of dynamic conditioning have focused on modulation of binaural localization cues, revealing that the responses of inferior colliculus (IC) neurons to particular values of interaural phase and level disparities depend critically on the context in which they occur. Here we show that monaural frequency transitions, which do not simulate azimuthal motion, also condition the responses of IC neurons. We characterized single-unit responses to two frequency transition stimuli: a glide stimulus comprising two tones linked by a linear frequency sweep (origin-sweep-target) and a step stimulus consisting of one tone followed immediately by another (origin-target). Using sets of glide and step stimuli converging on a common target, we constructed conditioned response functions (RFs) depicting the variability in the response to an identical stimulus as a function of the preceding origin frequency. For nearly all cells, the response to the target depended on the origin frequency, even for origins outside the excitatory frequency response area of the cell. Results from conditioned RFs based on long (2–4 s) and short (200 ms) duration step stimuli indicate that conditioning effects can be induced in the absence of the dynamic sweep, and by stimuli of relatively short duration. Because IC neurons are tuned to frequency, changes in the origin frequency often change the “effective” stimulus duty cycle. In many cases, the enhancement of the target response appeared related to the decrease in the “effective” stimulus duty cycle rather than to the prior presentation of a particular origin frequency. Although this implies that nonselective adaptive mechanisms are responsible for conditioned responses, slightly more than half of IC neurons in each paradigm responded significantly differently to targets following origins that elicited statistically indistinguishable responses. The prevailing influence of stimulus context when discharge history is controlled demonstrates that not all the mechanisms governing conditioning depend on the discharge history of the recorded neuron. Selective adaptation among the neuron's variously tuned afferents may help engender stimulus-specific conditioning. The demonstration that conditioning effects reflect sensitivity to spectral as well as spatial stimulus contrast has broad implications for the processing of a wide range of dynamic acoustic signals and sound sequences.

scholarly journals The dynamic neural code of the retina for natural scenes

2018 ◽  
Author(s):  
Niru Maheswaranathan ◽  
Lane T. McIntosh ◽  
Hidenori Tanaka ◽  
Satchel Grant ◽  
David B. Kastner ◽  
...  
Keyword(s):  
Visual Processing ◽  
Ganglion Cells ◽  
Predictive Coding ◽  
Neural Code ◽  
Natural Scenes ◽  
Large Set ◽  
Fundamental Limits ◽  
Fundamental Goal ◽  
Latent Effects ◽  
Highly Correlated

AbstractUnderstanding how the visual system encodes natural scenes is a fundamental goal of sensory neuroscience. We show here that a three-layer network model predicts the retinal response to natural scenes with an accuracy nearing the fundamental limits of predictability. The model’s internal structure is interpretable, in that model units are highly correlated with interneurons recorded separately and not used to fit the model. We further show the ethological relevance to natural visual processing of a diverse set of phenomena of complex motion encoding, adaptation and predictive coding. Our analysis uncovers a fast timescale of visual processing that is inaccessible directly from experimental data, showing unexpectedly that ganglion cells signal in distinct modes by rapidly (< 0.1 s) switching their selectivity for direction of motion, orientation, location and the sign of intensity. A new approach that decomposes ganglion cell responses into the contribution of interneurons reveals how the latent effects of parallel retinal circuits generate the response to any possible stimulus. These results reveal extremely flexible and rapid dynamics of the retinal code for natural visual stimuli, explaining the need for a large set of interneuron pathways to generate the dynamic neural code for natural scenes.

Spatiotemporal Parameters And Gait Variability In People With Psoriatic Arthritis (Psa) : A Cross-Sectional Study.

2021 ◽  
Author(s):  
Roua Walha ◽  
Nathaly Gaudreault ◽  
Pierre Dagenais ◽  
Patrick Boissy
Keyword(s):  
Psoriatic Arthritis ◽  
Gait Speed ◽  
Gait Variability ◽  
Functional Mobility ◽  
Clinical Settings ◽  
Foot Pain ◽  
Wide Range ◽  
Spatiotemporal Parameters ◽  
Foot Function ◽  
And Function

Abstract Background: Foot involvement is a major manifestation of psoriatic arthritis (PsA) and could lead to severe levels of foot pain and disability and impaired functional mobility and quality of life. Gait spatiotemporal parameters (STPs) and gait variability, used as a clinical index of gait stability, have been associated with several adverse health outcomes including risk of falling, functional decline, and mortality in a wide range of populations. Previous studies showed some alterations in STPs in people with PsA. However, gait variability and the relationships between STPs, gait variability and self-reported foot pain and disability have never been studied in this populations. Body-worn inertial measurement units (IMUs) are gaining interest in measuring gait parameters in clinical settings.Objectives: To assess STPs and gait variability in people with PsA using IMUs and, to explore their relationship with self-reported foot pain and function and to investigate the feasibility of using IMUs to discriminate patient groups based on gait speed-critical values.Methods: 21 participants with PsA (Age: 53.9 ± 8.9 yrs; median disease duration: 6 yrs) and 21 age and gender-matched healthy participants (Age 54.23 ± 9.3 yrs) were recruited. All the participants performed three 10-meter walk test trials at their comfortable speed. STPs and gait variability were recorded and calculated using six body-worn IMUs and the Mobility Lab software (APDM®). Foot pain and disability were assessed in participants with PsA using the foot function index (FFI).Results: Cadence, gait speed, stride length, and swing phase, were significantly lower, while double support was significantly higher, in the PsA group (p< 0.006). Strong correlations between STPs and the FFI total score were demonstrated (|r|> 0.57, p< 0.006). Gait variability was significantly increased in the PsA group, but it was not correlated with foot pain and function (p< 0.006). Using the IMUs three subgroups of participants with PsA with clinically meaningful differences in self-reported foot pain and disability were discriminated.Conclusion: STPs were significantly altered in participants with PsA which could be associated with self-reported foot pain and disability. Future studies are required to confirm the increased gait variability highlighted in this study and its potential underlying causes. Using IMUs in clinical settings has been useful to objectively assess foot function in people with PsA. Study registration: ClinicalTrials.gov, NCT05075343, Retrospectively registered on 29 September 2021.

scholarly journals Generating Bessel beams with broad depth-of-field by using phase-only acoustic holograms

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Sergio Jiménez-Gambín ◽  
Noé Jiménez ◽  
José M. Benlloch ◽  
Francisco Camarena
Keyword(s):  
Field Distribution ◽  
Focal Length ◽  
Bessel Beam ◽  
Transverse Field ◽  
High Order ◽  
Depth Of Field ◽  
Uniform Field ◽  
Axial Distribution ◽  
Bessel Beams ◽  
Wide Range

AbstractWe report zero-th and high-order acoustic Bessel beams with broad depth-of-field generated using acoustic holograms. While the transverse field distribution of Bessel beams generated using traditional passive methods is correctly described by a Bessel function, these methods present a common drawback: the axial distribution of the field is not constant, as required for ideal Bessel beams. In this work, we experimentally, numerically and theoretically report acoustic truncated Bessel beams of flat-intensity along their axis in the ultrasound regime using phase-only holograms. In particular, the beams present a uniform field distribution showing an elongated focal length of about 40 wavelengths, while the transverse width of the beam remains smaller than 0.7 wavelengths. The proposed acoustic holograms were compared with 3D-printed fraxicons, a blazed version of axicons. The performance of both phase-only holograms and fraxicons is studied and we found that both lenses produce Bessel beams in a wide range of frequencies. In addition, high-order Bessel beam were generated. We report first order Bessel beams that show a clear phase dislocation along their axis and a vortex with single topological charge. The proposed method may have potential applications in ultrasonic imaging, biomedical ultrasound and particle manipulation applications using passive lenses.

Neuroimaging of Direction-Selective Mechanisms for Second-Order Motion

2003 ◽  
Vol 90 (5) ◽  
pp. 3242-3254 ◽  
Author(s):  
Shin'ya Nishida ◽  
Yuka Sasaki ◽  
Ikuya Murakami ◽  
Takeo Watanabe ◽  
Roger B. H. Tootell
Keyword(s):  
Neural Substrates ◽  
Neural Correlates ◽  
Selective Adaptation ◽  
Motion Processing ◽  
Motion Adaptation ◽  
Motion Stimulus ◽  
Fmri Study ◽  
Wide Range ◽  
Contingent Response ◽  
Visual Cortical

Psychophysical findings have revealed a functional segregation of processing for 1st-order motion (movement of luminance modulation) and 2nd-order motion (e.g., movement of contrast modulation). However neural correlates of this psychophysical distinction remain controversial. To test for a corresponding anatomical segregation, we conducted a new functional magnetic resonance imaging (fMRI) study to localize direction-selective cortical mechanisms for 1st- and 2nd-order motion stimuli, by measuring direction-contingent response changes induced by motion adaptation, with deliberate control of attention. The 2nd-order motion stimulus generated direction-selective adaptation in a wide range of visual cortical areas, including areas V1, V2, V3, VP, V3A, V4v, and MT+. Moreover, the pattern of activity was similar to that obtained with 1st-order motion stimuli. Contrary to expectations from psychophysics, these results suggest that in the human visual cortex, the direction of 2nd-order motion is represented as early as V1. In addition, we found no obvious anatomical segregation in the neural substrates for 1st- and 2nd-order motion processing that can be resolved using standard fMRI.

scholarly journals SOCIAL LEARNING ABOUT CONSUMPTION

2015 ◽  
Vol 20 (7) ◽  
pp. 1795-1825 ◽  
Author(s):  
Isabelle Salle ◽  
Pascal Seppecher
Keyword(s):  
Social Learning ◽  
Adaptive Dynamics ◽  
Learning Model ◽  
Learning Objectives ◽  
Optimal Consumption ◽  
Speed Of Convergence ◽  
Learning Behavior ◽  
Macroeconomic Dynamics ◽  
Wide Range ◽  
Behavioral Interpretation

This paper applies a social learning model to the optimal consumption rule of Allen and Carroll [Macroeconomic Dynamics5(2001), 255–271] and delivers convincing convergence dynamics toward the optimal rule. These findings constitute a significant improvement over previous results in the literature, in terms of both speed of convergence and parsimony of the learning model. The learning model exhibits several appealing features: it is frugal, is easy to apply to a various range of learning objectives, and requires few procedures and little information. Particular care is given to behavioral interpretation of the modeling assumptions in light of evidence from the fields of psychology and social science. Our results highlight the need to depart from the genetic metaphor, and account for intentional decision-making, based on agents' relative performances. By contrast, we show that convergence is strongly hindered by exact imitation processes, or random exploration mechanisms, which are usually assumed when modeling social learning behavior. Our results suggest a method for modeling bounded rationality, which could be interestingly tested in a wide range of economic models with adaptive dynamics.

What Determines the Frequency of Fast Network Oscillations With Irregular Neural Discharges? I. Synaptic Dynamics and Excitation-Inhibition Balance

2003 ◽  
Vol 90 (1) ◽  
pp. 415-430 ◽  
Author(s):  
Nicolas Brunel ◽  
Xiao-Jing Wang
Keyword(s):  
Local Field ◽  
Oscillation Frequency ◽  
Field Potential ◽  
Spike Trains ◽  
Feedback Mechanism ◽  
Time Constants ◽  
Gamma Range ◽  
Fast Network ◽  
Synaptic Dynamics ◽  
Synaptic Parameters

When the local field potential of a cortical network displays coherent fast oscillations (∼40-Hz gamma or ∼200-Hz sharp-wave ripples), the spike trains of constituent neurons are typically irregular and sparse. The dichotomy between rhythmic local field and stochastic spike trains presents a challenge to the theory of brain rhythms in the framework of coupled oscillators. Previous studies have shown that when noise is large and recurrent inhibition is strong, a coherent network rhythm can be generated while single neurons fire intermittently at low rates compared to the frequency of the oscillation. However, these studies used too simplified synaptic kinetics to allow quantitative predictions of the population rhythmic frequency. Here we show how to derive quantitatively the coherent oscillation frequency for a randomly connected network of leaky integrate-and-fire neurons with realistic synaptic parameters. In a noise-dominated interneuronal network, the oscillation frequency depends much more on the shortest synaptic time constants (delay and rise time) than on the longer synaptic decay time, and ∼200-Hz frequency can be realized with synaptic time constants taken from slice data. In a network composed of both interneurons and excitatory cells, the rhythmogenesis is a compromise between two scenarios: the fast purely interneuronal mechanism, and the slower feedback mechanism (relying on the excitatory-inhibitory loop). The properties of the rhythm are determined essentially by the ratio of time scales of excitatory and inhibitory currents and by the balance between the mean recurrent excitation and inhibition. Faster excitation than inhibition, or a higher excitation/inhibition ratio, favors the feedback loop and a much slower oscillation (typically in the gamma range).

scholarly journals Modelling the neural code in large populations of correlated neurons

2020 ◽  
Author(s):  
Sacha Sokoloski ◽  
Amir Aschner ◽  
Ruben Coen-Cagli
Keyword(s):  
Neural Coding ◽  
Large Scale ◽  
Neural Code ◽  
Neural Population ◽  
Population Activity ◽  
Fundamental Properties ◽  
Wide Range ◽  
Large Populations ◽  
Scale Population ◽  
Quantitative Understanding

AbstractThe activity of a neural population encodes information about the stimulus that caused it, and decoding population activity reveals how neural circuits process that information. Correlations between neurons strongly impact both encoding and decoding, yet we still lack models that simultaneously capture stimulus encoding by large populations of correlated neurons and allow for accurate decoding of stimulus information, thus limiting our quantitative understanding of the neural code. To address this, we propose a class of models of large-scale population activity based on the theory of exponential family distributions. We apply our models to macaque primary visual cortex (V1) recordings, and show they capture a wide range of response statistics, facilitate accurate Bayesian decoding, and provide interpretable representations of fundamental properties of the neural code. Ultimately, our framework could allow researchers to quantitatively validate predictions of theories of neural coding against both large-scale response recordings and cognitive performance.

Evolutionary Branching in a Classical Model for Sympatric Speciation

2011 ◽  
Author(s):  
Michael Doebeli
Keyword(s):  
Reproductive Isolation ◽  
Adaptive Dynamics ◽  
Classical Model ◽  
Sympatric Speciation ◽  
Disruptive Selection ◽  
General Outcome ◽  
Wide Range ◽  
Genetic Mechanisms ◽  
Basic Concepts ◽  
Smith Model

This chapter begins by considering the Maynard Smith model. Much of this work concentrated on the genetic mechanisms for assortative mating and reproductive isolation, based on the assumption that the underlying niche ecology would generate disruptive selection. However, understanding the conditions under which disruptive selection arises in the first place is equally important, and indeed necessary for assessing whether diversification is a general outcome in the Maynard Smith model. The chapter then shows that disruptive selection and polymorphism are scenarios that occur generically, that is, for a wide range of parameters, in a classical and widely used speciation model. It also provides an introduction to some of the basic concepts of adaptive dynamics theory.

scholarly journals Indices for Testing Neural Codes

2008 ◽  
Vol 20 (12) ◽  
pp. 2895-2936 ◽  
Author(s):  
Jonathan D. Victor ◽  
Sheila Nirenberg
Keyword(s):  
Neural Code ◽  
Response Relationship ◽  
Information Theoretic ◽  
Systems Neuroscience ◽  
Transmitted Information ◽  
Stimulus Response ◽  
Wide Range ◽  
Relationship Of ◽  
The Relationship ◽  
Rule Out

One of the most critical challenges in systems neuroscience is determining the neural code. A principled framework for addressing this can be found in information theory. With this approach, one can determine whether a proposed code can account for the stimulus-response relationship. Specifically, one can compare the transmitted information between the stimulus and the hypothesized neural code with the transmitted information between the stimulus and the behavioral response. If the former is smaller than the latter (i.e., if the code cannot account for the behavior), the code can be ruled out. The information-theoretic index most widely used in this context is Shannon's mutual information. The Shannon test, however, is not ideal for this purpose: while the codes it will rule out are truly nonviable, there will be some nonviable codes that it will fail to rule out. Here we describe a wide range of alternative indices that can be used for ruling codes out. The range includes a continuum from Shannon information to measures of the performance of a Bayesian decoder. We analyze the relationship of these indices to each other and their complementary strengths and weaknesses for addressing this problem.

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