A Model for Perceptual Averaging and Stochastic Bistable Behavior and the Role of Voluntary Control

2006 ◽  
Vol 18 (12) ◽  
pp. 3069-3096 ◽  
Author(s):  
Ansgar R. Koene
Keyword(s):  
Receptive Fields ◽  
Population Coding ◽  
Voluntary Control ◽  
Signal Averaging ◽  
Perceptual Awareness ◽  
Large Stimulus ◽  
Surface Slant ◽  
Single Top ◽  
Inhibitory Feedback ◽  
Combine Population

We combine population coding, winner-take-all competition, and differentiated inhibitory feedback to model the process by which information from different, continuously variable signals is integrated for perceptual awareness. We focus on “slant rivalry,” where binocular disparity is in conflict with monocular perspective in specifying surface slant. Using a robust single parameter set, our model successfully replicates three key experimental results: (1) transition from signal averaging to bistability with increasing signal conflict, (2) change in perceptual reversal rates as a function of signal conflict, and (3) a shift in the distribution of percept durations through voluntary control exertion. Voluntary control is implemented through the use of a single top-down bias input. The transition from signal averaging to bistability arises as a natural consequence of combining population coding and wide receptive fields, common to higher cortical areas. The model architecture does not contain any assumption that would limit it to this particular example of stimulus rivalry. An emergent physiological interpretation is that differentiated inhibitory feedback may play an important role for increasing percept stability without reducing sensitivity to large stimulus changes, which for bistable conditions leads to increased alternation rate as a function of signal conflict.

Multisensory Bayesian Inference Depends on Synapse Maturation during Training: Theoretical Analysis and Neural Modeling Implementation

2017 ◽  
Vol 29 (3) ◽  
pp. 735-782 ◽  
Author(s):  
Mauro Ursino ◽  
Cristiano Cuppini ◽  
Elisa Magosso
Keyword(s):  
Bayesian Inference ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimate ◽  
Experimental Studies ◽  
Receptive Fields ◽  
Learning Rule ◽  
Population Coding ◽  
Audiovisual Integration ◽  
Likelihood Estimate ◽  
Bayesian Estimate

Recent theoretical and experimental studies suggest that in multisensory conditions, the brain performs a near-optimal Bayesian estimate of external events, giving more weight to the more reliable stimuli. However, the neural mechanisms responsible for this behavior, and its progressive maturation in a multisensory environment, are still insufficiently understood. The aim of this letter is to analyze this problem with a neural network model of audiovisual integration, based on probabilistic population coding—the idea that a population of neurons can encode probability functions to perform Bayesian inference. The model consists of two chains of unisensory neurons (auditory and visual) topologically organized. They receive the corresponding input through a plastic receptive field and reciprocally exchange plastic cross-modal synapses, which encode the spatial co-occurrence of visual-auditory inputs. A third chain of multisensory neurons performs a simple sum of auditory and visual excitations. The work includes a theoretical part and a computer simulation study. We show how a simple rule for synapse learning (consisting of Hebbian reinforcement and a decay term) can be used during training to shrink the receptive fields and encode the unisensory likelihood functions. Hence, after training, each unisensory area realizes a maximum likelihood estimate of stimulus position (auditory or visual). In cross-modal conditions, the same learning rule can encode information on prior probability into the cross-modal synapses. Computer simulations confirm the theoretical results and show that the proposed network can realize a maximum likelihood estimate of auditory (or visual) positions in unimodal conditions and a Bayesian estimate, with moderate deviations from optimality, in cross-modal conditions. Furthermore, the model explains the ventriloquism illusion and, looking at the activity in the multimodal neurons, explains the automatic reweighting of auditory and visual inputs on a trial-by-trial basis, according to the reliability of the individual cues.

scholarly journals A signature of neural coding at human perceptual limits

2016 ◽  
Author(s):  
Paul M Bays
Keyword(s):  
Neural Coding ◽  
Empirical Relationship ◽  
Human Perception ◽  
Population Coding ◽  
Neural Population ◽  
Perceptual Awareness ◽  
Neural Basis ◽  
Visual Neurons ◽  
Error Magnitude ◽  
Subjective Confidence

AbstractSimple visual features, such as orientation, are thought to be represented in the spiking of visual neurons using population codes. I show that optimal decoding of such activity predicts characteristic deviations from the normal distribution of errors at low gains. Examining human perception of orientation stimuli, I show that these predicted deviations are present at near-threshold levels of contrast. The findings may provide a neural-level explanation for the appearance of a threshold in perceptual awareness, whereby stimuli are categorized as seen or unseen. As well as varying in error magnitude, perceptual judgments differ in certainty about what was observed. I demonstrate that variations in the total spiking activity of a neural population can account for the empirical relationship between subjective confidence and precision. These results establish population coding and decoding as the neural basis of perception and perceptual confidence.

FROM SENSORS TO SPIKES: EVOLVING RECEPTIVE FIELDS TO ENHANCE SENSORIMOTOR INFORMATION IN A ROBOT-ARM

2012 ◽  
Vol 22 (04) ◽  
pp. 1250013 ◽  
Author(s):  
NICETO R. LUQUE ◽  
JESÚS A. GARRIDO ◽  
JARNO RALLI ◽  
JUANLU J. LAREDO ◽  
EDUARDO ROS
Keyword(s):  
Evolutionary Algorithm ◽  
Receptive Fields ◽  
Joint Space ◽  
Nerve Fibers ◽  
Population Coding ◽  
Multidimensional Space ◽  
Robot Arm ◽  
Joint Movement ◽  
Field Representation ◽  
Somatosensory Neurons

In biological systems, instead of actual encoders at different joints, proprioception signals are acquired through distributed receptive fields. In robotics, a single and accurate sensor output per link (encoder) is commonly used to track the position and the velocity. Interfacing bio-inspired control systems with spiking neural networks emulating the cerebellum with conventional robots is not a straight forward task. Therefore, it is necessary to adapt this one-dimensional measure (encoder output) into a multidimensional space (inputs for a spiking neural network) to connect, for instance, the spiking cerebellar architecture; i.e. a translation from an analog space into a distributed population coding in terms of spikes. This paper analyzes how evolved receptive fields (optimized towards information transmission) can efficiently generate a sensorimotor representation that facilitates its discrimination from other "sensorimotor states". This can be seen as an abstraction of the Cuneate Nucleus (CN) functionality in a robot-arm scenario. We model the CN as a spiking neuron population coding in time according to the response of mechanoreceptors during a multi-joint movement in a robot joint space. An encoding scheme that takes into account the relative spiking time of the signals propagating from peripheral nerve fibers to second-order somatosensory neurons is proposed. Due to the enormous number of possible encodings, we have applied an evolutionary algorithm to evolve the sensory receptive field representation from random to optimized encoding. Following the nature-inspired analogy, evolved configurations have shown to outperform simple hand-tuned configurations and other homogenized configurations based on the solution provided by the optimization engine (evolutionary algorithm). We have used artificial evolutionary engines as the optimization tool to circumvent nonlinearity responses in receptive fields.

Continuous Dimensions and Distributional Learning

2018 ◽  
pp. 127-154
Author(s):  
Vsevolod Kapatsinski
Keyword(s):  
Language Change ◽  
Irrelevant Dimension ◽  
Receptive Fields ◽  
Population Coding ◽  
Generative Model ◽  
Learning Mechanisms ◽  
Iterated Learning ◽  
Distributional Learning ◽  
The Difference ◽  
Attention Shifts

This chapter describes the evidence for the existence of dimensions, focusing on the difference between the difficulty of attention shifts to a previously relevant vs. irrelevant dimension. It discusses the representation of continuous dimensions in the associationist framework. including population coding and thermometer coding, as well as the idea that learning can adjust the breadth of adjustable receptive fields. In phonetics, continuous dimensions have been argued to be split into categories via distributional learning. This chapter reviews what we know about distributional learning and argues that it relies on several distinct learning mechanisms, including error-driven learning at two distinct levels and building a generative model of the speaker. The emergence of perceptual equivalence regions from error-driven learning is discussed, and implications for language change briefly noted with an iterated learning simulation.

Statistical Issues in the Analysis of Neuronal Data

2005 ◽  
Vol 94 (1) ◽  
pp. 8-25 ◽  
Author(s):  
Robert E. Kass ◽  
Valérie Ventura ◽  
Emery N. Brown
Keyword(s):  
Firing Rate ◽  
Recent Work ◽  
Neuronal Response ◽  
Receptive Fields ◽  
Population Coding ◽  
Time Varying ◽  
New Methods ◽  
Scientific Inference ◽  
Statistical Issues ◽  
Poisson Data

Analysis of data from neurophysiological investigations can be challenging. Particularly when experiments involve dynamics of neuronal response, scientific inference can become subtle and some statistical methods may make much more efficient use of the data than others. This article reviews well-established statistical principles, which provide useful guidance, and argues that good statistical practice can substantially enhance results. Recent work on estimation of firing rate, population coding, and time-varying correlation provides improvements in experimental sensitivity equivalent to large increases in the number of neurons examined. Modern nonparametric methods are applicable to data from repeated trials. Many within-trial analyses based on a Poisson assumption can be extended to non-Poisson data. New methods have made it possible to track changes in receptive fields, and to study trial-to-trial variation, with modest amounts of data.

scholarly journals Sensory coding and contrast invariance emerge from the control of plastic inhibition over excitatory connectivity

2020 ◽  
Author(s):  
René Larisch ◽  
Lorenz Gönner ◽  
Michael Teichmann ◽  
Fred H. Hamker
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Receptive Fields ◽  
Gain Control ◽  
Metabolic Efficiency ◽  
Tuning Curves ◽  
Coding Efficiency ◽  
Inhibitory Feedback ◽  
Efficient Code ◽  
Inhibitory Plasticity

Visual stimuli are represented by a highly efficient code in the primary visual cortex, but the development of this code is still unclear. Two distinct factors control coding efficiency: Representational efficiency, which is determined by neuronal tuning diversity, and metabolic efficiency, which is influenced by neuronal gain. How these determinants of coding efficiency are shaped during development, supported by excitatory and inhibitory plasticity, is only partially understood. We investigate a fully plastic spiking network of the primary visual cortex, building on phenomenological plasticity rules. Our results show that inhibitory plasticity is key to the emergence of tuning diversity and accurate input encoding. Additionally, inhibitory feedback increases the metabolic efficiency by implementing a gain control mechanism. Interestingly, this led to the spontaneous emergence of contrast-invariant tuning curves. Our findings highlight the role of interneuron plasticity during the development of receptive fields and in shaping sensory representations.

scholarly journals Modeling of Auditory Spatial Receptive Fields With Spherical Approximation Functions

1998 ◽  
Vol 80 (5) ◽  
pp. 2645-2656 ◽  
Author(s):  
Rick L. Jenison ◽  
Richard A. Reale ◽  
Joseph E. Hind ◽  
John F. Brugge
Keyword(s):  
Receptive Field ◽  
Computational Models ◽  
Receptive Fields ◽  
Radial Symmetry ◽  
Population Coding ◽  
Virtual Space ◽  
Spherical Approximation ◽  
Directional Sensitivity ◽  
Approximation Technique ◽  
Estimation Of Parameters

Jenison, Rick L., Richard A. Reale, Joseph E. Hind, and John F. Brugge. Modeling of auditory spatial receptive fields with spherical approximation functions. J. Neurophysiol. 80: 2645–2656, 1998. A spherical approximation technique is presented that affords a mathematical characterization of a virtual space receptive field (VSRF) based on first-spike latency in the auditory cortex of cat. Parameterizing directional sensitivity in this fashion is much akin to the use of difference-of-Gaussian (DOG) functions for modeling neural responses in visual cortex. Artificial neural networks and approximation techniques typically have been applied to problems conforming to a multidimensional Cartesian input space. The problem with using classical planar Gaussians is that radial symmetry and consistency on the plane actually translate into directionally dependent distortion on spherical surfaces. An alternative set of spherical basis functions, the von Mises basis function (VMBF), is used to eliminate spherical approximation distortion. Unlike the Fourier transform or spherical harmonic expansions, the VMBFs are nonorthogonal, and hence require some form of gradient-descent search for optimal estimation of parameters in the modeling of the VSRF. The optimization equations required to solve this problem are presented. Three descriptive classes of VSRF (contralateral, frontal, and ipsilateral) approximations are investigated, together with an examination of the residual error after parameter optimization. The use of the analytic receptive field model in computational models of population coding of sound direction is discussed, together with the importance of quantifying receptive field gradients. Because spatial hearing is by its very nature three dimensional or, more precisely, two dimensional (directional) on the sphere, we find that spatial receptive field models are best developed on the sphere.

scholarly journals Sub-optimality of the early visual system explained through biologically plausible plasticity

2019 ◽  
Author(s):  
Tushar Chauhan ◽  
Timothée Masquelier ◽  
Benoit R. Cottereau
Keyword(s):  
Visual Cortex ◽  
Receptive Fields ◽  
Spike Timing ◽  
Population Coding ◽  
Orientation Discrimination ◽  
Information Theoretic ◽  
Biologically Relevant ◽  
Early Visual Cortex ◽  
Efficient Representation ◽  
Multiple Levels

AbstractThe early visual cortex is the site of crucial pre-processing for more complex, biologically relevant computations that drive perception and, ultimately, behaviour. This pre-processing is often viewed as an optimisation which enables the most efficient representation of visual input. However, measurements in monkey and cat suggest that receptive fields in the primary visual cortex are often noisy, blobby, and symmetrical, making them sub-optimal for operations such as edge-detection. We propose that this suboptimality occurs because the receptive fields do not emerge through a global minimisation of the generative error, but through locally operating biological mechanisms such as spike-timing dependent plasticity. Using an orientation discrimination paradigm, we show that while sub-optimal, such models offer a much better description of biology at multiple levels: single-cell, population coding, and perception. Taken together, our results underline the need to carefully consider the distinction between information-theoretic and biological notions of optimality in early sensorial populations.

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