Context dependence of spectro-temporal receptive fields with implications for neural coding

2011 ◽  
Vol 271 (1-2) ◽  
pp. 123-132 ◽  
Author(s):  
Jos J. Eggermont
Keyword(s):  
Neural Coding ◽  
Receptive Fields ◽  
Context Dependence

Wavelet-Like Receptive Fields Emerges by Non-Linear Minimization of Neuron Error

2003 ◽  
Vol 13 (02) ◽  
pp. 87-91
Author(s):  
Allan Kardec Barros ◽  
Andrzej Cichocki ◽  
Noboru Ohnishi
Keyword(s):  
Neural Coding ◽  
Single Neuron ◽  
Receptive Fields ◽  
Natural Images ◽  
Squared Error ◽  
Redundancy Reduction ◽  
One Step ◽  
The One ◽  
Mutually Independent ◽  
Large Research

Redundancy reduction as a form of neural coding has been since the early sixties a topic of large research interest. A number of strategies has been proposed, but the one which is attracting most attention recently assumes that this coding is carried out so that the output signals are mutually independent. In this work we go one step further and suggest an strategy to deal also with non-orthogonal signals (i.e., ''dependent'' signals). Moreover, instead of working with the usual squared error, we design a neuron where the non-linearity is operating on the error. It is computationally more economic and, importantly, the permutation/scaling problem10 is avoided. The framework is given with a biological background, as we avocate throughout the manuscript that the algorithm fits well the single neuron and redundancy reduction doctrine.5 Moreover, we show that wavelet-like receptive fields emerges from natural images processed by this algorithm.

scholarly journals Scene statistics and noise determine the relative arrangement of receptive field mosaics

2021 ◽  
Vol 118 (39) ◽  
pp. e2105115118
Author(s):  
Na Young Jun ◽  
Greg D. Field ◽  
John Pearson
Keyword(s):  
Neural Coding ◽  
Coding Theory ◽  
Receptive Fields ◽  
Optimal Arrangement ◽  
Efficient Coding ◽  
Natural Stimuli ◽  
Signal Stimulus ◽  
Theoretical Predictions ◽  
On And Off Pathways ◽  
Contrast Response

Many sensory systems utilize parallel ON and OFF pathways that signal stimulus increments and decrements, respectively. These pathways consist of ensembles or grids of ON and OFF detectors spanning sensory space. Yet, encoding by opponent pathways raises a question: How should grids of ON and OFF detectors be arranged to optimally encode natural stimuli? We investigated this question using a model of the retina guided by efficient coding theory. Specifically, we optimized spatial receptive fields and contrast response functions to encode natural images given noise and constrained firing rates. We find that the optimal arrangement of ON and OFF receptive fields exhibits a transition between aligned and antialigned grids. The preferred phase depends on detector noise and the statistical structure of the natural stimuli. These results reveal that noise and stimulus statistics produce qualitative shifts in neural coding strategies and provide theoretical predictions for the configuration of opponent pathways in the nervous system.

scholarly journals Learning sparse codes from compressed representations with biologically plausible local wiring constraints

2020 ◽  
Author(s):  
Kion Fallah ◽  
Adam A. Willats ◽  
Ninghao Liu ◽  
Christopher J. Rozell
Keyword(s):  
Unsupervised Learning ◽  
Sparse Coding ◽  
Visual Information ◽  
Neural Coding ◽  
Receptive Fields ◽  
Sparse Representations ◽  
Linear Operators ◽  
Compression Process ◽  
Cortical Areas ◽  
Theoretical Neuroscience

AbstractSparse coding is an important method for unsupervised learning of task-independent features in theoretical neuroscience models of neural coding. While a number of algorithms exist to learn these representations from the statistics of a dataset, they largely ignore the information bottlenecks present in fiber pathways connecting cortical areas. For example, the visual pathway has many fewer neurons transmitting visual information to cortex than the number of photoreceptors. Both empirical and analytic results have recently shown that sparse representations can be learned effectively after performing dimensionality reduction with randomized linear operators, producing latent coefficients that preserve information. Unfortunately, current proposals for sparse coding in the compressed space require a centralized compression process (i.e., dense random matrix) that is biologically unrealistic due to local wiring constraints observed in neural circuits. The main contribution of this paper is to leverage recent results on structured random matrices to propose a theoretical neuroscience model of randomized projections for communication between cortical areas that is consistent with the local wiring constraints observed in neuroanatomy. We show analytically and empirically that unsupervised learning of sparse representations can be performed in the compressed space despite significant local wiring constraints in compression matrices of varying forms (corresponding to different local wiring patterns). Our analysis verifies that even with significant local wiring constraints, the learned representations remain qualitatively similar, have similar quantitative performance in both training and generalization error, and are consistent across many measures with measured macaque V1 receptive fields.

scholarly journals Predicting proprioceptive cortical anatomy and neural coding with topographic autoencoders

2021 ◽  
Author(s):  
Kyle P Blum ◽  
Max D Grogan ◽  
Yufei Wu ◽  
Alex J Harston ◽  
Lee E Miller ◽  
...  
Keyword(s):  
Neural Coding ◽  
Receptive Fields ◽  
Humanoid Robots ◽  
Large Set ◽  
Electrode Arrays ◽  
Movement Data ◽  
Computer Interfaces ◽  
Variational Autoencoder ◽  
Natural Movement ◽  
Cortical Map

Proprioception is one of the least understood senses yet fundamental for the control of movement. Even basic questions of how limb pose is represented in the somatosensory cortex are unclear. We developed a variational autoencoder with topographic lateral connectivity (topo-VAE) to compute a putative cortical map from a large set of natural movement data. Although not fitted to neural data, our model reproduces two sets of observations from monkey centre-out reaching: 1. The shape and velocity dependence of proprioceptive receptive fields in hand-centered coordinates despite the model having no knowledge of arm kinematics or hand coordinate systems. 2. The distribution of neuronal preferred directions (PDs) recorded from multi-electrode arrays. The model makes several testable predictions: 1. Encoding across the cortex has a blob-and-pinwheel-type geometry PDs. 2. Few neurons will encode just a single joint. Topo-VAE provides a principled basis for understanding of sensorimotor representations, and the theoretical basis of neural manifolds, with application the restoration of sensory feedback in brain-computer interfaces and the control of humanoid robots.

scholarly journals Coherent alpha oscillations link current and future receptive fields during saccades

2017 ◽  
Vol 114 (29) ◽  
pp. E5979-E5985 ◽  
Author(s):  
Sujaya Neupane ◽  
Daniel Guitton ◽  
Christopher C. Pack
Keyword(s):  
Receptive Field ◽  
Neural Coding ◽  
Visual Stimulation ◽  
Receptive Fields ◽  
Visual Space ◽  
Spatial Arrangement ◽  
Brain Regions ◽  
Alpha Oscillations ◽  
Area V4 ◽  
Before And After

Oscillations are ubiquitous in the brain, and they can powerfully influence neural coding. In particular, when oscillations at distinct sites are coherent, they provide a means of gating the flow of neural signals between different cortical regions. Coherent oscillations also occur within individual brain regions, although the purpose of this coherence is not well understood. Here, we report that within a single brain region, coherent alpha oscillations link stimulus representations as they change in space and time. Specifically, in primate cortical area V4, alpha coherence links sites that encode the retinal location of a visual stimulus before and after a saccade. These coherence changes exhibit properties similar to those of receptive field remapping, a phenomenon in which individual neurons change their receptive fields according to the metrics of each saccade. In particular, alpha coherence, like remapping, is highly dependent on the saccade vector and the spatial arrangement of current and future receptive fields. Moreover, although visual stimulation plays a modulatory role, it is neither necessary nor sufficient to elicit alpha coherence. Indeed, a similar pattern of coherence is observed even when saccades are made in darkness. Together, these results show that the pattern of alpha coherence across the retinotopic map in V4 matches many of the properties of receptive field remapping. Thus, oscillatory coherence might play a role in constructing the stable representation of visual space that is an essential aspect of conscious perception.

scholarly journals Temporal stability of stimulus representation increases along rodent visual cortical hierarchies

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Eugenio Piasini ◽  
Liviu Soltuzu ◽  
Paolo Muratore ◽  
Riccardo Caramellino ◽  
Kasper Vinken ◽  
...  
Keyword(s):  
Visual Processing ◽  
Visual Information ◽  
Neural Coding ◽  
Temporal Stability ◽  
Receptive Fields ◽  
Ventral Stream ◽  
Dynamic Stimuli ◽  
The Stability ◽  
Visual Cortical ◽  
The Impact

AbstractCortical representations of brief, static stimuli become more invariant to identity-preserving transformations along the ventral stream. Likewise, increased invariance along the visual hierarchy should imply greater temporal persistence of temporally structured dynamic stimuli, possibly complemented by temporal broadening of neuronal receptive fields. However, such stimuli could engage adaptive and predictive processes, whose impact on neural coding dynamics is unknown. By probing the rat analog of the ventral stream with movies, we uncovered a hierarchy of temporal scales, with deeper areas encoding visual information more persistently. Furthermore, the impact of intrinsic dynamics on the stability of stimulus representations grew gradually along the hierarchy. A database of recordings from mouse showed similar trends, additionally revealing dependencies on the behavioral state. Overall, these findings show that visual representations become progressively more stable along rodent visual processing hierarchies, with an important contribution provided by intrinsic processing.

scholarly journals Unsupervised learning in images and audio to produce neural receptive fields: a primer and accessible notebook

2021 ◽  
Author(s):  
Namratha Urs ◽  
Sahar Behpour ◽  
Angie Georgaras ◽  
Mark V. Albert
Keyword(s):  
Receptive Field ◽  
Learning Strategies ◽  
Sensory Processing ◽  
Neural Development ◽  
Neural Coding ◽  
Receptive Fields ◽  
Principal Component ◽  
Component Analysis ◽  
Neural Processing ◽  
Efficient Computation

AbstractSensory processing relies on efficient computation driven by a combination of low-level unsupervised, statistical structural learning, and high-level task-dependent learning. In the earliest stages of sensory processing, sparse and independent coding strategies are capable of modeling neural processing using the same coding strategy with only a change in the input (e.g., grayscale images, color images, and audio). We present a consolidated review of Independent Component Analysis (ICA) as an efficient neural coding scheme with the ability to model early visual and auditory neural processing. We created a self-contained, accessible Jupyter notebook using Python to demonstrate the efficient coding principle for different modalities following a consistent five-step strategy. For each modality, derived receptive field models from natural and non-natural inputs are contrasted, demonstrating how neural codes are not produced when the inputs sufficiently deviate from those animals were evolved to process. Additionally, the demonstration shows that ICA produces more neurally-appropriate receptive field models than those based on common compression strategies, such as Principal Component Analysis. The five-step strategy not only produces neural-like models but also promotes reuse of code to emphasize the input-agnostic nature where each modality can be modeled with only a change in inputs. This notebook can be used to readily observe the links between unsupervised machine learning strategies and early sensory neuroscience, improving our understanding of flexible data-driven neural development in nature and future applications.

scholarly journals The optimal spatial arrangement of ON and OFF receptive fields

2021 ◽  
Author(s):  
Na Young Jun ◽  
Greg Field ◽  
John Pearson
Keyword(s):  
Neural Coding ◽  
Coding Theory ◽  
Receptive Fields ◽  
Spatial Arrangement ◽  
Optimal Arrangement ◽  
Natural Stimuli ◽  
Signal Stimulus ◽  
Theoretical Predictions ◽  
On And Off Pathways ◽  
Contrast Response

Many sensory systems utilize parallel ON and OFF pathways that signal stimulus increments and decrements, respectively. These pathways consist of ensembles or grids of ON and OFF detectors spanning sensory space. Yet encoding by opponent pathways raises a question: How should grids of ON and OFF detectors be arranged to optimally encode natural stimuli? We investigated this question using a model of the retina guided by efficient coding theory. Specifically, we optimized spatial receptive fields and contrast response functions to encode natural images given noise and constrained firing rates. We find that the optimal arrangement of ON and OFF receptive fields exhibits a second-order phase transition between aligned and anti-aligned grids. The preferred phase depends on detector noise and the statistical structure of the natural stimuli. These results reveal that noise and stimulus statistics produce qualitative shifts in neural coding strategies and provide novel theoretical predictions for the configuration of opponent pathways in the nervous system.

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