Sensory cortex is optimised for prediction of future input

Neurons in sensory cortex are tuned to diverse features in natural scenes. But what determines which features neurons become selective to? Here we explore the idea that neuronal selectivity is optimised to represent features in the recent past of sensory input that best predict immediate future inputs. We tested this hypothesis using simple feedforward neural networks, which were trained to predict the next few video or audio frames in clips of natural scenes. The networks developed receptive fields that closely matched those of real cortical neurons, including the oriented spatial tuning of primary visual cortex, the frequency selectivity of primary auditory cortex and, most notably, in their temporal tuning properties. Furthermore, the better a network predicted future inputs the more closely its receptive fields tended to resemble those in the brain. This suggests that sensory processing is optimised to extract those features with the most capacity to predict future input.Impact statementPrediction of future input explains diverse neural tuning properties in sensory cortex.

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Sensory cortex is optimized for prediction of future input

eLife ◽

10.7554/elife.31557 ◽

2018 ◽

Vol 7 ◽

Cited By ~ 13

Author(s):

Yosef Singer ◽

Yayoi Teramoto ◽

Ben DB Willmore ◽

Jan WH Schnupp ◽

Andrew J King ◽

...

Keyword(s):

Neural Networks ◽

Sensory Processing ◽

Cortical Neurons ◽

Mammalian Species ◽

Receptive Fields ◽

Primary Auditory Cortex ◽

Feedforward Neural Networks ◽

Sensory Cortex ◽

Natural Scenes ◽

The Brain

Neurons in sensory cortex are tuned to diverse features in natural scenes. But what determines which features neurons become selective to? Here we explore the idea that neuronal selectivity is optimized to represent features in the recent sensory past that best predict immediate future inputs. We tested this hypothesis using simple feedforward neural networks, which were trained to predict the next few moments of video or audio in clips of natural scenes. The networks developed receptive fields that closely matched those of real cortical neurons in different mammalian species, including the oriented spatial tuning of primary visual cortex, the frequency selectivity of primary auditory cortex and, most notably, their temporal tuning properties. Furthermore, the better a network predicted future inputs the more closely its receptive fields resembled those in the brain. This suggests that sensory processing is optimized to extract those features with the most capacity to predict future input.

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Reorganization in processing of spectral and temporal input in the rat posterior auditory field induced by environmental enrichment

Journal of Neurophysiology ◽

10.1152/jn.01057.2010 ◽

2012 ◽

Vol 107 (5) ◽

pp. 1457-1475 ◽

Cited By ~ 21

Author(s):

Vikram Jakkamsetti ◽

Kevin Q. Chang ◽

Michael P. Kilgard

Keyword(s):

Environmental Enrichment ◽

Sensory Input ◽

Response Times ◽

Neuronal Response ◽

Receptive Fields ◽

Primary Auditory Cortex ◽

Response Strength ◽

Sensory Cortex ◽

Primary Sensory Cortex ◽

Auditory Field

Environmental enrichment induces powerful changes in the adult cerebral cortex. Studies in primary sensory cortex have observed that environmental enrichment modulates neuronal response strength, selectivity, speed of response, and synchronization to rapid sensory input. Other reports suggest that nonprimary sensory fields are more plastic than primary sensory cortex. The consequences of environmental enrichment on information processing in nonprimary sensory cortex have yet to be studied. Here we examine physiological effects of enrichment in the posterior auditory field (PAF), a field distinguished from primary auditory cortex (A1) by wider receptive fields, slower response times, and a greater preference for slowly modulated sounds. Environmental enrichment induced a significant increase in spectral and temporal selectivity in PAF. PAF neurons exhibited narrower receptive fields and responded significantly faster and for a briefer period to sounds after enrichment. Enrichment increased time-locking to rapidly successive sensory input in PAF neurons. Compared with previous enrichment studies in A1, we observe a greater magnitude of reorganization in PAF after environmental enrichment. Along with other reports observing greater reorganization in nonprimary sensory cortex, our results in PAF suggest that nonprimary fields might have a greater capacity for reorganization compared with primary fields.

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Coherent Electrical Activity Between Vibrissa Sensory Areas of Cerebellum and Neocortex Is Enhanced During Free Whisking

Journal of Neurophysiology ◽

10.1152/jn.00229.2001 ◽

2002 ◽

Vol 87 (4) ◽

pp. 2137-2148 ◽

Cited By ~ 70

Author(s):

Sean M. O'Connor ◽

Rune W. Berg ◽

David Kleinfeld

Keyword(s):

Electrical Activity ◽

Sensory Input ◽

Field Potential ◽

Sensory Cortex ◽

Brain Areas ◽

Low Coherence ◽

Primary Sensory Cortex ◽

High Level ◽

The Brain ◽

Local Field Potential Lfp

We tested if coherent signaling between the sensory vibrissa areas of cerebellum and neocortex in rats was enhanced as they whisked in air. Whisking was accompanied by 5- to 15-Hz oscillations in the mystatial electromyogram, a measure of vibrissa position, and by 5- to 20-Hz oscillations in the differentially recorded local field potential (∇LFP) within the vibrissa area of cerebellum and within the ∇LFP of primary sensory cortex. We observed that only 10% of the activity in either cerebellum or sensory neocortex was significantly phase-locked to rhythmic motion of the vibrissae; the extent of this modulation is in agreement with the results from previous single-unit measurements in sensory neocortex. In addition, we found that 40% of the activity in the vibrissa areas of cerebellum and neocortex was significantly coherent during periods of whisking. The relatively high level of coherence between these two brain areas, in comparison with their relatively low coherence with whisking per se, implies that the vibrissa areas of cerebellum and neocortex communicate in a manner that is incommensurate with whisking. To the extent that the vibrissa areas of cerebellum and neocortex communicate over the same frequency band as that used by whisking, these areas must multiplex electrical activity that is internal to the brain with activity that is that phase-locked to vibrissa sensory input.

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Influence of Context and Behavior on Stimulus Reconstruction From Neural Activity in Primary Auditory Cortex

Journal of Neurophysiology ◽

10.1152/jn.91128.2008 ◽

2009 ◽

Vol 102 (6) ◽

pp. 3329-3339 ◽

Cited By ~ 88

Author(s):

Nima Mesgarani ◽

Stephen V. David ◽

Jonathan B. Fritz ◽

Shihab A. Shamma

Keyword(s):

Auditory Cortex ◽

Cortical Neurons ◽

Receptive Fields ◽

Primary Auditory Cortex ◽

Neural Population ◽

Stimulus Context ◽

Sensory Stimuli ◽

Complex Sounds ◽

Population Responses ◽

Stimulus Reconstruction

Population responses of cortical neurons encode considerable details about sensory stimuli, and the encoded information is likely to change with stimulus context and behavioral conditions. The details of encoding are difficult to discern across large sets of single neuron data because of the complexity of naturally occurring stimulus features and cortical receptive fields. To overcome this problem, we used the method of stimulus reconstruction to study how complex sounds are encoded in primary auditory cortex (AI). This method uses a linear spectro-temporal model to map neural population responses to an estimate of the stimulus spectrogram, thereby enabling a direct comparison between the original stimulus and its reconstruction. By assessing the fidelity of such reconstructions from responses to modulated noise stimuli, we estimated the range over which AI neurons can faithfully encode spectro-temporal features. For stimuli containing statistical regularities (typical of those found in complex natural sounds), we found that knowledge of these regularities substantially improves reconstruction accuracy over reconstructions that do not take advantage of this prior knowledge. Finally, contrasting stimulus reconstructions under different behavioral states showed a novel view of the rapid changes in spectro-temporal response properties induced by attentional and motivational state.

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Input dependent modulation of olfactory bulb activity by GABAergic basal forebrain projections

10.1101/2020.03.29.014191 ◽

2020 ◽

Cited By ~ 1

Author(s):

Erik Böhm ◽

Daniela Brunert ◽

Markus Rothermel

Keyword(s):

Olfactory Bulb ◽

Sensory Processing ◽

Basal Forebrain ◽

Sensory Input ◽

Sensory Information ◽

Neuron Activity ◽

Differential Modulation ◽

Cholinergic Basal Forebrain ◽

The Brain ◽

Cholinergic Projections

AbstractBasal forebrain modulation of central circuits is associated with active sensation, attention and learning. While cholinergic modulations have been studied extensively the effect of non-cholinergic basal forebrain subpopulations on sensory processing remains largely unclear. Here, we directly compare optogenetic manipulation effects of two major basal forebrain subpopulations on principal neuron activity in an early sensory processing area, i.e. mitral/tufted cells (MTCs) in the olfactory bulb. In contrast to cholinergic projections, which consistently increased MTC firing, activation of GABAergic fibers from basal forebrain to the olfactory bulb lead to differential modulation effects: while spontaneous MTC activity is mainly inhibited, odor evoked firing is predominantly enhanced. Moreover, sniff triggered averages revealed an enhancement of maximal sniff evoked firing amplitude and an inhibition of firing rates outside the maximal sniff phase. These findings demonstrate that GABAergic neuromodulation affects MTC firing in a bimodal, sensory-input dependent way, suggesting that GABAergic basal forebrain modulation could be an important factor in attention mediated filtering of sensory information to the brain.

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Network Architecture, Receptive Fields, and Neuromodulation: Computational and Functional Implications of Cholinergic Modulation in Primary Auditory Cortex

Journal of Neurophysiology ◽

10.1152/jn.00459.2006 ◽

2006 ◽

Vol 96 (6) ◽

pp. 2972-2983 ◽

Cited By ~ 14

Author(s):

Gabriel Soto ◽

Nancy Kopell ◽

Kamal Sen

Keyword(s):

Auditory Cortex ◽

Cortical Neurons ◽

Network Architecture ◽

Frequency Tuning ◽

Receptive Fields ◽

Primary Auditory Cortex ◽

Physiological Data ◽

Cholinergic Modulation ◽

Tuning Curves ◽

Intracortical Circuits

Two fundamental issues in auditory cortical processing are the relative importance of thalamocortical versus intracortical circuits in shaping response properties in primary auditory cortex (ACx), and how the effects of neuromodulators on these circuits affect dynamic changes in network and receptive field properties that enhance signal processing and adaptive behavior. To investigate these issues, we developed a computational model of layers III and IV (LIII/IV) of AI, constrained by anatomical and physiological data. We focus on how the local and global cortical architecture shape receptive fields (RFs) of cortical cells and on how different well-established cholinergic effects on the cortical network reshape frequency-tuning properties of cells in ACx. We identify key thalamocortical and intracortical circuits that strongly affect tuning curves of model cortical neurons and are also sensitive to cholinergic modulation. We then study how differential cholinergic modulation of network parameters change the tuning properties of our model cells and propose two different mechanisms: one intracortical (involving muscarinic receptors) and one thalamocortical (involving nicotinic receptors), which may be involved in rapid plasticity in ACx, as recently reported in a study by Fritz and coworkers.

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Toward a Theory of the Striate Cortex

Neural Computation ◽

10.1162/neco.1994.6.1.127 ◽

1994 ◽

Vol 6 (1) ◽

pp. 127-146 ◽

Cited By ~ 65

Author(s):

Zhaoping Li ◽

Joseph J. Atick

Keyword(s):

Cortical Neurons ◽

Striate Cortex ◽

Receptive Fields ◽

Cell Types ◽

Natural Scenes ◽

Cortical Cells ◽

Distinct Cell ◽

Special Cases ◽

Depth Dimension ◽

Spatial Locations

We explore the hypothesis that linear cortical neurons are concerned with building a particular type of representation of the visual world—one that not only preserves the information and the efficiency achieved by the retina, but in addition preserves spatial relationships in the input—both in the plane of vision and in the depth dimension. Focusing on the linear cortical cells, we classify all transforms having these properties. They are given by representations of the scaling and translation group and turn out to be labeled by rational numbers ‘(p + q)/p’ (p, q integers). Any given (p, q) predicts a set of receptive fields that comes at different spatial locations and scales (sizes) with a bandwidth of log2 [(p + q)/p] octaves and, most interestingly, with a diversity of ‘q’ cell varieties. The bandwidth affects the trade-off between preservation of planar and depth relations and, we think, should be selected to match structures in natural scenes. For bandwidths between 1 and 2 octaves, which are the ones we feel provide the best matching, we find for each scale a minimum of two distinct cell types that reside next to each other and in phase quadrature, that is, differ by 90° in the phases of their receptive fields, as are found in the cortex, they resemble the “even-symmetric” and “odd-symmetric” simple cells in special cases. An interesting consequence of the representations presented here is that the pattern of activation in the cells in response to a translation or scaling of an object remains the same but merely shifts its locus from one group of cells to another. This work also provides a new understanding of color coding changes from the retina to the cortex.

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A Dependency Capturing Code for Robust Object Representation

10.1101/662130 ◽

2019 ◽

Author(s):

Rishabh Raj ◽

Dar Dahlen ◽

Kyle Duyck ◽

C. Ron Yu

Keyword(s):

Sensory Processing ◽

Object Representation ◽

Learning Experience ◽

Receptive Fields ◽

Efficient Coding ◽

Sensory Inputs ◽

Redundant Feature ◽

Deep Layers ◽

Robust Face Recognition ◽

The Brain

AbstractThe brain has a remarkable ability to recognize objects from noisy or corrupted sensory inputs. How this cognitive robustness is achieved computationally remains unknown. We present a coding paradigm, which encodes structural dependence among features of the input and transforms various forms of the same input into the same representation. The paradigm, through dimensionally expanded representation and sparsity constraint, allows redundant feature coding to enhance robustness and is efficient in representing objects. We demonstrate consistent representations of visual and olfactory objects under conditions of occlusion, high noise or with corrupted coding units. Robust face recognition is achievable without deep layers or large training sets. The paradigm produces both complex and simple receptive fields depending on learning experience, thereby offers a unifying framework of sensory processing.One line abstractWe present a framework of efficient coding of objects as a combination of structurally dependent feature groups that is robust against noise and corruption.

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Brain Plasticity and Hand Surgery: an Overview

Journal of Hand Surgery (European Volume) ◽

10.1054/jhsb.1999.0339 ◽

2000 ◽

Vol 25 (3) ◽

pp. 242-252 ◽

Cited By ~ 106

Author(s):

G. LUNDBORG

Keyword(s):

Neural Network ◽

Surgical Procedures ◽

Sensory Input ◽

Brain Cortex ◽

Brain Plasticity ◽

Nerve Repair ◽

Hand Surgery ◽

Sensory Cortex ◽

Activity Dependent ◽

The Brain

The hand is an extension of the brain, and the hand is projected and represented in large areas of the motor and sensory cortex. The brain is a complicated neural network which continuously remodels itself as a result of changes in sensory input. Such synaptic reorganizational changes may be activity-dependent, based on alterations in hand activity and tactile experience, or a result of deafferentiation such as nerve injury or amputation. Inferior recovery of functional sensibility following nerve repair, as well as phantom experiences in virtual, amputated limbs are phenomena reflecting profound cortical reorganizational changes. Surgical procedures on the hand are always accompanied by synaptic reorganizational changes in the brain cortex, and the outcome from many hand surgical procedures is to a large extent dependent on brain plasticity.

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