scholarly journals Emergence of opposite neurons in a decentralized firing-rate model of multisensory integration

2019 ◽  
Author(s):  
Xueyan Niu ◽  
Ho Yin Chau ◽  
Tai Sing Lee ◽  
Wen-Hao Zhang
Keyword(s):  
Computational Model ◽  
Firing Rate ◽  
Multisensory Integration ◽  
Hebbian Learning ◽  
Rate Model ◽  
New Model ◽  
Medial Superior Temporal ◽  
Learning Rules ◽  
Vestibular Cues ◽  
Self Motion

AbstractMultisensory integration areas such as dorsal medial superior temporal (MSTd) and ventral intraparietal (VIP) areas in macaques combine visual and vestibular cues to produce better estimates of self-motion. Congruent and opposite neurons, two types of neurons found in these areas, prefer congruent inputs and opposite inputs from the two modalities, respectively. A recently proposed computational model of congruent and opposite neurons reproduces their tuning properties and shows that congruent neurons optimally integrate information while opposite neurons compute disparity information. However, the connections in the network are fixed rather than learned, and in fact the connections of opposite neurons, as we will show, cannot arise from Hebbian learning rules. We therefore propose a new model of multisensory integration in which congruent neurons and opposite neurons emerge through Hebbian and anti-Hebbian learning rules, and show that these neurons exhibit experimentally observed tuning properties.

scholarly journals Emergence of opposite neurons in a firing-rate model of multisensory integration

2019 ◽  
Author(s):  
Ho Yin Chau ◽  
Wen-Hao Zhang ◽  
Tai Sing Lee
Keyword(s):  
Multisensory Integration ◽  
Neural Circuit ◽  
Hebbian Learning ◽  
Medial Superior Temporal ◽  
Learning Rules ◽  
Vestibular Cues ◽  
The One ◽  
Von Mises ◽  
Integrated Or ◽  
Self Motion

ABSTRACTOpposite neurons, found in macaque dorsal medial superior temporal (MSTd) and ventral intraparietal (VIP) areas, combine visual and vestibular cues of self-motion in opposite ways. A neural circuit recently proposed utilizes opposite neurons to perform causal inference and decide whether the visual and vestibular cues in MSTd and VIP should be integrated or segregated. However, it is unclear how these opposite connections can be formed with biologically realistic learning rules. We propose a network model capable of learning these opposite neurons, using Hebbian and Anti-Hebbian learning rules. The learned neurons are topographically organized and have von Mises-shaped feedforward connections, with tuning properties characteristic of opposite neurons. Our purpose is two-fold: on the one hand, we provide a circuit-level mechanism that explains the properties and formation of opposite neurons; on the other hand, we present a way to extend current theories of multisensory integration to account for appropriate segregation of sensory cues.

Examining the Effect of Age on Visual–Vestibular Self-Motion Perception Using a Driving Paradigm

Perception ◽  
2016 ◽  
Vol 46 (5) ◽  
pp. 566-585 ◽  
Author(s):  
Robert Ramkhalawansingh ◽  
Behrang Keshavarz ◽  
Bruce Haycock ◽  
Saba Shahab ◽  
Jennifer L. Campos
Keyword(s):  
Older Adults ◽  
Motion Perception ◽  
Multisensory Integration ◽  
Visual Cues ◽  
Lateral Position ◽  
Control Group ◽  
Younger Adults ◽  
Age Related ◽  
Vestibular Cues ◽  
Self Motion

Previous psychophysical research has examined how younger adults and non-human primates integrate visual and vestibular cues to perceive self-motion. However, there is much to be learned about how multisensory self-motion perception changes with age, and how these changes affect performance on everyday tasks involving self-motion. Evidence suggests that older adults display heightened multisensory integration compared with younger adults; however, few previous studies have examined this for visual–vestibular integration. To explore age differences in the way that visual and vestibular cues contribute to self-motion perception, we had younger and older participants complete a basic driving task containing visual and vestibular cues. We compared their performance against a previously established control group that experienced visual cues alone. Performance measures included speed, speed variability, and lateral position. Vestibular inputs resulted in more precise speed control among older adults, but not younger adults, when traversing curves. Older adults demonstrated more variability in lateral position when vestibular inputs were available versus when they were absent. These observations align with previous evidence of age-related differences in multisensory integration and demonstrate that they may extend to visual–vestibular integration. These findings may have implications for vehicle and simulator design when considering older users.

scholarly journals Primate Extrastriate Cortical Area MST: A Gateway between Sensation and Cognition

2021 ◽  
Author(s):  
Benedict Wild ◽  
Stefan Treue
Keyword(s):  
Visual Cortex ◽  
Temporal Cortex ◽  
Receptive Fields ◽  
Sensory Information ◽  
Action Planning ◽  
Motion Patterns ◽  
Medial Superior Temporal ◽  
Vestibular Cues ◽  
Primate Visual Cortex ◽  
Self Motion

Primate visual cortex consists of dozens of distinct brain areas, each providing a highly specialized component to the sophisticated task of encoding the incoming sensory information and creating a representation of our visual environment that underlies our perception and action. One such area is the medial superior temporal cortex (MST), a motion-sensitive, direction-selective part of the primate visual cortex. It receives most of its input from the middle temporal (MT) area, but MST cells have larger receptive fields and respond to more complex motion patterns. The finding that MST cells are tuned for optic flow patterns has led to the suggestion that the area plays an important role in the perception of self-motion. This hypothesis has received further support from studies showing that some MST cells also respond selectively to vestibular cues. Furthermore, the area is part of a network that controls the planning and execution of smooth pursuit eye movements and its activity is modulated by cognitive factors, such as attention and working memory. This review of more than 90 studies focuses on providing clarity of the heterogeneous findings on MST in the macaque cortex and its putative homolog in the human cortex. From this analysis of the unique anatomical and functional position in the hierarchy of areas and processing steps in primate visual cortex, MST emerges as a gateway between perception, cognition, and action planning. Given this pivotal role, this area represents an ideal model system for the transition from sensation to cognition.

Impacts of Rotation Axis and Frequency on Vestibular Perceptual Thresholds

2022 ◽  
pp. 1-29
Author(s):  
Andrew R. Wagner ◽  
Megan J. Kobel ◽  
Daniel M. Merfeld
Keyword(s):  
Multisensory Integration ◽  
Rotation Axis ◽  
Semicircular Canals ◽  
Rotation Velocity ◽  
Pass Filter ◽  
Motion Cues ◽  
Left Posterior ◽  
Rotation Plane ◽  
Self Motion ◽  
Lower Frequencies

Abstract In an effort to characterize the factors influencing the perception of self-motion rotational cues, vestibular self-motion perceptual thresholds were measured in 14 subjects for rotations in the roll and pitch planes, as well as in the planes aligned with the anatomic orientation of the vertical semicircular canals (i.e., left anterior, right posterior; LARP, and right anterior, left posterior; RALP). To determine the multisensory influence of concurrent otolith cues, within each plane of motion, thresholds were measured at four discrete frequencies for rotations about earth-horizontal (i.e., tilts; EH) and earth-vertical axes (i.e., head positioned in the plane of the rotation; EV). We found that the perception of rotations, stimulating primarily the vertical canals, was consistent with the behavior of a high-pass filter for all planes of motion, with velocity thresholds increasing at lower frequencies of rotation. In contrast, tilt (i.e, EH rotation) velocity thresholds, stimulating both the canals and otoliths (i.e., multisensory integration), decreased at lower frequencies and were significantly lower than earth-vertical rotation thresholds at each frequency below 2 Hz. These data suggest that multisensory integration of otolithic gravity cues with semicircular canal rotation cues enhances perceptual precision for tilt motions at frequencies below 2 Hz. We also showed that rotation thresholds, at least partially, were dependent on the orientation of the rotation plane relative to the anatomical alignment of the vertical canals. Collectively these data provide the first comprehensive report of how frequency and axis of rotation influence perception of rotational self-motion cues stimulating the vertical canals.

scholarly journals Logarithmic distributions prove that intrinsic learning is Hebbian

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1222 ◽  
Author(s):  
Gabriele Scheler
Keyword(s):  
Adaptive Learning ◽  
Hebbian Learning ◽  
Neural Model ◽  
Intrinsic Excitability ◽  
Logarithmic Scale ◽  
Brain Areas ◽  
Lognormal Distributions ◽  
Learning Rules ◽  
Heavy Tailed ◽  
The Difference

In this paper, we present data for the lognormal distributions of spike rates, synaptic weights and intrinsic excitability (gain) for neurons in various brain areas, such as auditory or visual cortex, hippocampus, cerebellum, striatum, midbrain nuclei. We find a remarkable consistency of heavy-tailed, specifically lognormal, distributions for rates, weights and gains in all brain areas examined. The difference between strongly recurrent and feed-forward connectivity (cortex vs. striatum and cerebellum), neurotransmitter (GABA (striatum) or glutamate (cortex)) or the level of activation (low in cortex, high in Purkinje cells and midbrain nuclei) turns out to be irrelevant for this feature. Logarithmic scale distribution of weights and gains appears to be a general, functional property in all cases analyzed. We then created a generic neural model to investigate adaptive learning rules that create and maintain lognormal distributions. We conclusively demonstrate that not only weights, but also intrinsic gains, need to have strong Hebbian learning in order to produce and maintain the experimentally attested distributions. This provides a solution to the long-standing question about the type of plasticity exhibited by intrinsic excitability.

scholarly journals How multisensory neurons solve causal inference

2021 ◽  
Vol 118 (32) ◽  
pp. e2106235118
Author(s):  
Reuben Rideaux ◽  
Katherine R. Storrs ◽  
Guido Maiello ◽  
Andrew E. Welchman
Keyword(s):  
Motion Estimation ◽  
Causal Inference ◽  
Visual Motion ◽  
Temporal Area ◽  
Medial Superior Temporal ◽  
Electrophysiological Recordings ◽  
Visual Motion Cues ◽  
Integrated Or ◽  
Self Motion ◽  
The Brain

Sitting in a static railway carriage can produce illusory self-motion if the train on an adjoining track moves off. While our visual system registers motion, vestibular signals indicate that we are stationary. The brain is faced with a difficult challenge: is there a single cause of sensations (I am moving) or two causes (I am static, another train is moving)? If a single cause, integrating signals produces a more precise estimate of self-motion, but if not, one cue should be ignored. In many cases, this process of causal inference works without error, but how does the brain achieve it? Electrophysiological recordings show that the macaque medial superior temporal area contains many neurons that encode combinations of vestibular and visual motion cues. Some respond best to vestibular and visual motion in the same direction (“congruent” neurons), while others prefer opposing directions (“opposite” neurons). Congruent neurons could underlie cue integration, but the function of opposite neurons remains a puzzle. Here, we seek to explain this computational arrangement by training a neural network model to solve causal inference for motion estimation. Like biological systems, the model develops congruent and opposite units and recapitulates known behavioral and neurophysiological observations. We show that all units (both congruent and opposite) contribute to motion estimation. Importantly, however, it is the balance between their activity that distinguishes whether visual and vestibular cues should be integrated or separated. This explains the computational purpose of puzzling neural representations and shows how a relatively simple feedforward network can solve causal inference.

Reduced Representation by Neural Networks with Restricted Receptive Fields

1995 ◽  
Vol 7 (3) ◽  
pp. 507-517 ◽  
Author(s):  
Marco Idiart ◽  
Barry Berk ◽  
L. F. Abbott
Keyword(s):  
Neural Networks ◽  
Biological Networks ◽  
Input Data ◽  
Hebbian Learning ◽  
Receptive Fields ◽  
Principal Component ◽  
Data Sets ◽  
Reduced Representation ◽  
Learning Rules ◽  
Reduced Representations

Model neural networks can perform dimensional reductions of input data sets using correlation-based learning rules to adjust their weights. Simple Hebbian learning rules lead to an optimal reduction at the single unit level but result in highly redundant network representations. More complex rules designed to reduce or remove this redundancy can develop optimal principal component representations, but they are not very compelling from a biological perspective. Neurons in biological networks have restricted receptive fields limiting their access to the input data space. We find that, within this restricted receptive field architecture, simple correlation-based learning rules can produce surprisingly efficient reduced representations. When noise is present, the size of the receptive fields can be optimally tuned to maximize the accuracy of reconstructions of input data from a reduced representation.

An Approach to Artificial Concept Learning Based on Human Concept Learning by Using Artificial Neural Networks

2011 ◽  
pp. 130-145
Author(s):  
Enrique Mérida-Casermeiro ◽  
Domingo López-Rodríguez ◽  
J.M. Ortiz-de-Lazcano-Lobato
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Associative Memory ◽  
Concept Learning ◽  
Energy Function ◽  
Hebbian Learning ◽  
Clustering Algorithms ◽  
Network Capacity ◽  
New Model

In this chapter, two important issues concerning associative memory by neural networks are studied: a new model of hebbian learning, as well as the effect of the network capacity when retrieving patterns and performing clustering tasks. Particularly, an explanation of the energy function when the capacity is exceeded: the limitation in pattern storage implies that similar patterns are going to be identified by the network, therefore forming different clusters. This ability can be translated as an unsupervised learning of pattern clusters, with one major advantage over most clustering algorithms: the number of data classes is automatically learned, as confirmed by the experiments. Two methods to reinforce learning are proposed to improve the quality of the clustering, by enhancing the learning of patterns relationships. As a related issue, a study on the net capacity, depending on the number of neurons and possible outputs, is presented, and some interesting conclusions are commented.

Difficulties with synaptic theory of learning and memory and possible remedies

2000 ◽  
Vol 23 (4) ◽  
pp. 550-551
Author(s):  
Mikhail N. Zhadin
Keyword(s):  
Cerebral Cortex ◽  
Associative Learning ◽  
Learning And Memory ◽  
Hebbian Learning ◽  
Behavioral Responses ◽  
Learning Rules ◽  
The Brain

The absence of a clear influence of an animal's behavioral responses to Hebbian associative learning in the cerebral cortex requires some changes in the Hebbian learning rules. The participation of the brain monoaminergic systems in Hebbian associative learning is considered.

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