Dendritic predictive coding: A theory of cortical computation with spiking neurons

Mikulasch, Fabian A.; Rudelt, Lucas; Wibral, Michael; Priesemann, Viola

doi:10.48550/arxiv.2205.05303

Cited by 6 publications

(11 citation statements)

References 68 publications

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“…Moreover, the network model in Fig. 7a challenges the prevalent view 72,74 that error signals are necessarily represented by distinct neural populations (or alternatively distinct dendritic compartments 75 ). While our network model supports the possibility of distinct error populations, we show that prediction errors could also be computed and conveyed by the same neurons representing other quantities, such as the motion sources, μ t , and even the structure, λ 2 t , using a distributed neural code.…”

Section: Discussionmentioning

confidence: 90%

Visual motion perception as online hierarchical inference

2022

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Identifying the structure of motion relations in the environment is critical for navigation, tracking, prediction, and pursuit. Yet, little is known about the mental and neural computations that allow the visual system to infer this structure online from a volatile stream of visual information. We propose online hierarchical Bayesian inference as a principled solution for how the brain might solve this complex perceptual task. We derive an online Expectation-Maximization algorithm that explains human percepts qualitatively and quantitatively for a diverse set of stimuli, covering classical psychophysics experiments, ambiguous motion scenes, and illusory motion displays. We thereby identify normative explanations for the origin of human motion structure perception and make testable predictions for future psychophysics experiments. The proposed online hierarchical inference model furthermore affords a neural network implementation which shares properties with motion-sensitive cortical areas and motivates targeted experiments to reveal the neural representations of latent structure.

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Section: Discussionmentioning

confidence: 90%

Visual motion perception as online hierarchical inference

2022

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“…Moreover, the network model in Fig. 6a challenges the prevalent view [72, 74] that error signals are necessarily represented by distinct neural populations (or alternatively distinct dendritic compartments [75]). While our network model supports the possibility of distinct error populations, we show that prediction errors could also be computed and conveyed by the same neurons representing other quantities, such as the motion sources, µ t , and even the structure, , using a distributed neural code.…”

Section: Discussionmentioning

confidence: 96%

Structure in motion: visual motion perception as online hierarchical inference

Bill

Gershman

Drugowitsch

2021

Preprint

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Identifying the structure of motion relations in the environment is critical for navigation, tracking, prediction, and pursuit. Yet, little is known about the mental and neural computations that allow the visual system to infer this structure online from a volatile stream of visual information. We propose online hierarchical Bayesian inference as a principled solution for how the brain might solve this complex perceptual task. We derive an online Expectation-Maximization algorithm that explains human percepts qualitatively and quantitatively for a diverse set of stimuli, covering classical psychophysics experiments, ambiguous motion scenes, and illusory motion displays. We thereby identify normative explanations for the origin of human motion structure perception and make testable predictions for new psychophysics experiments. The algorithm furthermore affords a neural network implementation which shares properties with motion-sensitive cortical areas and motivates a novel class of experiments to reveal the neural representations of latent structure.

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“…Another rewarding direction is to consider spiking neurons which are biologically more realistic [16]. The simple noise-free response dynamics (1) then will be replaced by the more complicated and stochastic integrate-and-fire dynamics of spiking neurons.…”

Section: Discussionmentioning

confidence: 99%

“…[13][14][15]). Whether special prediction-error computing neurons really exist in the brain is also a widely debated issue [16].…”

Section: Introductionmentioning

confidence: 99%

Lateral predictive coding revisited: Internal model, symmetry breaking, and response time

Huang¹,

Fan²,

Zhou³

et al. 2022

Preprint

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Predictive coding is a promising theoretical framework in neuroscience for understanding information transmission and perception. It posits that the brain perceives the external world through internal models and updates these models under the guidance of prediction errors. Previous studies on predictive coding emphasized top-down feedback interactions in hierarchical multi-layered networks but largely ignored lateral recurrent interactions. We perform analytical and numerical investigations in this work on the effects of single-layer lateral interactions. We consider a simple predictive response dynamics and run it on the MNIST dataset of hand-written digits. We find that learning will generally break the interaction symmetry between peer neurons, and that high input correlation between two neurons does not necessarily bring strong direct interactions between them. The optimized network responds to familiar input signals much faster than to novel or random inputs, and it significantly reduces the correlations between the output states of pairs of neurons.

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Dendritic predictive coding: A theory of cortical computation with spiking neurons

Cited by 6 publications

References 68 publications

Visual motion perception as online hierarchical inference

Visual motion perception as online hierarchical inference

Structure in motion: visual motion perception as online hierarchical inference

Lateral predictive coding revisited: Internal model, symmetry breaking, and response time

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