A spatiotemporal mechanism of visual attention: Superdiffusive motion and theta oscillations of neural population activity patterns

Chen, Guozhang; Gong, Pulin

doi:10.1126/sciadv.abl4995

Cited by 16 publications

(18 citation statements)

References 84 publications

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“…In some of these environmental sampling tasks, such as eye movement-based sampling of natural scenes, the existence of Lévy-like motion has been reported 67 . These observations lead us to predict that our FNS-based probabilistic computation might be the underlying computational mechanism of these cognitive functions; indeed, recently we have successfully applied FNS dynamics to explain key neural and behavioral effects of visual attention 51 . Furthermore, it is interesting to note that the phase of theta oscillations often modulates the amplitude of gamma fluctuations 68 .…”

Section: Discussionmentioning

confidence: 90%

“…Only in State II, the CoM of the pattern exhibits fractional Lévy motion with small steps occasionally interrupted by long jumps (Fig. 1 a) 51 , as indicated by a tail index of α < 2 with the trough of α coinciding with the transition point at ξ = ξ c = 3.4. This value of I–E ratio ( ξ c = 3.4) is quantitatively consistent with that measured in the visual cortex of awake mice 53 .…”

Section: Resultsmentioning

confidence: 98%

“…Interestingly, these oscillations ride on top of a 1/f arrhythmic component. Certain oscillatory components such as theta accompanied by 1/f-like activity have been widely observed in the cortex 20 , 21 ; they are particularly relevant for cognitive functions such as visual-spatial attention 51 , 52 . These results provide further neurophysiological validity of our circuit model of FNS.…”

Section: Resultsmentioning

confidence: 99%

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Fractional neural sampling as a theory of spatiotemporal probabilistic computations in neural circuits

Gong

2022

Nat Commun

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A range of perceptual and cognitive processes have been characterized from the perspective of probabilistic representations and inference. To understand the neural circuit mechanism underlying these probabilistic computations, we develop a theory based on complex spatiotemporal dynamics of neural population activity. We first implement and explore this theory in a biophysically realistic, spiking neural circuit. Population activity patterns emerging from the circuit capture realistic variability or fluctuations of neural dynamics both in time and in space. These activity patterns implement a type of probabilistic computations that we name fractional neural sampling (FNS). We further develop a mathematical model to reveal the algorithmic nature of FNS and its computational advantages for representing multimodal distributions, a major challenge faced by existing theories. We demonstrate that FNS provides a unified account of a diversity of experimental observations of neural spatiotemporal dynamics and perceptual processes such as visual perception inference, and that FNS makes experimentally testable predictions.

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Section: Discussionmentioning

confidence: 90%

Section: Resultsmentioning

confidence: 98%

Section: Resultsmentioning

confidence: 99%

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Fractional neural sampling as a theory of spatiotemporal probabilistic computations in neural circuits

Gong

2022

Nat Commun

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“…Notably, this discussion does not lead to a conclusion that SNNs are fundamentally inferior to ANNs in performing attention. On the contrary, the unique characteristics of SNNs can be further investigated to implement various attention mechanisms (Chen and Gong, 2022 ). In this work, popular ReLU-based ANN neurons are adopted in the implementation of the attention generator.…”

Section: Methodsmentioning

confidence: 99%

Enhancing spiking neural networks with hybrid top-down attention

Liu

Zhao

2022

Front. Neurosci.

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As the representatives of brain-inspired models at the neuronal level, spiking neural networks (SNNs) have shown great promise in processing spatiotemporal information with intrinsic temporal dynamics. SNNs are expected to further improve their robustness and computing efficiency by introducing top-down attention at the architectural level, which is crucial for the human brain to support advanced intelligence. However, this attempt encounters difficulties in optimizing the attention in SNNs largely due to the lack of annotations. Here, we develop a hybrid network model with a top-down attention mechanism (HTDA) by incorporating an artificial neural network (ANN) to generate attention maps based on the features extracted by a feedforward SNN. The attention map is then used to modulate the encoding layer of the SNN so that it focuses on the most informative sensory input. To facilitate direct learning of attention maps and avoid labor-intensive annotations, we propose a general principle and a corresponding weakly-supervised objective, which promotes the HTDA model to utilize an integral and small subset of the input to give accurate predictions. On this basis, the ANN and the SNN can be jointly optimized by surrogate gradient descent in an end-to-end manner. We comprehensively evaluated the HTDA model on object recognition tasks, which demonstrates strong robustness to adversarial noise, high computing efficiency, and good interpretability. On the widely-adopted CIFAR-10, CIFAR-100, and MNIST benchmarks, the HTDA model reduces firing rates by up to 50% and improves adversarial robustness by up to 10% with comparable or better accuracy compared with the state-of-the-art SNNs. The HTDA model is also verified on dynamic neuromorphic datasets and achieves consistent improvements. This study provides a new way to boost the performance of SNNs by employing a hybrid top-down attention mechanism.

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“…141 Moreover, the interaction of theta and gamma oscillations (eg, thetagamma coupling) my play a key role in visual selective and sustained attention. 142,143 In this context, low-frequency oscillations in the theta range may play a crucial role in bringing together local information processing by neurons engaged in high-frequency oscillatory activity in the gamma range. Individual neurons make their contribution to global gamma oscillations at specific phases of ongoing global theta rhythms.…”

Section: Cannabinoids and Neural Oscillations: Clinical Implications ...mentioning

confidence: 99%

The Relationship Between Cannabinoids and Neural Oscillations: How Cannabis Disrupts Sensation, Perception, and Cognition

et al. 2022

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Disruptions in neural oscillations are believed to be one critical mechanism by which cannabinoids, such as delta-9-tetrahyrdrocannabinol (THC; the primary psychoactive constituent of cannabis), perturbs brain function. Here we briefly review the role of synchronized neural activity, particularly in the gamma (30–80 Hz) and theta (4–7 Hz) frequency range, in sensation, perception, and cognition. This is followed by a review of clinical studies utilizing electroencephalography (EEG) which have demonstrated that both chronic and acute cannabinoid exposure disrupts neural oscillations in humans. We also offer a hypothetical framework through which endocannabinoids modulate neural synchrony at the network level. This also includes speculation on how both chronic and acute cannabinoids disrupt functionally relevant neural oscillations by altering the fine tuning of oscillations and the inhibitory/excitatory balance of neural circuits. Finally, we highlight important clinical implications of such oscillatory disruptions, such as the potential relationship between cannabis use, altered neural synchrony, and disruptions in sensation, perception, and cognition, which are perturbed in disorders such as schizophrenia.

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A spatiotemporal mechanism of visual attention: Superdiffusive motion and theta oscillations of neural population activity patterns

Cited by 16 publications

References 84 publications

Fractional neural sampling as a theory of spatiotemporal probabilistic computations in neural circuits

Fractional neural sampling as a theory of spatiotemporal probabilistic computations in neural circuits

Enhancing spiking neural networks with hybrid top-down attention

The Relationship Between Cannabinoids and Neural Oscillations: How Cannabis Disrupts Sensation, Perception, and Cognition

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