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Article ; Online: What is exactly the problem with panpsychism?

Pennartz, Cyriel M A

2022 Volume 45, Page(s) e57

Abstract: Merker et al.'s critique calls for a deeper analysis of panpsychism. In principle, the concept of integrated information can be applied to photodiodes and subatomic particles, but I suggest the main obstacle is the lack of any evidence to confirm the ... ...

Abstract	Merker et al.'s critique calls for a deeper analysis of panpsychism. In principle, the concept of integrated information can be applied to photodiodes and subatomic particles, but I suggest the main obstacle is the lack of any evidence to confirm the presence of consciousness. Also MRW's perspectivalist theory illustrates the difficulties in synthesizing a full-fledged theory of consciousness.
MeSH term(s)	Consciousness ; Humans
Language	English
Publishing date	2022-03-23
Publishing country	England
Document type	Journal Article ; Comment
ZDB-ID	423721-3
ISSN	1469-1825 ; 0140-525X
ISSN (online)	1469-1825
ISSN	0140-525X
DOI	10.1017/S0140525X21001916
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Zs.A 1470: Show issues

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Article ; Online: What is neurorepresentationalism? From neural activity and predictive processing to multi-level representations and consciousness.

Pennartz, Cyriel M A

Behavioural brain research

2022 Volume 432, Page(s) 113969

Abstract: ... constructing best-guess representations of sensations originating in our environment and body (Pennartz, 2015 ...

Abstract	This review provides an update on Neurorepresentationalism, a theoretical framework that defines conscious experience as multimodal, situational survey and explains its neural basis from brain systems constructing best-guess representations of sensations originating in our environment and body (Pennartz, 2015). It posits that conscious experience is characterized by five essential hallmarks: (i) multimodal richness, (ii) situatedness and immersion, (iii) unity and integration, (iv) dynamics and stability, and (v) intentionality. Consciousness is furthermore proposed to have a biological function, framed by the contrast between reflexes and habits (not requiring consciousness) versus goal-directed, planned behavior (requiring multimodal, situational survey). Conscious experience is therefore understood as a sensorily rich, spatially encompassing representation of body and environment, while we nevertheless have the impression of experiencing external reality directly. Contributions to understanding neural mechanisms underlying consciousness are derived from models for predictive processing, which are trained in an unsupervised manner, do not necessarily require overt action, and have been extended to deep neural networks. Even with predictive processing in place, however, the question remains why this type of neural network activity would give rise to phenomenal experience. Here, I propose to tackle the Hard Problem with the concept of multi-level representations which emergently give rise to multimodal, spatially wide superinferences corresponding to phenomenal experiences. Finally, Neurorepresentationalism is compared to other neural theories of consciousness, and its implications for defining indicators of consciousness in animals, artificial intelligence devices and immobile or unresponsive patients with disorders of consciousness are discussed.
MeSH term(s)	Animals ; Artificial Intelligence ; Brain ; Consciousness ; Neural Networks, Computer
Language	English
Publishing date	2022-06-16
Publishing country	Netherlands
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't
ZDB-ID	449927-x
ISSN	1872-7549 ; 0166-4328
ISSN (online)	1872-7549
ISSN	0166-4328
DOI	10.1016/j.bbr.2022.113969
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Zs.A 1599: Show issues

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Article ; Online: How deep is the brain? The shallow brain hypothesis.

Suzuki, Mototaka / Pennartz, Cyriel M A / Aru, Jaan

Nature reviews. Neuroscience

2023 Volume 24, Issue 12, Page(s) 778–791

Abstract: Deep learning and predictive coding architectures commonly assume that inference in neural networks is hierarchical. However, largely neglected in deep learning and predictive coding architectures is the neurobiological evidence that all hierarchical ... ...

Abstract	Deep learning and predictive coding architectures commonly assume that inference in neural networks is hierarchical. However, largely neglected in deep learning and predictive coding architectures is the neurobiological evidence that all hierarchical cortical areas, higher or lower, project to and receive signals directly from subcortical areas. Given these neuroanatomical facts, today's dominance of cortico-centric, hierarchical architectures in deep learning and predictive coding networks is highly questionable; such architectures are likely to be missing essential computational principles the brain uses. In this Perspective, we present the shallow brain hypothesis: hierarchical cortical processing is integrated with a massively parallel process to which subcortical areas substantially contribute. This shallow architecture exploits the computational capacity of cortical microcircuits and thalamo-cortical loops that are not included in typical hierarchical deep learning and predictive coding networks. We argue that the shallow brain architecture provides several critical benefits over deep hierarchical structures and a more complete depiction of how mammalian brains achieve fast and flexible computational capabilities.
MeSH term(s)	Animals ; Humans ; Brain ; Neural Networks, Computer ; Mammals
Language	English
Publishing date	2023-10-27
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	2034150-7
ISSN	1471-0048 ; 1471-0048 ; 1471-003X
ISSN (online)	1471-0048
ISSN	1471-0048 ; 1471-003X
DOI	10.1038/s41583-023-00756-z
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Zs.A 5473: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
Zs.MG 47: Show issues

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Article: Predictive coding with spiking neurons and feedforward gist signaling.

Lee, Kwangjun / Dora, Shirin / Mejias, Jorge F / Bohte, Sander M / Pennartz, Cyriel M A

Frontiers in computational neuroscience

2024 Volume 18, Page(s) 1338280

Abstract: Predictive coding (PC) is an influential theory in neuroscience, which suggests the existence of a cortical architecture that is constantly generating and updating predictive representations of sensory inputs. Owing to its hierarchical and generative ... ...

Abstract	Predictive coding (PC) is an influential theory in neuroscience, which suggests the existence of a cortical architecture that is constantly generating and updating predictive representations of sensory inputs. Owing to its hierarchical and generative nature, PC has inspired many computational models of perception in the literature. However, the biological plausibility of existing models has not been sufficiently explored due to their use of artificial neurons that approximate neural activity with firing rates in the continuous time domain and propagate signals synchronously. Therefore, we developed a spiking neural network for predictive coding (SNN-PC), in which neurons communicate using event-driven and asynchronous spikes. Adopting the hierarchical structure and Hebbian learning algorithms from previous PC neural network models, SNN-PC introduces two novel features: (1) a fast feedforward sweep from the input to higher areas, which generates a spatially reduced and abstract representation of input (i.e., a neural code for the gist of a scene) and provides a neurobiological alternative to an arbitrary choice of priors; and (2) a separation of positive and negative error-computing neurons, which counters the biological implausibility of a bi-directional error neuron with a very high baseline firing rate. After training with the MNIST handwritten digit dataset, SNN-PC developed hierarchical internal representations and was able to reconstruct samples it had not seen during training. SNN-PC suggests biologically plausible mechanisms by which the brain may perform perceptual inference and learning in an unsupervised manner. In addition, it may be used in neuromorphic applications that can utilize its energy-efficient, event-driven, local learning, and parallel information processing nature.
Language	English
Publishing date	2024-04-12
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2452964-3
ISSN	1662-5188
ISSN	1662-5188
DOI	10.3389/fncom.2024.1338280
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Article ; Online: Triple dissociation of visual, auditory and motor processing in mouse primary visual cortex.

Oude Lohuis, Matthijs N / Marchesi, Pietro / Olcese, Umberto / Pennartz, Cyriel M A

Nature neuroscience

2024 Volume 27, Issue 4, Page(s) 758–771

Abstract: Primary sensory cortices respond to crossmodal stimuli-for example, auditory responses are found in primary visual cortex (V1). However, it remains unclear whether these responses reflect sensory inputs or behavioral modulation through sound-evoked body ... ...

Abstract	Primary sensory cortices respond to crossmodal stimuli-for example, auditory responses are found in primary visual cortex (V1). However, it remains unclear whether these responses reflect sensory inputs or behavioral modulation through sound-evoked body movement. We address this controversy by showing that sound-evoked activity in V1 of awake mice can be dissociated into auditory and behavioral components with distinct spatiotemporal profiles. The auditory component began at approximately 27 ms, was found in superficial and deep layers and originated from auditory cortex. Sound-evoked orofacial movements correlated with V1 neural activity starting at approximately 80-100 ms and explained auditory frequency tuning. Visual, auditory and motor activity were expressed by different laminar profiles and largely segregated subsets of neuronal populations. During simultaneous audiovisual stimulation, visual representations remained dissociable from auditory-related and motor-related activity. This three-fold dissociability of auditory, motor and visual processing is central to understanding how distinct inputs to visual cortex interact to support vision.
MeSH term(s)	Animals ; Mice ; Acoustic Stimulation ; Primary Visual Cortex ; Photic Stimulation ; Visual Perception/physiology ; Auditory Cortex/physiology ; Auditory Perception/physiology
Language	English
Publishing date	2024-02-02
Publishing country	United States
Document type	Journal Article
ZDB-ID	1420596-8
ISSN	1546-1726 ; 1097-6256
ISSN (online)	1546-1726
ISSN	1097-6256
DOI	10.1038/s41593-023-01564-5
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Zs.A 4891: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
Zs.MG 67: Show issues

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Article ; Online: A novel task to investigate vibrotactile detection in mice.

Muller, Mariel / Pennartz, Cyriel M A / Bosman, Conrado A / Olcese, Umberto

PloS one

2023 Volume 18, Issue 4, Page(s) e0284735

Abstract: Throughout the last decades, understanding the neural mechanisms of sensory processing has been a key objective for neuroscientists. Many studies focused on uncovering the microcircuit-level architecture of somatosensation using the rodent whisker system ...

Abstract	Throughout the last decades, understanding the neural mechanisms of sensory processing has been a key objective for neuroscientists. Many studies focused on uncovering the microcircuit-level architecture of somatosensation using the rodent whisker system as a model. Although these studies have significantly advanced our understanding of tactile processing, the question remains to what extent the whisker system can provide results translatable to the human somatosensory system. To address this, we developed a restrained vibrotactile detection task involving the limb system in mice. A vibrotactile stimulus was delivered to the hindlimb of head-fixed mice, who were trained to perform a Go/No-go detection task. Mice were able to learn this task with satisfactory performance and with reasonably short training times. In addition, the task we developed is versatile, as it can be combined with diverse neuroscience methods. Thus, this study introduces a novel task to study the neuron-level mechanisms of tactile processing in a system other than the more commonly studied whisker system.
MeSH term(s)	Mice ; Humans ; Animals ; Touch ; Touch Perception ; Hindlimb ; Vibrissae ; Lower Extremity ; Somatosensory Cortex
Language	English
Publishing date	2023-04-20
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0284735
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Article: Local minimization of prediction errors drives learning of invariant object representations in a generative network model of visual perception.

Brucklacher, Matthias / Bohté, Sander M / Mejias, Jorge F / Pennartz, Cyriel M A

Frontiers in computational neuroscience

2023 Volume 17, Page(s) 1207361

Abstract: The ventral visual processing hierarchy of the cortex needs to fulfill at least two key functions: perceived objects must be mapped to high-level representations invariantly of the precise viewing conditions, and a generative model must be learned that ... ...

Abstract	The ventral visual processing hierarchy of the cortex needs to fulfill at least two key functions: perceived objects must be mapped to high-level representations invariantly of the precise viewing conditions, and a generative model must be learned that allows, for instance, to fill in occluded information guided by visual experience. Here, we show how a multilayered predictive coding network can learn to recognize objects from the bottom up and to generate specific representations via a top-down pathway through a single learning rule: the local minimization of prediction errors. Trained on sequences of continuously transformed objects, neurons in the highest network area become tuned to object identity invariant of precise position, comparable to inferotemporal neurons in macaques. Drawing on this, the dynamic properties of invariant object representations reproduce experimentally observed hierarchies of timescales from low to high levels of the ventral processing stream. The predicted faster decorrelation of error-neuron activity compared to representation neurons is of relevance for the experimental search for neural correlates of prediction errors. Lastly, the generative capacity of the network is confirmed by reconstructing specific object images, robust to partial occlusion of the inputs. By learning invariance from temporal continuity within a generative model, the approach generalizes the predictive coding framework to dynamic inputs in a more biologically plausible way than self-supervised networks with non-local error-backpropagation. This was achieved simply by shifting the training paradigm to dynamic inputs, with little change in architecture and learning rule from static input-reconstructing Hebbian predictive coding networks.
Language	English
Publishing date	2023-09-25
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2452964-3
ISSN	1662-5188
ISSN	1662-5188
DOI	10.3389/fncom.2023.1207361
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Article ; Online: How 'visual' is the visual cortex? The interactions between the visual cortex and other sensory, motivational and motor systems as enabling factors for visual perception.

Pennartz, Cyriel M A / Oude Lohuis, Matthijs N / Olcese, Umberto

Philosophical transactions of the Royal Society of London. Series B, Biological sciences

2023 Volume 378, Issue 1886, Page(s) 20220336

Abstract: The definition of the visual cortex is primarily based on the evidence that lesions of this area impair visual perception. However, this does not exclude that the visual cortex may process more information than of retinal origin alone, or that other ... ...

Abstract	The definition of the visual cortex is primarily based on the evidence that lesions of this area impair visual perception. However, this does not exclude that the visual cortex may process more information than of retinal origin alone, or that other brain structures contribute to vision. Indeed, research across the past decades has shown that non-visual information, such as neural activity related to reward expectation and value, locomotion, working memory and other sensory modalities, can modulate primary visual cortical responses to retinal inputs. Nevertheless, the function of this non-visual information is poorly understood. Here we review recent evidence, coming primarily from studies in rodents, arguing that non-visual and motor effects in visual cortex play a role in visual processing itself, for instance disentangling direct auditory effects on visual cortex from effects of sound-evoked orofacial movement. These findings are placed in a broader framework casting vision in terms of predictive processing under control of frontal, reward- and motor-related systems. In contrast to the prevalent notion that vision is exclusively constructed by the visual cortical system, we propose that visual percepts are generated by a larger network-the extended visual system-spanning other sensory cortices, supramodal areas and frontal systems. This article is part of the theme issue 'Decision and control processes in multisensory perception'.
MeSH term(s)	Motivation ; Visual Perception/physiology ; Visual Cortex/physiology ; Sound ; Causality
Language	English
Publishing date	2023-08-07
Publishing country	England
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't
ZDB-ID	208382-6
ISSN	1471-2970 ; 0080-4622 ; 0264-3839 ; 0962-8436
ISSN (online)	1471-2970
ISSN	0080-4622 ; 0264-3839 ; 0962-8436
DOI	10.1098/rstb.2022.0336
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Article ; Online: Theta Phase Entrainment of Single-Cell Spiking in Rat Somatosensory Barrel Cortex and Secondary Visual Cortex Is Enhanced during Multisensory Discrimination Behavior.

Ruikes, Thijs R / Fiorilli, Julien / Lim, Judith / Huis In 't Veld, Gerjan / Bosman, Conrado / Pennartz, Cyriel M A

eNeuro

2024 Volume 11, Issue 4

Abstract: Phase entrainment of cells by theta oscillations is thought to globally coordinate the activity of cell assemblies across different structures, such as the hippocampus and neocortex. This coordination is likely required for optimal processing of sensory ... ...

Abstract	Phase entrainment of cells by theta oscillations is thought to globally coordinate the activity of cell assemblies across different structures, such as the hippocampus and neocortex. This coordination is likely required for optimal processing of sensory input during recognition and decision-making processes. In quadruple-area ensemble recordings from male rats engaged in a multisensory discrimination task, we investigated phase entrainment of cells by theta oscillations in areas along the corticohippocampal hierarchy: somatosensory barrel cortex (S1BF), secondary visual cortex (V2L), perirhinal cortex (PER), and dorsal hippocampus (dHC). Rats discriminated between two 3D objects presented in tactile-only, visual-only, or both tactile and visual modalities. During task engagement, S1BF, V2L, PER, and dHC LFP signals showed coherent theta-band activity. We found phase entrainment of single-cell spiking activity to locally recorded as well as hippocampal theta activity in S1BF, V2L, PER, and dHC. While phase entrainment of hippocampal spikes to local theta oscillations occurred during sustained epochs of task trials and was nonselective for behavior and modality, somatosensory and visual cortical cells were only phase entrained during stimulus presentation, mainly in their preferred modality (S1BF, tactile; V2L, visual), with subsets of cells selectively phase-entrained during cross-modal stimulus presentation (S1BF: visual; V2L: tactile). This effect could not be explained by modulations of firing rate or theta amplitude. Thus, hippocampal cells are phase entrained during prolonged epochs, while sensory and perirhinal neurons are selectively entrained during sensory stimulus presentation, providing a brief time window for coordination of activity.
MeSH term(s)	Animals ; Male ; Theta Rhythm/physiology ; Somatosensory Cortex/physiology ; Visual Cortex/physiology ; Discrimination, Psychological/physiology ; Neurons/physiology ; Hippocampus/physiology ; Visual Perception/physiology ; Touch Perception/physiology ; Action Potentials/physiology ; Rats, Long-Evans ; Rats
Language	English
Publishing date	2024-04-26
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2800598-3
ISSN	2373-2822 ; 2373-2822
ISSN (online)	2373-2822
ISSN	2373-2822
DOI	10.1523/ENEURO.0180-23.2024
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Article: Deep Gated Hebbian Predictive Coding Accounts for Emergence of Complex Neural Response Properties Along the Visual Cortical Hierarchy.

Dora, Shirin / Bohte, Sander M / Pennartz, Cyriel M A

Frontiers in computational neuroscience

2021 Volume 15, Page(s) 666131

Abstract: Predictive coding provides a computational paradigm for modeling perceptual processing as the construction of representations accounting for causes of sensory inputs. Here, we developed a scalable, deep network architecture for predictive coding that is ... ...

Abstract	Predictive coding provides a computational paradigm for modeling perceptual processing as the construction of representations accounting for causes of sensory inputs. Here, we developed a scalable, deep network architecture for predictive coding that is trained using a gated Hebbian learning rule and mimics the feedforward and feedback connectivity of the cortex. After training on image datasets, the models formed latent representations in higher areas that allowed reconstruction of the original images. We analyzed low- and high-level properties such as orientation selectivity, object selectivity and sparseness of neuronal populations in the model. As reported experimentally, image selectivity increased systematically across ascending areas in the model hierarchy. Depending on the strength of regularization factors, sparseness also increased from lower to higher areas. The results suggest a rationale as to why experimental results on sparseness across the cortical hierarchy have been inconsistent. Finally, representations for different object classes became more distinguishable from lower to higher areas. Thus, deep neural networks trained using a gated Hebbian formulation of predictive coding can reproduce several properties associated with neuronal responses along the visual cortical hierarchy.
Language	English
Publishing date	2021-07-28
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2452964-3
ISSN	1662-5188
ISSN	1662-5188
DOI	10.3389/fncom.2021.666131
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