Deep neural networks: a new framework for modeling biological vision and brain information processing
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Abstract
Recent advances in neural network modeling have enabled major strides in computer vision and other artificial intelligence applications. Human-level visual recognition abilities are coming within reach of artificial systems. Artificial neural networks are inspired by the brain, and their computations could be implemented in biological neurons. Convolutional feedforward networks, which now dominate computer vision, take further inspiration from the architecture of the primate visual hierarchy. However, the current models are designed with engineering goals, not to model brain computations. Nevertheless, initial studies comparing internal representations between these models and primate brains find surprisingly similar representational spaces. With human-level performance no longer out of reach, we are entering an exciting new era, in which we will be able to build biologically faithful feedforward and recurrent computational models of how biological brains perform high-level feats of intelligence, including vision.
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There is evidence that the perceptual representation spaces learned by current DNNs already closely resemble those of the human visual system, so their corresponding quality spaces might already be substantially aligned.
“In fact, there is evidence that the perceptual representation spaces learned by current DNNs already closely resemble those of the human visual system, meaning their corresponding “quality spaces” might already be substantially aligned.”
From Consciousness in Artificial Intelligence: Insights from the Science of Consciousness (2023) · cites this paper as Kriegeskorte (2015)
In “Early Studies Testing Deep Neural Nets as Models of Biological Brain Representations,” the paper says neural networks learn representations very similar to those of the primate ventral visual pathway, including human visual-cortex representations. It does not address whether the corresponding “quality spaces” are aligned; that is an additional inference in the citing sentence. Page numbers are not provided in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredDeep-learning algorithms share organizational overlap with the ventral visual stream of the brain.
“and share organizational overlap with the ventral visual stream of the brain [144-146].”
From How Do Expectations Shape Perception? (2018) · cites this paper as [146]
Computational models that mimic brain information processing during perceptual, cognitive and control tasks are beginning to be developed and tested with brain and behavioral data.
“Computational models that mimic brain information processing during perceptual, cognitive and control tasks are beginning to be developed and tested with brain and behavioral data.”
From Cognitive computational neuroscience (2018) · cites this paper as [37]
Computational neuroscience has begun to test complex computational models that can explain high-level sensory and cognitive brain representations.
“Computational neuroscience has also begun to test complex computational models that can explain high-level sensory and cognitive brain representations37,38.”
From Cognitive computational neuroscience (2018) · cites this paper as [37]
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- Authors
- Kriegeskorte, N.
- Affiliations
- MRC Cognition and Brain Sciences Unit (GB)
- Corresponding author
- Kriegeskorte, Nikolaus
- Venue
- Annual Review of Vision Science
- Publisher
- Annual Reviews
- Published
- 2015-11-24
- Volume
- 1
- Issue
- 1
- Pages
- 417-446
- ISSN
- 2374-4642 (ISSN-L)
- 2374-4650
- OpenAlex
- W2176287621
- Cited by
- 1,093
- References
- 112
- Citations by year
- 2026: 77 · 2025: 125 · 2024: 124 · 2023: 125 · 2022: 117 · 2021: 125 · 2020: 132 · 2019: 109 · 2018: 76 · 2017: 69 · 2016: 12 · 2015: 2
- FWCI
- 10.98
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Annual Review of Vision Science (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- Research Publications (Maastricht University) (submittedVersion)
- Funders
- Wellcome Trust (WT091540MA)
- Medical Research Council (MC-A060- 5PR20, ERC-2010-StG 261352)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- deep neural network, biological vision, brain information processing, convolutional neural networks, representational similarity, internal representations, recurrent neural networks, computational neuroscience, visual recognition
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