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.
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Acknowledgements
M.S. discloses support for the research of this work from the Brain Science Foundation and the Sumitomo Foundation (2200084). C.M.A.P. discloses support for the research of this work from the European Union’s Horizon 2020 Framework Program for Research and Innovation (Human Brain Project SGA3, 945539). J.A. discloses support for the research of this work from the European Social Fund through the ‘ICT programme’ measure and the Estonian Research Council grant (PSG728).
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Glossary
- Bayesian inference
-
A method of statistical analysis that is grounded in Bayes’ theorem, which describes how the probability of a hypothesis (posterior probability) is updated as new data (evidence) become available, given prior knowledge about the hypothesis (prior probability).
- Cortico-cortical loops
-
Neural circuits that connect different regions of the cerebral cortex to one another, allowing communication and integration of information across various cortical areas. These loops can be either short range, connecting adjacent or nearby cortical regions, or long range, linking distant regions of the cortex.
- Deep hierarchy
-
A hierarchical structure consisting of many layers (roughly analogous to cortical areas) through which information from the external world is processed step by step.
- Deep learning architectures
-
Structured configurations of hierarchical, interconnected layers of artificial neurons, or nodes, in a neural network. Common types of deep learning architecture include feedforward convolutional neural networks and recurrent neural networks.
- Hierarchical inference
-
The process of drawing conclusions from data wherein parameters are organized into different levels or layers. In hierarchical Bayesian inference, Bayesian statistics are employed within a layered framework, integrating prior knowledge at multiple levels to refine posterior distributions.
- Higher-order thalamic nuclei
-
Thalamic nuclei can be categorized anatomically into first-order and higher-order nuclei. First-order nuclei receive driving afferents from ascending pathways, whereas the higher-order nuclei receive driving afferents from cortical layer 5 pyramidal (L5p) neurons. Notable examples of higher-order thalamic nuclei include the pulvinar and the medial dorsal nucleus.
- Non-hierarchical lateral connections
-
Connections made between two cortical areas that are not distinguished hierarchically (for instance, primary auditory and visual cortex). This connectivity pattern is illustrated in Fig. 1b.
- Recurrent connections
-
Connections in which the output of a neuron at a given layer is fed back as an input to either the same layer or a previous layer. This creates a loop in the network, allowing information, for instance, to persist and be reused across sequential steps.
- Recurrent neural network
-
A class of neural networks in which connections between nodes form directed cycles, enabling the retention of information from previous inputs. This sequential memory feature makes recurrent neural networks suitable for tasks involving time-series or sequential data.
- Reinforcement learning
-
A machine learning method in which an agent makes decisions and receives reinforcing feedback to train the network to improve its output (for example, reward for desired behaviours, punishment for behaviour resulting in undesirable output).
- Shallow architectures
-
Architectures that do not consist of a deep hierarchy. Shallow architectures instead have a minimum number of layers.
- Shallow processing
-
Computations carried out by a shallow architecture, namely in a few steps instead of tens or hundreds of layers of processing.
- Thalamo-cortical loops
-
Bidirectional pathways between the thalamus and the cerebral cortex. Thalamo-cortical loops play a vital role in the regulation of consciousness, attention and sensory processing, and have been implicated in several neurological and psychiatric disorders.
- Trans-thalamic connections
-
Connections made between two brain regions via the thalamus.
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Suzuki, M., Pennartz, C.M.A. & Aru, J. How deep is the brain? The shallow brain hypothesis. Nat. Rev. Neurosci. 24, 778–791 (2023). https://doi.org/10.1038/s41583-023-00756-z
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DOI: https://doi.org/10.1038/s41583-023-00756-z
