Deep neural networks in computational neuroscience
Published version of a preprint
Abstract
The goal of computational neuroscience is to find mechanistic explanations of how the nervous system processes information to give rise to cognitive function and behavior. At the heart of the field are its models, that is, mathematical and computational descriptions of the system being studied, which map sensory stimuli to neural responses and/or neural to behavioral responses. These models range from simple to complex. Recently, deep neural networks (DNNs) have come to dominate several domains of artificial intelligence (AI). As the term “neural network” suggests, these models are inspired by biological brains. However, current DNNs neglect many details of biological neural networks. These simplifications contribute to their computational efficiency, enabling them to perform complex feats of intelligence, ranging from perceptual (e.g., visual object and auditory speech recognition) to cognitive tasks (e.g., machine translation), and on to motor control (e.g., playing computer games or controlling a robot arm). In addition to their ability to model complex intelligent behaviors, DNNs excel at predicting neural responses to novel sensory stimuli with accuracies well beyond any other currently available model type. DNNs can have millions of parameters, which are required to capture the domain knowledge needed for successful task performance. Contrary to the intuition that this renders them into impenetrable black boxes, the computational properties of the network units are the…
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Artificial neural networks have become a prominent tool for understanding neural representations because they provide a language for expressing computational hypotheses about brain function.
“To make progress in understanding these neural representations, artificial neural networks (ANNs) have become a prominent tool as they provide a language for expressing computational hypotheses about brain function [1–3]”
From The Umwelt Representation Hypothesis: rethinking Universality (2026) · cites this paper as [3]
ANNs can also be characterized as having an Umwelt determined by constraints such as architecture, training data, and learning objectives.
“Note that ANNs can also be characterized as having an Umwelt determined by constraints such as architecture, training data, and learning objectives (Figure 1E) [1–3]”
From The Umwelt Representation Hypothesis: rethinking Universality (2026) · cites this paper as [3]
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Neural Networks for Modeling Neural Spiking in S1 Cortex
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- Authors
- Kietzmann, T. C., McClure, P., & Kriegeskorte, N.
- Affiliations
- MRC Cognition and Brain Sciences Unit (GB)
- Venue
- Oxford Research Encyclopedia of Neuroscience
- Publisher
- Oxford University PressNew York, NY
- Published
- 2019-01-25
- OpenAlex
- W2611715315
- Cited by
- 186
- References
- 140
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 8 · 2023: 17 · 2022: 64 · 2021: 23 · 2020: 30 · 2019: 21 · 2018: 5 · 2017: 3
- Type
- reference entry
- Language
- en
- Open access
- closed
- Locations
- Oxford Research Encyclopedia of Neuroscience (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Undergraduate Neuroscience Education and Research
- Keywords
- deep neural network, computational neuroscience, neural network model, neural response prediction, sensory stimuli, network structure, learning algorithms, input statistics, network visualization, neural representations, black-box interpretability, artificial intelligence
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