Computational approaches to sensorimotor transformations
Abstract
Behaviors such as sensing an object and then moving your eyes or your hand toward it require that sensory information be used to help generate a motor command, a process known as a sensorimotor transformation. Here we review models of sensorimotor transformations that use a flexible intermediate representation that relies on basis functions. The use of basis functions as an intermediate is borrowed from the theory of nonlinear function approximation. We show that this approach provides a unifying insight into the neural basis of three crucial aspects of sensorimotor transformations, namely, computation, learning and short-term memory. This mathematical formalism is consistent with the responses of cortical neurons and provides a fresh perspective on the issue of frames of reference in spatial representations.
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Neurons in many cortical and subcortical brain areas exhibit robust gain modulation that may contribute to multiple cognitive functions, including attention, learning, sensory processing and multimodal integration.
“Neurons in many cortical and subcortical brain areas exhibit robust gain modulation that may contribute to multiple cognitive functions, including attention, learning, sensory processing and multimodal integration1–3,7,8,13–17.”
From Mechanisms underlying gain modulation in the cortex (2020) · cites this paper as [8]
Gain-modulated cells are suited to perform multimodal computations, such as conversions from sensory-centred into motor-centred reference frames, and for the generation of invariant responses to input features despite contextual variability.
“Gain-modulated cells are ideally suited to perform multimodal computations, such as conversions from sensory-centred into motor-centred reference frames, and for the generation of invariant responses to input features despite contextual variability6–9.”
From Mechanisms underlying gain modulation in the cortex (2020) · cites this paper as [8]
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“Some models have shown that the above information can be used to perform coordinate transformations (Zipser and Andersen, 1988; Pouget and Snyder, 2000).”
BackgroundMultimodal Integration in Rostral Fastigial Nucleus Provides an Estimate of Body Movement
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“…led theorists to propose gain fields for the implementation of such coordinate transformations (Andersen et al., 1993; Pouget and Sejnowski, 1997; Pouget and Snyder, 2000; Salinas and Abbott, 2001; Zipser and Andersen, 1988), but the predicted neural circuit connectivity has not been directly…”
BackgroundMethodA neural circuit architecture for rapid learning in goal-directed navigation
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“6,7 These internal representa-tions—for example, those carried by head direction (HD), grid, and place cells 8,9 —are often tethered to sensory cues, but they allow animals to achieve behavioral goals without directly depending on those cues.”
BackgroundA neural circuit architecture for rapid behavioral flexibility in goal-directed navigation
Dan, C., Hulse, B. K., Kappagantula, R., Jayaraman, V., & Hermundstad, A. M. (2021) · doi:10.1101/2021.08.18.456004 · Cited by 51 · see citing works
“However, sensory cues are not always reliable or even available, and many animals have evolved the ability to behave still more flexibly by generating and using internal representations of their relationship to their surroundings [6, 7].”
Background
- Authors
- Pouget, A., & Snyder, L. H.
- Affiliations
- University of Rochester (US)
- Washington University in St. Louis (US)
- Corresponding author
- Pouget, Alexandre
- ORCID
- Pouget, Alexandre
- Venue
- Nature Neuroscience
- Published
- 2000-11
- Volume
- 3
- Issue
- S11
- Pages
- 1192-1198
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- OpenAlex
- W1598361563
- Cited by
- 357
- References
- 45
- Citations by year
- 2026: 11 · 2025: 8 · 2024: 8 · 2023: 9 · 2022: 6 · 2021: 9 · 2020: 14 · 2019: 10 · 2018: 11 · 2017: 19 · 2016: 20 · 2015: 16 · 2014: 15 · 2013: 23 · 2012: 24
- FWCI
- 1.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Nature
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- sensorimotor transformation, basis functions, intermediate representation, nonlinear function approximation, motor command generation, neural computation, learning, short-term memory, reference frames, spatial representation, cortical neurons
- MeSH terms
- Animals, Brain, Humans, Learning, Memory, Short-Term, Models, Neurological, Nerve Net, Neurons, Psychomotor Performance, Space Perception, Linear Models, Nonlinear Dynamics
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