High-dimensional cortical signals reveal rich bimodal and working memory-like representations among S1 neuron populations
(2024) In Communications Biology 7(1).- Abstract
Complexity is important for flexibility of natural behavior and for the remarkably efficient learning of the brain. Here we assessed the signal complexity among neuron populations in somatosensory cortex (S1). To maximize our chances of capturing population-level signal complexity, we used highly repeatable resolvable visual, tactile, and visuo-tactile inputs and neuronal unit activity recorded at high temporal resolution. We found the state space of the spontaneous activity to be extremely high-dimensional in S1 populations. Their processing of tactile inputs was profoundly modulated by visual inputs and even fine nuances of visual input patterns were separated. Moreover, the dynamic activity states of the S1 neuron population signaled... (More)
Complexity is important for flexibility of natural behavior and for the remarkably efficient learning of the brain. Here we assessed the signal complexity among neuron populations in somatosensory cortex (S1). To maximize our chances of capturing population-level signal complexity, we used highly repeatable resolvable visual, tactile, and visuo-tactile inputs and neuronal unit activity recorded at high temporal resolution. We found the state space of the spontaneous activity to be extremely high-dimensional in S1 populations. Their processing of tactile inputs was profoundly modulated by visual inputs and even fine nuances of visual input patterns were separated. Moreover, the dynamic activity states of the S1 neuron population signaled the preceding specific input long after the stimulation had terminated, i.e., resident information that could be a substrate for a working memory. Hence, the recorded high-dimensional representations carried rich multimodal and internal working memory-like signals supporting high complexity in cortical circuitry operation.
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- author
- Kristensen, Sofie S. LU ; Kesgin, Kaan LU and Jörntell, Henrik LU
- organization
- publishing date
- 2024-12
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Communications Biology
- volume
- 7
- issue
- 1
- article number
- 1043
- publisher
- Nature Publishing Group
- external identifiers
-
- scopus:85202267139
- pmid:39179675
- ISSN
- 2399-3642
- DOI
- 10.1038/s42003-024-06743-z
- language
- English
- LU publication?
- yes
- id
- b2365b83-b92a-41f3-87ff-dd71fed6f4f3
- date added to LUP
- 2024-10-28 14:48:45
- date last changed
- 2025-06-10 10:01:05
@article{b2365b83-b92a-41f3-87ff-dd71fed6f4f3,
abstract = {{<p>Complexity is important for flexibility of natural behavior and for the remarkably efficient learning of the brain. Here we assessed the signal complexity among neuron populations in somatosensory cortex (S1). To maximize our chances of capturing population-level signal complexity, we used highly repeatable resolvable visual, tactile, and visuo-tactile inputs and neuronal unit activity recorded at high temporal resolution. We found the state space of the spontaneous activity to be extremely high-dimensional in S1 populations. Their processing of tactile inputs was profoundly modulated by visual inputs and even fine nuances of visual input patterns were separated. Moreover, the dynamic activity states of the S1 neuron population signaled the preceding specific input long after the stimulation had terminated, i.e., resident information that could be a substrate for a working memory. Hence, the recorded high-dimensional representations carried rich multimodal and internal working memory-like signals supporting high complexity in cortical circuitry operation.</p>}},
author = {{Kristensen, Sofie S. and Kesgin, Kaan and Jörntell, Henrik}},
issn = {{2399-3642}},
language = {{eng}},
number = {{1}},
publisher = {{Nature Publishing Group}},
series = {{Communications Biology}},
title = {{High-dimensional cortical signals reveal rich bimodal and working memory-like representations among S1 neuron populations}},
url = {{http://dx.doi.org/10.1038/s42003-024-06743-z}},
doi = {{10.1038/s42003-024-06743-z}},
volume = {{7}},
year = {{2024}},
}