Hippocampal output profoundly impacts the interpretation of tactile input patterns in SI cortical neurons

Etemadi, Leila; Enander, Jonas M.D.; Jörntell, Henrik

doi:10.1016/j.isci.2023.106885

Cited by 5 publications

(7 citation statements)

References 43 publications

(94 reference statements)

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“…5) is in line with this. In fact, this is what would be expected given that the cortical and thalamocortical network is extensively globally interconnected 14,[17][18][19]41,42 . In principle, if such a recursive network was deterministic, i.e.…”

Section: High-dimensional Working Memory-like Signalsmentioning

confidence: 65%

“…Principal component analysis (PCA) was used to identify the covariance patterns in the activity distribution across the neuron population. Note that we have previously explored the separation of different spatiotemporal tactile input patterns of S1 neurons, and spatiotemporal visual input patterns of V1 neurons, using PCA applied to the full temporal profiles of the responses of each individual neuron recorded in sequence 10,14,[17][18][19]30 . Here, instead, we looked at the momentary activity distribution across the simultaneously recorded population of neurons, but did not analyze the full temporal profile of the responses.…”

Section: Principal Component Analysismentioning

confidence: 99%

“…Manipulating internal cortical states have been shown to alter the processing of given sensory inputs [16][17][18]39 . Further evidence that internal cortical processing is deeply involved in the structure of the recorded signal was provided by Nguyen et al ( 2023) who showed that any sensory input is repeatedly followed by internal replay, which gradually alters the cortical population responses to the given sensory inputs, i.e.…”

Section: High-dimensional Working Memory-like Signalsmentioning

confidence: 99%

“…Recent studies suggest a strong involvement of cortical internal activity, broadcast across essentially the entire thalamocortical system, including the hippocampus [13][14][15][16][17][18][19] . Because of its wide base of origin, such internal broadcast could be expected to exhibit high complexity.…”

Section: Introductionmentioning

confidence: 99%

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High-dimensional cortical signals reveal rich bimodal and working memory-like representations among S1 neuron populations

Kristensen,

Kesgin,

Jörntell

2024

Preprint

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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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Section: High-dimensional Working Memory-like Signalsmentioning

confidence: 65%

Section: Principal Component Analysismentioning

confidence: 99%

Section: High-dimensional Working Memory-like Signalsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

High-dimensional cortical signals reveal rich bimodal and working memory-like representations among S1 neuron populations

Kristensen,

Kesgin,

Jörntell

2024

Preprint

Self Cite

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show abstract

“…We have previously reported that the exact same tactile input pattern can trigger a wide variety of responses in individual primary somatosensory (S1) cortical neurons (Norrlid et al, 2021). The response variations are due to internal state variations in the neocortical neuronal network globally, which can be demonstrated by weakly perturbing either a remote area of the cortex (Etemadi et al, 2022;Wahlbom et al, 2019) or the output of the hippocampus (Etemadi et al, 2023). These response variations in individual neurons can be explained as a consequence of subtle changes in the state of the neocortex globally, which in turn can alter the number of "open" network pathways supplying the recorded neuron with tactile information.…”

Section: Introductionmentioning

confidence: 99%

Local field potential sharp waves with diversified impact on cortical neuronal encoding of haptic input

Kristensen,

Jörntell

2023

Preprint

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In cortical sensory processing, internal activity substantially impact the cortical response to any given input. But controlling for that internal activity is difficult since the thalamocortical network is high-dimensional, perpetually active and its state can change at a high pace. Here we report on a type of spontaneous local field potential sharp wave (LFP-SPW) in the somatosensory cortex (S1) and explore its impact on spiking responses evoked in S1 neurons by tactile stimulation. LFP-SPWs that preceded the tactile input could have major impacts, depressing the tactile responses in some neurons and enhancing them in others. In spontaneous data, in contrast, LFP-SPW events triggered short-lasting but massive neuronal activation in all neurons recorded with a subset of neurons initiating their activation up to 20 ms before the LFP-SPW onset. LFP-SPWs often coincided with ECoG-SPWs recorded at the cortical surface 2 mm away from the patch electrode, suggesting that the LFP-SPWs could be part of a more global cortical signal with a variable extent. When LFP-SPWs and ECoG-SPWs coincided, the impact of the LFP-SPW on the neuronal tactile response could change substantially, including inverting the impact to its opposite. Such diversified impacts of the SPWs on different S1 neurons are well in line with previous observations of cortical neurons displaying diverse, complementary, response types to given sensory inputs. These cortical SPWs had similar overall activity patterns as reported for hippocampal SPWs and may be a marker for a particular type of state change that possibly involves both hippocampus and neocortex.

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Local field potential sharp waves with diversified impact on cortical neuronal encoding of haptic input

Kristensen,

Jörntell

2024

Sci Rep

Self Cite

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Cortical sensory processing is greatly impacted by internally generated activity. But controlling for that activity is difficult since the thalamocortical network is a high-dimensional system with rapid state changes. Therefore, to unwind the cortical computational architecture there is a need for physiological ‘landmarks’ that can be used as frames of reference for computational state. Here we use a waveshape transform method to identify conspicuous local field potential sharp waves (LFP-SPWs) in the somatosensory cortex (S1). LFP-SPW events triggered short-lasting but massive neuronal activation in all recorded neurons with a subset of neurons initiating their activation up to 20 ms before the LFP-SPW onset. In contrast, LFP-SPWs differentially impacted the neuronal spike responses to ensuing tactile inputs, depressing the tactile responses in some neurons and enhancing them in others. When LFP-SPWs coactivated with more distant cortical surface (ECoG)-SPWs, suggesting an involvement of these SPWs in global cortical signaling, the impact of the LFP-SPW on the neuronal tactile response could change substantially, including inverting its impact to the opposite. These cortical SPWs shared many signal fingerprint characteristics as reported for hippocampal SPWs and may be a biomarker for a particular type of state change that is possibly shared byboth hippocampus and neocortex.

show abstract

Hippocampal output profoundly impacts the interpretation of tactile input patterns in SI cortical neurons

Cited by 5 publications

References 43 publications

High-dimensional cortical signals reveal rich bimodal and working memory-like representations among S1 neuron populations

High-dimensional cortical signals reveal rich bimodal and working memory-like representations among S1 neuron populations

Local field potential sharp waves with diversified impact on cortical neuronal encoding of haptic input

Local field potential sharp waves with diversified impact on cortical neuronal encoding of haptic input

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