The Role of Prefrontal Mixed Selectivity in Cognitive Control

Ramirez-Cardenas, Araceli; Viswanathan, Pooja

doi:10.1523/jneurosci.1816-16.2016

Cited by 8 publications

(8 citation statements)

References 19 publications

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“…Given the multifaceted nature of the sensory information in real life setting, this is a reasonable and efficient strategy to process numerous types of distinct stimuli within a limited sensory system resource (Chu et al, 2016). Indeed, similar phenomena have been reported in complex cognitive tasks of the prefrontal cortex, known as “mixed selectivity” (Rigotti et al, 2013; Ramirez-Cardenas and Viswanathan, 2016; Parthasarathy et al, 2017). Thus, our findings extend this “mixed selectivity” concept to the somatosensory cortex, suggesting that it is a more general mechanism in the cortex than previously thought.…”

Section: Discussionsupporting

confidence: 61%

S1 Employs Feature-Dependent Differential Selectivity of Single Cells and Distributed Patterns of Populations to Encode Mechanosensations

Kim

Yoon

et al. 2019

Front. Cell. Neurosci.

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The primary somatosensory (S1) cortex plays an important role in the perception and discrimination of touch and pain mechanosensations. Conventionally, neurons in the somatosensory system including S1 cortex have been classified into low/high threshold (HT; non-nociceptive/nociceptive) or wide dynamic range (WDR; convergent) neurons by their electrophysiological responses to innocuous brush-stroke and noxious forceps-pinch stimuli. Besides this “noxiousness” (innocuous/noxious) feature, each stimulus also includes other stimulus features: “texture” (brush hairs/forceps-steel arm), “dynamics” (dynamic stroke/static press) and “intensity” (weak/strong). However, it remains unknown how S1 neurons inclusively process such diverse features of brushing and pinch at the single-cell and population levels. Using in vivo two-photon Ca 2+ imaging in the layer 2/3 neurons of the mouse S1 cortex, we identified clearly separated response patterns of the S1 neural population with distinct tuning properties of individual cells to texture, dynamics and noxiousness features of cutaneous mechanical stimuli. Among cells other than broadly tuned neurons, the majority of the cells showed a highly selective response to the difference in texture, but low selectivity to the difference in dynamics or noxiousness. Between the two low selectivity features, the difference in dynamics was slightly more specific, yet both could be decoded using the response patterns of neural populations. In addition, more neurons are recruited and stronger Ca 2+ responses are evoked as the intensity of forceps-pinch is gradually increased. Our results suggest that S1 neurons encode various features of mechanosensations with feature-dependent differential selectivity of single cells and distributed response patterns of populations. Moreover, we raise a caution about describing neurons by a single stimulus feature ignoring other aspects of the sensory stimuli.

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Section: Discussionsupporting

confidence: 61%

S1 Employs Feature-Dependent Differential Selectivity of Single Cells and Distributed Patterns of Populations to Encode Mechanosensations

Kim

Yoon

et al. 2019

Front. Cell. Neurosci.

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“…Cortical neurons have been shown to encode mixed representations of multiple task variables during cognitively demanding tasks ( Johnston et al, 2020 ; Parthasarathy et al, 2017 ; Rigotti et al, 2013 ; Zhang et al, 2017 ), and nonlinear mixed selectivity (NMS) in PFC has been particularly emphasized as an important mechanism for cognitive computations. Specifically, NMS can potentially facilitate a linear readout of task variables, and the strength of NMS is correlated with the subjects’ behavior ( Fusi et al, 2016 ; Ramirez-Cardenas and Viswanathan, 2016 ; Rigotti et al, 2013 ). Our observation of nonlinear integrative encoding in PFC may be related to recent experimental and theoretical work on NMS in PFC ( Parthasarathy et al, 2017 ; Rigotti et al, 2013 ; Zhang et al, 2017 ).…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, the interaction term cannot be directly related to the NMS in our study. However, the structure of the task used in the current study prohibits us from following their analytical approach due to the smaller number of distinct conditions tested in our task ( Fusi et al, 2016 ; Ramirez-Cardenas and Viswanathan, 2016 ; Rigotti et al, 2013 ).…”

Section: Methodsmentioning

confidence: 99%

Distributed functions of prefrontal and parietal cortices during sequential categorical decisions

Zhou

Rosen

Swaminathan

et al. 2021

eLife

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Comparing sequential stimuli is crucial for guiding complex behaviors. To understand mechanisms underlying sequential decisions, we compared neuronal responses in the prefrontal cortex (PFC), the lateral intraparietal (LIP), and medial intraparietal (MIP) areas in monkeys trained to decide whether sequentially presented stimuli were from matching (M) or nonmatching (NM) categories. We found that PFC leads M/NM decisions, whereas LIP and MIP appear more involved in stimulus evaluation and motor planning, respectively. Compared to LIP, PFC showed greater nonlinear integration of currently visible and remembered stimuli, which correlated with the monkeys’ M/NM decisions. Furthermore, multi-module recurrent networks trained on the same task exhibited key features of PFC and LIP encoding, including nonlinear integration in the PFC-like module, which was causally involved in the networks’ decisions. Network analysis found that nonlinear units have stronger and more widespread connections with input, output, and within-area units, indicating putative circuit-level mechanisms for sequential decisions.

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“…Cortical neurons have been shown to encode mixed representations of multiple task variables during cognitively demanding tasks (Johnston et al, 2020;Parthasarathy et al, 2017;Rigotti et al, 2013;Zhang et al, 2017), and nonlinear mixed selectivity (NMS) in PFC has been particularly emphasized as an important mechanism for cognitive computations. Specifically, NMS can potentially facilitate a linear readout of task variables, and the strength of NMS is correlated with the subjects' behavior (Fusi et al, 2016;Ramirez-Cardenas and Viswanathan, 2016;Rigotti et al, 2013). This is potentially related to the results of the current study.…”

Section: Discussionmentioning

confidence: 72%

Distributed functions of prefrontal and parietal cortices during sequential categorical decisions

Zhou

Rosen

Swaminathan

et al. 2020

Preprint

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The ability to compare sequential sensory inputs is crucial for solving many behavioral tasks. To understand the neuronal mechanisms underlying sequential decisions, we compared neuronal responses in the prefrontal cortex (PFC) and the lateral and medial intra-parietal (LIP and MIP) areas in monkeys trained to decide whether sequentially presented stimuli were from matching (M) or nonmatching (NM) categories. We found that PFC leads the M/NM decision process relying on nonlinear neuronal integration of sensory and mnemonic information, whereas LIP and MIP are more involved in sensory evaluation and motor planning, respectively. Furthermore, multi-module recurrent neural networks trained on the same task exhibited the key features of PFC and LIP encoding, including nonlinear integrative encoding in the PFC-like module which was crucial for M/NM decisions. Together, our results illuminate the relative functions of LIP, PFC, and MIP in sensory, cognitive and motor functions, and suggest that nonlinear integration of task-related variables in PFC is important for mediating sequential decisions.

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The Role of Prefrontal Mixed Selectivity in Cognitive Control

Cited by 8 publications

References 19 publications

S1 Employs Feature-Dependent Differential Selectivity of Single Cells and Distributed Patterns of Populations to Encode Mechanosensations

S1 Employs Feature-Dependent Differential Selectivity of Single Cells and Distributed Patterns of Populations to Encode Mechanosensations

Distributed functions of prefrontal and parietal cortices during sequential categorical decisions

Distributed functions of prefrontal and parietal cortices during sequential categorical decisions

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