Prediction error neurons in mouse cortex are molecularly targetable cell types

O’Toole, Sean; Oyibo, Hassana K.; Keller, Georg B.

doi:10.1101/2022.07.20.500837

Cited by 15 publications

(8 citation statements)

References 56 publications

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“…More generally, the distinct inhibitory environments of upper and lower layer 2/3 have been observed across the brain, from primary sensory cortices 43,77 to higher-order association areas 78 , suggesting that it may reflect a general functional specialization. For example, the distinct inhibitory environments of layer 2 and layer 3 are potentially well-posed for differential inhibition of top-down versus sensory-driven activity that has been recently described 79,80 .…”

Section: Discussionmentioning

confidence: 99%

Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex

Schneider-Mizell

Bodor

Brittain

et al. 2023

Preprint

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Mammalian cortex features a large diversity of neuronal cell types, each with characteristic anatomical, molecular and functional properties. Synaptic connectivity rules powerfully shape how each cell type participates in the cortical circuit, but comprehensively mapping connectivity at the resolution of distinct cell types remains difficult. Here, we used millimeter-scale volumetric electron microscopy to investigate the connectivity of inhibitory neurons across a dense neuronal population spanning all layers of mouse visual cortex with synaptic resolution. We classified all 1183 excitatory neurons within a 100 micron column into anatomical subclasses using quantitative morphological and synapse features based on full dendritic reconstructions, finding both familiar subclasses corresponding to axonal projections and novel intralaminar distinctions based on synaptic properties. To relate these subclasses to single-cell connectivity, we reconstructed all 164 inhibitory interneurons in the same column, producing a wiring diagram of inhibition with more than 70,000 synapses. We found widespread cell-type-specific inhibition, including interneurons selectively targeting certain excitatory subpopulations among spatially intermingled neurons in layer 2/3, layer 5, and layer 6. Globally, inhibitory connectivity was organized into "motif groups," heterogeneous collections of cells that collectively target both perisomatic and dendritic compartments of the same combinations of excitatory subtypes. We also discovered a novel category of disinhibitory-specialist interneurons that preferentially targets basket cells. Collectively, our analysis revealed new organizing principles for cortical inhibition and will serve as a powerful foundation for linking modern multimodal neuronal atlases with the cortical wiring diagram.

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Section: Discussionmentioning

confidence: 99%

Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex

Schneider-Mizell

Bodor

Brittain

et al. 2023

Preprint

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“…Interestingly, a recent experimental study suggested that positive and negative prediction-error neurons in L2/3 map to different transcriptomically defined cell types [28]. If that is correct, then one can expect the above mentioned connectivity differences between these classes – namely, prominent differences in inputs to the different classes of L2/3 neurons from, e.g., L4, but less difference in recurrent connections in L2/3.…”

Section: Resultsmentioning

confidence: 99%

Competition between bottom-up visual input and internal inhibition generates error neurons in a model of the mouse primary visual cortex

Mirasso

Fraile

Scherr

et al. 2023

Preprint

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The predictive coding theory, although attractive, is far from being proven. Supporters of this theory agree that bottom-up sensory inputs and top-down predictions of these inputs must be compared in certain types of neurons called error neurons. Excitatory neurons in layer 2/3 (E2/3) of the primary visual cortex (V1) are ideal candidates to act as error neurons, although how these error neurons are generated is poorly understood. In this study, we aimed to gain insight into how the genetically encoded structure of canonical microcircuits in the neocortex implements the emergence of error neurons. To this end, we used a biologically realistic computational model of V1, developed by the Allen Institute, to study the effect that sudden changes in bottom-up input had on the dynamics of E2/3 neurons. We found that the responses of these neurons can be divided into two main classes: one that depolarized (reporting positive errors; dVf neurons) and one that hyperpolarized (reporting negative errors; hVf neurons). Detailed analysis of both network and effective connectivity allowed us to uncover the mechanism that led to the dynamic segregation of these neurons. This mechanism was found to be the competition between the external visual input, originating in the thalamus, and the recurrent inhibition, originating mainly in layers 2/3 and 4. In contrast, we found no evidence of similar division and responses in excitatory infragranular neurons of layers 5 and 6. Our results are in agreement with recent experimental findings and shed light on the mechanisms responsible for the emergence of error neurons.

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“…Previous work has shown that in V1, L2/3 PyCs indeed respond to mismatches between visual flow and locomotion 7,[52][53][54] . Recent evidence suggests that two genetically defined subpopulations of L2/3 PyCs preferentially respond to (unexpected) visual stimuli, which could be considered positive prediction errors, or visuomotor mismatch, which could be seen as negative prediction errors 51 . In line with these findings, we observed two functional clusters of PyCs in L2/3, one responding predominantly to visual stimuli (V-PyCs) and the other to visuomotor mismatch (NV-PyCs).…”

Section: Discussionmentioning

confidence: 99%

“…Previous research has demonstrated that a significant fraction of V1 PyCs exhibit strong visuomotor mismatch responses when the visual flow of the tunnel is abruptly stopped while the mice are still running 7,[51][52][53][54][55][56] . Recent evidence suggests that PyCs with visuomotor mismatch responses may belong to a genetically distinct subpopulation that is less visually responsive 51 . Therefore, we assessed whether the two populations of PyCs we identified also differed in terms of their visuomotor mismatch responses.…”

Section: Chcs Have Similar Response Properties As Non-visual Pycsmentioning

confidence: 99%

Experience Shapes Chandelier Cell Function and Structure in the Visual Cortex

Seignette

Jamann

Papale

et al. 2023

Preprint

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Detailed characterization of interneuron subtypes in primary visual cortex (V1) has greatly contributed to understanding visual perception, yet the role of chandelier cells (ChCs) in visual processing remains poorly characterized. Using viral tracing we found that V1 ChCs predominantly receive monosynaptic input from local layer 5 pyramidal cells and higher-order cortical regions. Two-photon calcium imaging and convolutional neural network modelling revealed that ChCs are visually responsive but weakly selective for stimulus content. In mice running in a virtual tunnel, ChCs respond strongly to locomotion and halting visual flow, suggesting arousal-related activity. Visuomotor experience in the tunnel diminished visual responses of ChCs and induced structural plasticity of ChC boutons and axon initial segment length. Finally, ChCs only weakly inhibited pyramidal cells. These findings suggest that ChCs provide an arousal-related signal to layer 2/3 pyramidal cells that may modulate their activity and/or gate plasticity of their axon initial segments during behaviorally relevant events.

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Prediction error neurons in mouse cortex are molecularly targetable cell types

Cited by 15 publications

References 56 publications

Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex

Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex

Competition between bottom-up visual input and internal inhibition generates error neurons in a model of the mouse primary visual cortex

Experience Shapes Chandelier Cell Function and Structure in the Visual Cortex

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