Opposing Influence of Top-down and Bottom-up Input on Excitatory Layer 2/3 Neurons in Mouse Primary Visual Cortex

Neuron, Journal Year: 2020, Volume and Issue: 108(6), P. 1194 - 1206.e5

Published: Oct. 21, 2020

Processing in cortical circuits is driven by combinations of and subcortical inputs. These inputs are often conceptually categorized as bottom-up, conveying sensory information, top-down, contextual information. Using intracellular recordings mouse primary visual cortex, we measured neuronal responses to input, locomotion, visuomotor mismatches. We show that layer 2/3 (L2/3) neurons compute a difference between top-down motor-related input bottom-up flow input. Most L2/3 responded mismatch with either hyperpolarization or depolarization, the size this response was correlated distinct physiological properties. Consistent subtraction had opposing influence on neurons. In infragranular neurons, found no evidence computation were consistent positive integration Our results provide functions bidirectional comparator

Language: Английский

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NMDA receptors in visual cortex are necessary for normal visuomotor integration and skill learning

eLife, Journal Year: 2022, Volume and Issue: 11

Published: Feb. 16, 2022

The experience of coupling between motor output and visual feedback is necessary for the development visuomotor skills shapes integration in cortex. Whether these experience-dependent changes responses V1 depend on modifications local circuit or are consequence outside remains unclear. Here, we probed role N-methyl-d-aspartate (NMDA) receptor-dependent signaling, which known to be involved neuronal plasticity, mouse primary cortex (V1) during development. We used a knockout NMDA receptors photoactivatable inhibition CaMKII first probe activity as well influence performance task. found that before, but not after, reduced unpredictable stimuli, diminished suppression predictable V1, impaired skill learning later life. Our results demonstrate signaling critical shaping enabling learning.

Language: Английский

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Prediction-error neurons in circuits with multiple neuron types: Formation, refinement, and functional implications

Proceedings of the National Academy of Sciences, Journal Year: 2022, Volume and Issue: 119(13)

Published: March 23, 2022

Significance An influential idea in neuroscience is that neural circuits do not only passively process sensory information but rather actively compare them with predictions thereof. A core element of this comparison prediction-error neurons, the activity which changes upon mismatches between actual and predicted stimuli. While it has been shown these neurons come different variants, largely unresolved how they are simultaneously formed shaped by highly interconnected networks. By using a computational model, we study circuit-level mechanisms give rise to variants neurons. Our results shed light on formation, refinement, robustness circuits, an important step toward better understanding predictive processing.

Language: Английский

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The locus coeruleus as a global model failure system

Trends in Neurosciences, Journal Year: 2023, Volume and Issue: 47(2), P. 92 - 105

Published: Dec. 14, 2023

Predictive processing models posit that brains constantly attempt to predict their sensory inputs. Prediction errors signal when these predictions are incorrect and thought be instructive signals drive corrective plasticity. Recent findings support the idea locus coeruleus (LC) - a brain-wide neuromodulatory system several types of prediction error. I discuss how proposing LC global model failures: instances where about world strongly violated. Focusing on cortex, explore utility this in learning rate control, circuit may compute signal, view aid our understanding neurodivergence.

Language: Английский

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Supralinear dendritic integration in murine dendrite-targeting interneurons

eLife, Journal Year: 2025, Volume and Issue: 13

Published: Jan. 31, 2025

Non-linear summation of synaptic inputs to the dendrites pyramidal neurons has been proposed increase computation capacity through coincidence detection, signal amplification, and additional logic operations such as XOR. Supralinear dendritic integration documented extensively in principal neurons, mediated by several voltage-dependent conductances. It also reported parvalbumin-positive hippocampal basket cells, innervated feedback excitatory synapses. Whether other interneurons, which support feed-forward or inhibition neuron dendrites, exhibit local non-linear excitation is not known. Here, we use patch-clamp electrophysiology, two-photon calcium imaging glutamate uncaging, show that supralinear near-synchronous spatially clustered glutamate-receptor depolarization occurs NDNF-positive neurogliaform cells oriens-lacunosum moleculare interneurons mouse hippocampus. was detected via recordings somatic depolarizations elicited uncaging on fragments, and, concurrent transients. Supralinearity abolished blocking NMDA receptors (NMDARs) but resisted blockade voltage-gated sodium channels. Blocking L-type channels signalling only had a minor effect voltage supralinearity. Dendritic boosting signals argues for previously unappreciated computational complexity dendrite-projecting inhibitory

Language: Английский

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Learning excitatory-inhibitory neuronal assemblies in recurrent networks

eLife, Journal Year: 2021, Volume and Issue: 10

Published: April 26, 2021

Understanding the connectivity observed in brain and how it emerges from local plasticity rules is a grand challenge modern neuroscience. In primary visual cortex (V1) of mice, synapses between excitatory pyramidal neurons inhibitory parvalbumin-expressing (PV) interneurons tend to be stronger for that respond similar stimulus features, although these are not topographically arranged according their preference. The presence such excitatory-inhibitory (E/I) neuronal assemblies indicates stimulus-specific form feedback inhibition. Here, we show activity-dependent synaptic on input output PV generates circuit structure consistent with mouse V1. Computational modeling reveals both forms must act synergy E/I assemblies. Once established, produce competition neurons. Our model suggests can refine circuits actively shape cortical computations.

Language: Английский

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Simple synaptic modulations implement diverse novelty computations

Cell Reports, Journal Year: 2024, Volume and Issue: 43(5), P. 114188 - 114188

Published: May 1, 2024

Detecting novelty is ethologically useful for an organism's survival. Recent experiments characterize how different types of over timescales from seconds to weeks are reflected in the activity excitatory and inhibitory neuron types. Here, we introduce a learning mechanism, familiarity-modulated synapses (FMSs), consisting multiplicative modulations dependent on presynaptic or pre/postsynaptic activity. With FMSs, network responses that encode emerge under unsupervised continual minimal connectivity constraints. Implementing FMSs within experimentally constrained model visual cortical circuit, demonstrate generalizability by simultaneously fitting absolute, contextual, omission effects. Our also reproduces functional diversity cell subpopulations, leading testable predictions about synaptic dynamics can produce both population-level heterogeneous individual signals. Altogether, our findings simple plasticity mechanisms circuit structure qualitatively distinct complex responses.

Language: Английский

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Spatial navigation signals in rodent visual cortex

Current Opinion in Neurobiology, Journal Year: 2020, Volume and Issue: 67, P. 163 - 173

Published: Dec. 25, 2020

During navigation, animals integrate sensory information with body movements to guide actions. The impact of both navigational and movement-related signals on cortical visual processing remains largely unknown. We review recent studies in awake rodents that have revealed navigation-related the primary cortex (V1) including speed, distance travelled head-orienting movements. Both subcortical inputs convey self-motion related V1 neurons: for example, top-down from secondary motor retrosplenial cortices about head spatial expectations. Within V1, subtypes inhibitory neurons are critical integration signals. conclude potential functional roles gain control, error predictive coding.

Language: Английский

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Fast adaptation to rule switching using neuronal surprise

PLoS Computational Biology, Journal Year: 2024, Volume and Issue: 20(2), P. e1011839 - e1011839

Published: Feb. 20, 2024

In humans and animals, surprise is a physiological reaction to an unexpected event, but how can be linked plausible models of neuronal activity open problem. We propose self-supervised spiking neural network model where signal extracted from increase in after imbalance excitation inhibition. The modulates synaptic plasticity via three-factor learning rule which increases at moments surprise. remains small when transitions between sensory events follow previously learned immediately switching. with several modules, rules are protected against overwriting, as long the number modules larger than total rules—making step towards solving stability-plasticity dilemma neuroscience. Our relates subjective notion specific predictions on circuit level.

Language: Английский

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Local minimization of prediction errors drives learning of invariant object representations in a generative network model of visual perception

Frontiers in Computational Neuroscience, Journal Year: 2023, Volume and Issue: 17

Published: Sept. 25, 2023

The ventral visual processing hierarchy of the cortex needs to fulfill at least two key functions: perceived objects must be mapped high-level representations invariantly precise viewing conditions, and a generative model learned that allows, for instance, fill in occluded information guided by experience. Here, we show how multilayered predictive coding network can learn recognize from bottom up generate specific via top-down pathway through single learning rule: local minimization prediction errors. Trained on sequences continuously transformed objects, neurons highest area become tuned object identity invariant position, comparable inferotemporal macaques. Drawing this, dynamic properties reproduce experimentally observed hierarchies timescales low high levels stream. predicted faster decorrelation error-neuron activity compared representation is relevance experimental search neural correlates Lastly, capacity confirmed reconstructing images, robust partial occlusion inputs. By invariance temporal continuity within model, approach generalizes framework inputs more biologically plausible way than self-supervised networks with non-local error-backpropagation. This was achieved simply shifting training paradigm inputs, little change architecture rule static input-reconstructing Hebbian networks.

Language: Английский

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