Rate and oscillatory switching dynamics of a multilayer visual microcircuit model

eLife, Год журнала: 2022, Номер 11

Опубликована: Авг. 22, 2022

The neocortex is organized around layered microcircuits consisting of a variety excitatory and inhibitory neuronal types which perform rate- oscillation-based computations. Using modeling, we show that both superficial deep layers the primary mouse visual cortex implement two ultrasensitive bistable switches built on mutual connectivity motives between somatostatin, parvalbumin, vasoactive intestinal polypeptide cells. toggle pyramidal neurons high low firing rate states are synchronized across through translaminar connectivity. Moreover, inhibited disinhibited characterized by low- high-frequency oscillations, respectively, with layer-specific differences in frequency power asymmetric changes during state transitions. These findings consistent number experimental observations embed together oscillatory within switch interpretation microcircuit.

Язык: Английский

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Prediction of the Soil Permeability Coefficient of Reservoirs Using a Deep Neural Network Based on a Dendrite Concept

Processes, Год журнала: 2023, Номер 11(3), С. 661 - 661

Опубликована: Фев. 22, 2023

Changes in the pore water pressure of soil are essential factors that affect movement structures during and after construction terms stability safety. Soil permeability represents quantity transferred using pressure. However, these changes cannot be easily identified require considerable time money. This study predicted evaluated coefficient a multiple regression (MR) model, adaptive network-based fuzzy inference system (ANFIS), general deep neural network (DNN) DNN dendrite concept (DNN−T, which was proposed this study). The void ratio, unit weight, particle size were obtained from 164 undisturbed samples collected embankments reservoirs South Korea as input variables for aforementioned models. data used included seven variables, ratios training to validation randomly extracted, such 6:4, 7:3, 8:2, used. analysis results each model showed median correlation r = 0.6 or less low efficiency Nash–Sutcliffe (NSE) 0.35 result predicting MR ANFIS. DNN−T both have good performance, with strong 0.75 higher. Evidently, performance r, NSE, root mean square error (RMSE) improved more than DNN. difference between absolute percent (MAPE) small (11%). Regarding ratio verification data, 7:3 8:2 better compared 6:4 indicators, RMSE, MAPE. We assumed phenomenon caused by thinking layer. shows DNN−T, structure DNN, is an alternative estimating safety inspection sites excellent methodology can save budget.

Язык: Английский

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Uncertainty-modulated prediction errors in cortical microcircuits

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Май 12, 2023

Abstract Understanding the variability of environment is essential to function in everyday life. The brain must hence take uncertainty into account when updating its internal model world. basis for are prediction errors that arise from a difference between current and new sensory experiences. Although error neurons have been identified layer 2/3 diverse areas, how modulates these learning is, however, unclear. Here, we use normative approach derive should modulate postulate represent uncertainty-modulated (UPE). We further hypothesise circuit calculates UPE through subtractive divisive inhibition by different inhibitory cell types. By implementing calculation UPEs microcircuit model, show types can compute means variances stimulus distribution. With local activity-dependent plasticity rules, computations be learned context-dependently, allow upcoming stimuli their Finally, mechanism enables an organism optimise strategy via adaptive rates.

Язык: Английский

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Knowing what you don’t know: Estimating the uncertainty of feedforward and feedback inputs with prediction-error circuits

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Дек. 14, 2023

Abstract At any moment, our brains receive a stream of sensory stimuli arising from the world we interact with. Simultaneously, neural circuits are shaped by feedback signals carrying predictions about same inputs experience. Those feedforward and often do not perfectly match. Thus, have challenging task integrating these conflicting streams information according to their reliabilities. However, how keep track both stimulus prediction uncertainty is well understood. Here, propose network model whose core hierarchical prediction-error circuit. We show that can estimate variance using activity negative positive neurons. In line with previous hypotheses, demonstrate rely strongly on if perceived noisy underlying generative process, is, environment stable. Moreover, modulate at onset new stimulus, even this reliable. network, estimation, and, hence, much predictions, be influenced perturbing intricate interplay different inhibitory interneurons. We, therefore, investigate contribution those interneurons weighting inputs. Finally, linked biased perception unravel contribute contraction bias.

Язык: Английский

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Modeling circuit mechanisms of opposing cortical responses to visual flow perturbations

PLoS Computational Biology, Год журнала: 2024, Номер 20(3), С. e1011921 - e1011921

Опубликована: Март 7, 2024

In an ever-changing visual world, animals’ survival depends on their ability to perceive and respond rapidly changing motion cues. The primary cortex (V1) is at the forefront of this sensory processing, orchestrating neural responses perturbations in flow. However, underlying mechanisms that lead distinct cortical such remain enigmatic. study, our objective was uncover dynamics govern V1 neurons’ flow using a biologically realistic computational model. By subjecting model sudden changes input, we observed opposing excitatory layer 2/3 (L2/3) neurons, namely, depolarizing hyperpolarizing responses. We found segregation primarily driven by competition between external input recurrent inhibition, particularly within L2/3 L4. This division not L5/6 suggesting more prominent role for inhibitory processing upper layers. Our findings share similarities with recent experimental studies focusing influence top-down bottom-up inputs mouse during perturbations.

Язык: Английский

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