A Model for Time Interval Learning in The Purkinje Cell

Mapping Intimacies ◽

10.1101/448027 ◽

2018 ◽

Cited By ~ 1

Author(s):

Daniel Majoral ◽

Ajmal Zemmar ◽

Raul Vicente

Keyword(s):

Purkinje Cell ◽

Purkinje Cells ◽

Classical Theory ◽

Biophysical Model ◽

Calcium Signal ◽

Time Interval ◽

Time Intervals ◽

Delay Conditioning ◽

Cerebellar Function ◽

Experimental Findings

AbstractRecent experimental findings indicate that Purkinje cells in the cerebellum represent time intervals by mechanisms other than conventional synaptic weights. This finding adds to the theoretical and experimental observations suggesting the presence of intra-cellular mechanisms for adaptation and processing. To account for these experimental results we developed a biophysical model for time interval learning in a Purkinje cell. The numerical model focuses on a classical delay conditioning task (e.g. eyeblink conditioning) and relies on a few computational steps. In particular, the model posits the activation by the parallel fiber input of a local intra-cellular calcium store which can be modulated by intra-cellular pathways. The reciprocal interaction of the calcium signal with several proteins forming negative and positive feedback loops ensures that the timing of inhibition in the Purkinje cell anticipates the interval between parallel and climbing fiber inputs during training. We show that the model is able to learn along the 150-1000 ms interval range. Finally, we discuss how this model would allow the cerebellum to detect and generate specific spatio-temporal patterns, a classical theory for cerebellar function.Author SummaryThe prevailing view in neurosciences considers synaptic weights between neurons the determinant factor for learning and processing information in the nervous system. Theoretical considerations [1, 2] and experiments [3, 4] examined some potential limitations of this classical paradigm, pointing out that adaptation and computation might also have to rely on other mechanisms besides the learning of synaptic weights. Recent experimental findings [5–7] indicate that Purkinje cells in the cerebellum represent time intervals by mechanisms other than conventional synaptic weights. We propose here a biologically plausible model which complements the modification of synaptic weights for learning one time interval in one synapse of one Purkinje cell. In the model a calcium signal in a small domain keeps track of time. Several molecules read and modify this calcium signal to learn a time interval. We discuss how this model would allow the cerebellum to detect and generate specific patterns in space and time, a classical theory for cerebellar function proposed by Braitenberg [8, 9].

A biochemical mechanism for time-encoding memory formation within individual synapses of Purkinje cells

PLoS ONE ◽

10.1371/journal.pone.0251172 ◽

2021 ◽

Vol 16 (5) ◽

pp. e0251172

Author(s):

Ayush Mandwal ◽

Javier G. Orlandi ◽

Christoph Simon ◽

Jörn Davidsen

Keyword(s):

Purkinje Cell ◽

Firing Rate ◽

G Protein ◽

Purkinje Cells ◽

Memory Formation ◽

Time Interval ◽

Biochemical Mechanism ◽

Conditional Response ◽

Tonic Firing ◽

Time Encoding

Within the classical eye-blink conditioning, Purkinje cells within the cerebellum are known to suppress their tonic firing rates for a well defined time period in response to the conditional stimulus after training. The temporal profile of the drop in tonic firing rate, i.e., the onset and the duration, depend upon the time interval between the onsets of the conditional and unconditional training stimuli. Direct stimulation of parallel fibers and climbing fiber by electrodes was found to be sufficient to reproduce the same characteristic drop in the firing rate of the Purkinje cell. In addition, the specific metabotropic glutamate-based receptor type 7 (mGluR7) was found responsible for the initiation of the response, suggesting an intrinsic mechanism within the Purkinje cell for the temporal learning. In an attempt to look for a mechanism for time-encoding memory formation within individual Purkinje cells, we propose a biochemical mechanism based on recent experimental findings. The proposed mechanism tries to answer key aspects of the “Coding problem” of Neuroscience by focusing on the Purkinje cell’s ability to encode time intervals through training. According to the proposed mechanism, the time memory is encoded within the dynamics of a set of proteins—mGluR7, G-protein, G-protein coupled Inward Rectifier Potassium ion channel, Protein Kinase A, Protein Phosphatase 1 and other associated biomolecules—which self-organize themselves into a protein complex. The intrinsic dynamics of these protein complexes can differ and thus can encode different time durations. Based on their amount and their collective dynamics within individual synapses, the Purkinje cell is able to suppress its own tonic firing rate for a specific time interval. The time memory is encoded within the effective dynamics of the biochemical reactions and altering these dynamics means storing a different time memory. The proposed mechanism is verified by both a minimal and a more comprehensive mathematical model of the conditional response behavior of the Purkinje cell and corresponding dynamical simulations of the involved biomolecules, yielding testable experimental predictions.

A biochemical mechanism for time-encoding memory formation within individual synapses of Purkinje cells

10.1101/596148 ◽

2019 ◽

Author(s):

Ayush Mandwal ◽

Javier G. Orlandi ◽

Christoph Simon ◽

Jörn Davidsen

Keyword(s):

Purkinje Cell ◽

Firing Rate ◽

Purkinje Cells ◽

Protein Interactions ◽

Effective Rate ◽

Memory Formation ◽

Time Interval ◽

Biochemical Mechanism ◽

Tonic Firing ◽

Time Encoding

AbstractWithin the classical eye-blink conditioning, Purkinje cells within the cerebellum are known to suppress their tonic firing rates for a well defined time period in response to the conditional stimulus after training. The temporal profile of the drop in tonic firing rate, i.e., the onset and the duration, depend upon the time interval between the onsets of the training conditional and unconditional stimulus. Direct stimulation of parallel fibers and climbing fiber by electrodes was found to be sufficient to reproduce the same characteristic drop in the firing rate of the Purkinje cell. In addition, the specific metabotropic glutamate-based receptor type 7 (mGluR7), which resides on the Purkinje cell synapses, was found responsible for the initiation of the response, suggesting an intrinsic mechanism within the Purkinje cell for the temporal learning. In an attempt to look for a mechanism for time-encoding memory formation within individual Purkinje cells, we propose a biochemical mechanism based on recent experimental findings. The proposed model tries to answer key aspects of the “Coding problem” of Neuroscience by focussing on the Purkinje cell’s ability to encode time intervals through training. According to the proposed mechanism, the time memory is encoded within the dynamics of a set of proteins — mGluR7, G-protein, G-protein coupled Inward Rectifier Potassium ion channel, Protein Kinase A and Protein Phosphatase 1 — which self-organize themselves into a protein complex. The intrinsic dynamics of these protein complexes can differ and thus can encode different time durations. Based on their amount and their collective dynamics within individual synapses, the Purkinje cell is able to suppress its own tonic firing rate for a specific time interval. The time memory is encoded within the effective rate constants of the biochemical reactions and altering these rates constants means storing a different time memory. The proposed mechanism is verified by a simplified mathematical model and corresponding dynamical simulations of the involved biomolecules, yielding testable experimental predictions.Author summaryHebbian plasticity is a widely accepted form of learning that can encode memories in our brain. Spike-timing dependent plasticity resulting in Long-term Potentiation or Depression of synapses has become the default mechanistic explanation behind memory formation within a neuronal population. However, recent experiments of conditional eyeblink response in Purkinje cells have challenged this point of view by showing that these mechanisms alone cannot account for time memory formation in the Purkinje cell. To explain the underlying mechanism behind this novel synaptic plasticity, we introduce a biochemical mechanism based on protein interactions occurring within a single synapse. These protein interactions and the associated effective rate constants are sufficient to encode time delays by auto-induced inhibition on a single excitatory synapse, suggesting that synapses are capable of storing more information than previously thought.

Purkinje Cell Representations of Behavior: Diary of a Busy Neuron

The Neuroscientist ◽

10.1177/1073858418785628 ◽

2018 ◽

Vol 25 (3) ◽

pp. 241-257 ◽

Cited By ~ 4

Author(s):

Laurentiu S. Popa ◽

Martha L. Streng ◽

Timothy J. Ebner

Keyword(s):

Purkinje Cell ◽

Purkinje Cells ◽

Motor Behavior ◽

Cell Activity ◽

Computational Framework ◽

Simple Spike ◽

Cerebellar Function ◽

Performance Error ◽

And Performance ◽

High Degree

Fundamental for understanding cerebellar function is determining the representations in Purkinje cell activity, the sole output of the cerebellar cortex. Up to the present, the most accurate descriptions of the information encoded by Purkinje cells were obtained in the context of motor behavior and reveal a high degree of heterogeneity of kinematic and performance error signals encoded. The most productive framework for organizing Purkinje cell firing representations is provided by the forward internal model hypothesis. Direct tests of this hypothesis show that individual Purkinje cells encode two different forward models simultaneously, one for effector kinematics and one for task performance. Newer results demonstrate that the timing of simple spike encoding of motor parameters spans an extend interval of up to ±2 seconds. Furthermore, complex spike discharge is not limited to signaling errors, can be predictive, and dynamically controls the information in the simple spike firing to meet the demands of upcoming behavior. These rich, diverse, and changing representations highlight the integrative aspects of cerebellar function and offer the opportunity to generalize the cerebellar computational framework over both motor and non-motor domains.

A Cerebellar Computational Mechanism for Delay Conditioning at Precise Time Intervals

Neural Computation ◽

10.1162/neco_a_01318 ◽

2020 ◽

Vol 32 (11) ◽

pp. 2069-2084

Author(s):

Terence D. Sanger ◽

Mitsuo Kawato

Keyword(s):

Conditioned Stimulus ◽

Purkinje Cells ◽

Granule Cells ◽

Mossy Fiber ◽

Mossy Fibers ◽

Long Term Potentiation ◽

Time Intervals ◽

Delay Conditioning ◽

Precise Time

The cerebellum is known to have an important role in sensing and execution of precise time intervals, but the mechanism by which arbitrary time intervals can be recognized and replicated with high precision is unknown. We propose a computational model in which precise time intervals can be identified from the pattern of individual spike activity in a population of parallel fibers in the cerebellar cortex. The model depends on the presence of repeatable sequences of spikes in response to conditioned stimulus input. We emulate granule cells using a population of Izhikevich neuron approximations driven by random but repeatable mossy fiber input. We emulate long-term depression (LTD) and long-term potentiation (LTP) synaptic plasticity at the parallel fiber to Purkinje cell synapse. We simulate a delay conditioning paradigm with a conditioned stimulus (CS) presented to the mossy fibers and an unconditioned stimulus (US) some time later issued to the Purkinje cells as a teaching signal. We show that Purkinje cells rapidly adapt to decrease firing probability following onset of the CS only at the interval for which the US had occurred. We suggest that detection of replicable spike patterns provides an accurate and easily learned timing structure that could be an important mechanism for behaviors that require identification and production of precise time intervals.

Firing Dynamics of Cerebellar Purkinje Cells

Journal of Neurophysiology ◽

10.1152/jn.00306.2007 ◽

2007 ◽

Vol 98 (1) ◽

pp. 278-294 ◽

Cited By ~ 48

Author(s):

Fernando R. Fernandez ◽

Jordan D. T. Engbers ◽

Ray W. Turner

Keyword(s):

Purkinje Cell ◽

Purkinje Cells ◽

Low Frequency ◽

High Gain ◽

Neuronal Firing ◽

Biophysical Model ◽

State Transitions ◽

Cell Dynamics ◽

Low Frequencies ◽

Cerebellar Purkinje Cells

Knowledge of intrinsic neuronal firing dynamics is a critical first step to establishing an accurate biophysical model of any neuron. In this study we examined cerebellar Purkinje cells to determine the bifurcations likely to underlie firing dynamics within a biophysically realistic and experimentally supported model. We show that Purkinje cell dynamics are consistent with a system undergoing a saddle-node bifurcation of fixed points in the transition from rest to firing and a saddle homoclinic bifurcation from firing to rest. Our analyses account for numerous observed Purkinje cell firing properties that include bistability, plateau potentials, specific aspects of the frequency–current ( F– I) relationship, first spike latency, and the ability for climbing fiber input to induce state transitions in the bistable regime. We also experimentally confirm new properties predicted from our model and analysis that include the presence of a depolarizing afterpotential (DAP), the ability to fire at low frequencies (<50 Hz) and with a high gain in the F– I relationship, and a bistable region limited to low-frequency firing. Purkinje cell dynamics, including bistability, prove to arise from numerous biophysical factors that include the DAP, fast refractory dynamics, and a long membrane time constant. A hyperpolarizing activated cation current ( IH) is shown not to be directly involved in establishing bistable dynamics but rather reduces the range for bistability. A combined electrophysiological and modeling approach thus accounts for several properties of Purkinje cells, providing a firm basis from which to assess Purkinje cell output patterns.

METABOLIC STUDIES WITH 131I-LABELED THYROXINE. II.

Acta Endocrinologica ◽

10.1530/acta.0.0440475 ◽

1963 ◽

Vol 44 (3) ◽

pp. 475-480 ◽

Cited By ~ 1

Author(s):

R. Grinberg

Keyword(s):

Spinal Cord ◽

Central Nervous System ◽

Nervous System ◽

Optic Nerve ◽

Pituitary Gland ◽

Time Interval ◽

Time Intervals ◽

Pituitary Glands ◽

The Central Nervous System ◽

Tentative Explanation

ABSTRACT Radiologically thyroidectomized female Swiss mice were injected intraperitoneally with 131I-labeled thyroxine (T4*), and were studied at time intervals of 30 minutes and 4, 28, 48 and 72 hours after injection, 10 mice for each time interval. The organs of the central nervous system and the pituitary glands were chromatographed, and likewise serum from the same animal. The chromatographic studies revealed a compound with the same mobility as 131I-labeled triiodothyronine in the organs of the CNS and in the pituitary gland, but this compound was not present in the serum. In most of the chromatographic studies, the peaks for I, T4 and T3 coincided with those for the standards. In several instances, however, such an exact coincidence was lacking. A tentative explanation for the presence of T3* in the pituitary gland following the injection of T4* is a deiodinating system in the pituitary gland or else the capacity of the pituitary gland to concentrate T3* formed in other organs. The presence of T3* is apparently a characteristic of most of the CNS (brain, midbrain, medulla and spinal cord); but in the case of the optic nerve, the compound is not present under the conditions of this study.

Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm

Energies ◽

10.3390/en14041213 ◽

2021 ◽

Vol 14 (4) ◽

pp. 1213

Author(s):

Ahmed Aljanad ◽

Nadia M. L. Tan ◽

Vassilios G. Agelidis ◽

Hussain Shareef

Keyword(s):

Neural Networks ◽

Particle Swarm Optimization ◽

Solar Irradiance ◽

Particle Swarm ◽

Time Interval ◽

Time Intervals ◽

Swarm Optimization ◽

Backpropagation Neural Networks ◽

Proposed Model ◽

Short Time

Hourly global solar irradiance (GSR) data are required for sizing, planning, and modeling of solar photovoltaic farms. However, operating and controlling such farms exposed to varying environmental conditions, such as fast passing clouds, necessitates GSR data to be available for very short time intervals. Classical backpropagation neural networks do not perform satisfactorily when predicting parameters within short intervals. This paper proposes a hybrid backpropagation neural networks based on particle swarm optimization. The particle swarm algorithm is used as an optimization algorithm within the backpropagation neural networks to optimize the number of hidden layers and neurons used and its learning rate. The proposed model can be used as a reliable model in predicting changes in the solar irradiance during short time interval in tropical regions such as Malaysia and other regions. Actual global solar irradiance data of 5-s and 1-min intervals, recorded by weather stations, are applied to train and test the proposed algorithm. Moreover, to ensure the adaptability and robustness of the proposed technique, two different cases are evaluated using 1-day and 3-days profiles, for two different time intervals of 1-min and 5-s each. A set of statistical error indices have been introduced to evaluate the performance of the proposed algorithm. From the results obtained, the 3-days profile’s performance evaluation of the BPNN-PSO are 1.7078 of RMSE, 0.7537 of MAE, 0.0292 of MSE, and 31.4348 of MAPE (%), at 5-s time interval, where the obtained results of 1-min interval are 0.6566 of RMSE, 0.2754 of MAE, 0.0043 of MSE, and 1.4732 of MAPE (%). The results revealed that proposed model outperformed the standalone backpropagation neural networks method in predicting global solar irradiance values for extremely short-time intervals. In addition to that, the proposed model exhibited high level of predictability compared to other existing models.

Real-World Experience with Artificial Intelligence-Based Triage in Transferred Large Vessel Occlusion Stroke Patients

Cerebrovascular Diseases ◽

10.1159/000515320 ◽

2021 ◽

pp. 1-6

Author(s):

Jacob R. Morey ◽

Xiangnan Zhang ◽

Kurt A. Yaeger ◽

Emily Fiano ◽

Naoum Fares Marayati ◽

...

Keyword(s):

Artificial Intelligence ◽

Clinical Outcomes ◽

Warning System ◽

Large Vessel ◽

Time Interval ◽

Large Vessel Occlusion ◽

Time Intervals ◽

Vessel Occlusion ◽

Left Behind ◽

Stroke Center

Background and Purpose: Randomized controlled trials have demonstrated the importance of time to endovascular therapy (EVT) in clinical outcomes in large vessel occlusion (LVO) acute ischemic stroke. Delays to treatment are particularly prevalent when patients require a transfer from hospitals without EVT capability onsite. A computer-aided triage system, Viz LVO, has the potential to streamline workflows. This platform includes an image viewer, a communication system, and an artificial intelligence (AI) algorithm that automatically identifies suspected LVO strokes on CTA imaging and rapidly triggers alerts. We hypothesize that the Viz application will decrease time-to-treatment, leading to improved clinical outcomes. Methods: A retrospective analysis of a prospectively maintained database was assessed for patients who presented to a stroke center currently utilizing Viz LVO and underwent EVT following transfer for LVO stroke between July 2018 and March 2020. Time intervals and clinical outcomes were compared for 55 patients divided into pre- and post-Viz cohorts. Results: The median initial door-to-neuroendovascular team (NT) notification time interval was significantly faster (25.0 min [IQR = 12.0] vs. 40.0 min [IQR = 61.0]; p = 0.01) with less variation (p < 0.05) following Viz LVO implementation. The median initial door-to-skin puncture time interval was 25 min shorter in the post-Viz cohort, although this was not statistically significant (p = 0.15). Conclusions: Preliminary results have shown that Viz LVO implementation is associated with earlier, more consistent NT notification times. This application can serve as an early warning system and a failsafe to ensure that no LVO is left behind.

Optimal Perturbations of an Oceanic Vortex Lens

Fluids ◽

10.3390/fluids3030063 ◽

2018 ◽

Vol 3 (3) ◽

pp. 63 ◽

Cited By ~ 3

Author(s):

Thomas Meunier ◽

Claire Ménesguen ◽

Xavier Carton ◽

Sylvie Le Gentil ◽

Richard Schopp

Keyword(s):

Normal Mode ◽

Growth Rates ◽

Time Interval ◽

Time Intervals ◽

Horizontal Structure ◽

Azimuthal Wave ◽

Wave Numbers ◽

Non Linear ◽

Long Time ◽

Short Time

The stability properties of a vortex lens are studied in the quasi geostrophic (QG) framework using the generalized stability theory. Optimal perturbations are obtained using a tangent linear QG model and its adjoint. Their fine-scale spatial structures are studied in details. Growth rates of optimal perturbations are shown to be extremely sensitive to the time interval of optimization: The most unstable perturbations are found for time intervals of about 3 days, while the growth rates continuously decrease towards the most unstable normal mode, which is reached after about 170 days. The horizontal structure of the optimal perturbations consists of an intense counter-shear spiralling. It is also extremely sensitive to time interval: for short time intervals, the optimal perturbations are made of a broad spectrum of high azimuthal wave numbers. As the time interval increases, only low azimuthal wave numbers are found. The vertical structures of optimal perturbations exhibit strong layering associated with high vertical wave numbers whatever the time interval. However, the latter parameter plays an important role in the width of the vertical spectrum of the perturbation: short time interval perturbations have a narrow vertical spectrum while long time interval perturbations show a broad range of vertical scales. Optimal perturbations were set as initial perturbations of the vortex lens in a fully non linear QG model. It appears that for short time intervals, the perturbations decay after an initial transient growth, while for longer time intervals, the optimal perturbation keeps on growing, quickly leading to a non-linear regime or exciting lower azimuthal modes, consistent with normal mode instability. Very long time intervals simply behave like the most unstable normal mode. The possible impact of optimal perturbations on layering is also discussed.

Effect of mechanical pressure-controlled ventilation in patients with disturbed respiratory function during laparoscopic cholecystectomy

Vojnosanitetski pregled ◽

10.2298/vsp1301009s ◽

2013 ◽

Vol 70 (1) ◽

pp. 9-15

Author(s):

Maja Surbatovic ◽

Zoran Vesic ◽

Dragan Djordjevic ◽

Sonja Radakovic ◽

Snjezana Zeba ◽

...

Keyword(s):

Laparoscopic Cholecystectomy ◽

Respiratory Function ◽

Respiratory Diseases ◽

Positive Pressure ◽

Time Interval ◽

Time Intervals ◽

Controlled Ventilation ◽

Arterial Blood ◽

Pressure Controlled Ventilation ◽

Pressure Controlled

Background/Aim: Laparoscopic cholecystectomy is considered to be the gold standard for laparoscopic surgical procedures. In ASA III patients with concomitant respiratory diseases, however, creation of pneumoperitoneum and the position of patients during surgery exert additional negative effect on intraoperative respiratory function, thus making a higher challenge for the anesthesiologist than for the surgeon. The aim of this study was to compare the effect of intermittent positive pressure ventilation (IPPV) and pressure controlled ventilation (PCV) during general anesthesia on respiratory function in ASA III patients submitted to laparoscopic cholecystectomy. Methods. The study included 60 patients randomized into two groups depending on the mode of ventilation: IPPV or PCV. Respiratory volume (VT), peak inspiratory pressure (PIP), compliance (C), end-tidal CO2 pressure (PETCO2), oxygen saturation (SpO2), partial pressures of O2, CO2 (PaO2 and PaCO2) and pH of arterial blood were recorded within four time intervals. Results. There were no statistically significant differences in VT, SpO2, PaO2, PaCO2 and pH values neither within nor between the two groups. In time interval t1 there were no statistically significant differences in PIP, C, PETCO2 values between the IPPV and the PCV group. But, in the next three time intervals there was a difference in PIP, C, and PETCO2 values between the two groups which ranged from statistically significant to highly significant; PIP was lower, C and PETCO2 were higher in the PCV group. Conclusion. Pressure controlled ventilation better maintains stability regarding intraoperative ventilatory parameters in ASA III patients with concomitant respiratory diseases during laparoscopic cholecystectomy.