scholarly journals M-current regulates firing mode and spike reliability in a collision detecting neuron

2018 ◽  
Author(s):  
Richard B. Dewell ◽  
Fabrizio Gabbiani
Keyword(s):  
Computational Modeling ◽  
Collision Detection ◽  
Compound Eye ◽  
Spike Timing ◽  
Burst Firing ◽  
False Alarms ◽  
Movement Detector ◽  
M Current ◽  
Firing Mode

AbstractAll animals must detect impending collisions to escape them, and they must reliably discriminate them from non-threatening stimuli to prevent false alarms. Therefore, it is no surprise that animals have evolved highly selective and sensitive neurons dedicated to such tasks. We examined a well-studied collision detection neuron in the grasshopper Schistocerca americana using in vivo electrophysiology, pharmacology, and computational modeling. This lobula giant movement detector (LGMD) neuron is excitable by inputs originating from each ommatidia of the compound eye, and it has many intrinsic properties that increase its selectivity to objects approaching on a collision course, including switching between burst and non-burst firing. Here, we demonstrate that the LGMD neuron exhibits a large M current, generated by non-inactivating K+ channels, that narrows the window of dendritic integration, regulates a firing mode switch between burst and isolated spiking, increases the precision of spike timing, and increases the reliability of spike propagation to downstream motor centers. By revealing how the M current increases the LGMD’s ability to detect impending collisions our results suggest that it may play an analogous role in other collision detection circuits.New & NoteworthyThe ability to reliably detect impending collisions is a critical survival skill. The nervous systems of many animals have developed dedicated neurons for accomplishing this task. We used a mix of in vivo electrophysiology and computational modeling to investigate the role of M potassium channels within one such collision detecting neuron and showed that through regulation of burst firing and increasing spiking reliability the M current increases the ability to detect impending collisions.

scholarly journals M current regulates firing mode and spike reliability in a collision-detecting neuron

2018 ◽  
Vol 120 (4) ◽  
pp. 1753-1764 ◽  
Author(s):  
Richard B. Dewell ◽  
Fabrizio Gabbiani
Keyword(s):  
Computational Modeling ◽  
Collision Detection ◽  
Compound Eye ◽  
Spike Timing ◽  
False Alarms ◽  
Movement Detector ◽  
In Vivo Electrophysiology ◽  
M Current ◽  
Firing Mode

All animals must detect impending collisions to escape and reliably discriminate them from nonthreatening stimuli, thus preventing false alarms. Therefore, it is no surprise that animals have evolved highly selective and sensitive neurons dedicated to such tasks. We examined a well-studied collision-detection neuron in the grasshopper ( Schistocerca americana) using in vivo electrophysiology, pharmacology, and computational modeling. This lobula giant movement detector (LGMD) neuron is excitable by inputs originating from each ommatidia of the compound eye. It possesses many intrinsic properties that increase its selectivity to objects approaching on a collision course, including switching between burst and nonburst firing. In this study, we demonstrate that the LGMD neuron exhibits a large M current, generated by noninactivating K+ channels, that shortens the temporal window of dendritic integration, regulates a firing mode switch between burst and isolated spiking, increases the precision of spike timing, and increases the reliability of spike propagation to downstream motor centers. By revealing how the M current increases the LGMD’s ability to detect impending collisions, our results suggest that similar channels may play an analogous role in other collision detection circuits. NEW & NOTEWORTHY The ability to reliably detect impending collisions is a critical survival skill. The nervous systems of many animals have developed dedicated neurons for accomplishing this task. We used a mix of in vivo electrophysiology and computational modeling to investigate the role of M potassium channels within one such collision-detecting neuron and show that through regulation of burst firing and enhancement of spiking reliability, the M current increases the ability to detect impending collisions.

scholarly journals Active membrane conductances and morphology of a collision detection neuron broaden its impedance profile and improve membrane synchrony

2018 ◽  
Author(s):  
Richard Dewell ◽  
Fabrizio Gabbiani
Keyword(s):  
Synaptic Input ◽  
Collision Detection ◽  
Dendritic Morphology ◽  
Synaptic Integration ◽  
Neuronal Membrane ◽  
Hcn Channels ◽  
Movement Detector ◽  
Membrane Impedance ◽  
Wide Range

Brains processes information through the coordinated efforts of billions of individual neurons, each encoding a small part of the overall information stream. Central to this is how neurons integrate and transform complex patterns of synaptic inputs. The neuronal membrane impedance sets the gain and timing for synaptic integration, determining a neuron's ability to discriminate between synaptic input patterns. Using single and dual dendritic recordings in vivo, pharmacology, and computational modeling, we characterized the membrane impedance of a collision detection neuron in the grasshopper, Schistocerca americana. We examined how the cellular properties of the lobula giant movement detector (LGMD) neuron are tuned to enable the discrimination of synaptic input patterns key to its role in collision detection. We found that two common active conductances gH and gM, mediated respectively by hyperpolarization-activated cyclic nucleotide gated (HCN) channels and by muscarine sensitive M-type K+ channels, promote broadband integration with high temporal precision over the LGMD's natural range of membrane potentials and synaptic input frequencies. Additionally, we found that the LGMD's branching morphology increased the gain and decreased delays associated with the mapping of synaptic input currents to membrane potential. We investigated whether other branching dendritic morphologies fulfill a similar function and found this to be true for a wide range of morphologies, including those of neocortical pyramidal neurons and cerebellar Purkinje cells. These findings further our understanding of the integration properties of individual neurons by showing the unexpected role played by two widespread active conductances and by dendritic morphology in shaping synaptic integration.

scholarly journals Mechanisms for multiple activity modes of VTA dopamine neurons

2014 ◽  
Author(s):  
Andrew M. Oster ◽  
Philippe Faure ◽  
Boris S. Gutkin
Keyword(s):  
Cell Activity ◽  
Burst Firing ◽  
Dopamine Neurons ◽  
Substantia Nigra Pars Compacta ◽  
Depolarization Block ◽  
Gaba Inhibition ◽  
Potassium Dynamics ◽  
Firing Mode ◽  
Sudden Decrease

Midbrain ventral segmental area (VTA) dopaminergic neurons send numerous projections to cortical and sub-cortical areas, and diffusely release dopamine (DA) to their targets. DA neurons display a range of activity modes that vary in frequency and degree of burst ring. Importantly, DA neuronal bursting is associated with a significantly greater degree of DA release than an equivalent tonic activity pattern. Here, we introduce a single compartmental, conductance-based computational model for DA cell activity that captures the behavior of DA neuronal dynamics and examine the multiple factors that underlie DA firing modes: the strength of the SK conductance, the amount of drive, and GABA inhibition. Our results suggest that neurons with low SK conductance are in a fast firing mode, are correlated with burst firing, and require higher levels of applied current before undergoing depolarization block. We go on to consider the role of GABAergic inhibition on an ensemble of dynamical classes of DA neurons and find that strong GABA inhibition suppresses burst firing. Our studies suggest differences in the distribution of the SK conductance and GABA inhibition levels may indicate subclasses of DA neurons within the VTA. We further identify, that by considering alternate potassium dynamics, the dynamics display burst patterns that terminate via depolarization block, akin to those observed in vivo in VTA DA neurons and in substantia nigra pars compacta DA cell preparations under apamin application. In addition, we consider the generation of transient burst ring events that are NMDA-initiated or elicited by a sudden decrease of GABA inhibition, that is, disinhibition.

scholarly journals Recapitulating Cardiac Structure and Function In Vitro from Simple to Complex Engineering

Micromachines ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 386
Author(s):  
Ana Santos ◽  
Yongjun Jang ◽  
Inwoo Son ◽  
Jongseong Kim ◽  
Yongdoo Park
Keyword(s):  
Tissue Engineering ◽  
Extracellular Matrix ◽  
Computational Modeling ◽  
Cardiac Tissue ◽  
Cardiac Development ◽  
Heart Tissue ◽  
Cardiac Tissue Engineering ◽  
Cardiac Cells

Cardiac tissue engineering aims to generate in vivo-like functional tissue for the study of cardiac development, homeostasis, and regeneration. Since the heart is composed of various types of cells and extracellular matrix with a specific microenvironment, the fabrication of cardiac tissue in vitro requires integrating technologies of cardiac cells, biomaterials, fabrication, and computational modeling to model the complexity of heart tissue. Here, we review the recent progress of engineering techniques from simple to complex for fabricating matured cardiac tissue in vitro. Advancements in cardiomyocytes, extracellular matrix, geometry, and computational modeling will be discussed based on a technology perspective and their use for preparation of functional cardiac tissue. Since the heart is a very complex system at multiscale levels, an understanding of each technique and their interactions would be highly beneficial to the development of a fully functional heart in cardiac tissue engineering.

scholarly journals Dynamic modulation of spike timing-dependent calcium influx during corticostriatal upstates

2013 ◽  
Vol 110 (7) ◽  
pp. 1631-1645 ◽  
Author(s):  
R. C. Evans ◽  
Y. M. Maniar ◽  
K. T. Blackwell
Keyword(s):  
Synaptic Plasticity ◽  
Slow Wave Sleep ◽  
Calcium Influx ◽  
Spike Timing ◽  
Medium Spiny Neuron ◽  
Calcium Transients ◽  
Biophysical Model ◽  
Published Data ◽  
High Calcium

The striatum of the basal ganglia demonstrates distinctive upstate and downstate membrane potential oscillations during slow-wave sleep and under anesthetic. The upstates generate calcium transients in the dendrites, and the amplitude of these calcium transients depends strongly on the timing of the action potential (AP) within the upstate. Calcium is essential for synaptic plasticity in the striatum, and these large calcium transients during the upstates may control which synapses undergo plastic changes. To investigate the mechanisms that underlie the relationship between calcium and AP timing, we have developed a realistic biophysical model of a medium spiny neuron (MSN). We have implemented sophisticated calcium dynamics including calcium diffusion, buffering, and pump extrusion, which accurately replicate published data. Using this model, we found that either the slow inactivation of dendritic sodium channels (NaSI) or the calcium inactivation of voltage-gated calcium channels (CDI) can cause high calcium corresponding to early APs and lower calcium corresponding to later APs. We found that only CDI can account for the experimental observation that sensitivity to AP timing is dependent on NMDA receptors. Additional simulations demonstrated a mechanism by which MSNs can dynamically modulate their sensitivity to AP timing and show that sensitivity to specifically timed pre- and postsynaptic pairings (as in spike timing-dependent plasticity protocols) is altered by the timing of the pairing within the upstate. These findings have implications for synaptic plasticity in vivo during sleep when the upstate-downstate pattern is prominent in the striatum.

Fast Inhibition Alters First Spike Timing in Auditory Brainstem Neurons

2004 ◽  
Vol 92 (4) ◽  
pp. 2615-2621 ◽  
Author(s):  
Antonio G. Paolini ◽  
Janine C. Clarey ◽  
Karina Needham ◽  
Graeme M. Clark
Keyword(s):  
Lateral Inhibition ◽  
Cochlear Nucleus ◽  
Stellate Cell ◽  
Discharge Rate ◽  
Stellate Cells ◽  
Auditory Pathway ◽  
Auditory Brainstem ◽  
Spike Timing ◽  
Inhibitory Neurons

Within the first processing site of the central auditory pathway, inhibitory neurons (D stellate cells) broadly tuned to tonal frequency project on narrowly tuned, excitatory output neurons (T stellate cells). The latter is thought to provide a topographic representation of sound spectrum, whereas the former is thought to provide lateral inhibition that improves spectral contrast, particularly in noise. In response to pure tones, the overall discharge rate in T stellate cells is unlikely to be suppressed dramatically by D stellate cells because they respond primarily to stimulus onset and provide fast, short-duration inhibition. In vivo intracellular recordings from the ventral cochlear nucleus (VCN) showed that, when tones were presented above or below the characteristic frequency (CF) of a T stellate neuron, they were inhibited during depolarization. This resulted in a delay in the initial action potential produced by T stellate cells. This ability of fast inhibition to alter the first spike timing of a T stellate neuron was confirmed by electrically activating the D stellate cell pathway that arises in the contralateral cochlear nucleus. Delay was also induced when two tones were presented: one at CF and one outside the frequency response area of the T stellate neuron. These findings suggest that the traditional view of lateral inhibition within the VCN should incorporate delay as one of its principle outcomes.

Dynamics of neurons in the cat lateral geniculate nucleus: in vivo electrophysiology and computational modeling

1995 ◽  
Vol 74 (3) ◽  
pp. 1222-1243 ◽  
Author(s):  
P. Mukherjee ◽  
E. Kaplan
Keyword(s):  
Computational Modeling ◽  
Spike Train ◽  
Lateral Geniculate Nucleus ◽  
Transfer Functions ◽  
Temporal Frequency ◽  
Relay Cell ◽  
Lateral Geniculate ◽  
Low Pass ◽  
Band Pass

1. We investigated the time domain transformation that thalamocortical relay cells of the cat lateral geniculate nucleus (LGN) perform on their retinal input, and used computational modeling to explore the biophysical properties that determine the dynamics of the LGN relay cells in vivo. 2. We recorded simultaneously the input (S potentials) and output (action potentials) of 50 cat LGN relay cells stimulated by drifting sinusoidal gratings of varying temporal frequency. The temporal modulation transfer functions (TMTFs) of the neurons were derived from these data. The burstiness of the LGN spike trains was also assessed using objective criteria. 3. We found that the form of the TMTF was quite variable among cells, ranging from low-pass to strongly band-pass. The optimal temporal frequency of band-pass neurons was between 2 and 8 Hz. In addition, the TMTF of some cells was nonstationary: their temporal tuning changed with time. 4. The temporal tuning of a cell was directly related to the degree of burstiness of its spike train. Tonically firing relay cells had low-pass TMTFs, whereas the most bursty neurons exhibited the most sharply band-pass transfer functions. This was also true for single cells that altered their temporal tuning: a shift to more band-pass tuning was associated with increased burstiness of the spike train, and vice versa. 5. We constructed a computer simulation of the LGN relay cell. The model was a simplified five-channel version of the thalamocortical neuron model of McCormick and Huguenard. It incorporated the quantitative kinetics of the Ca2+ T channel, as well as the Hodgkin-Huxley Na+ and K+ channels, as the only active membrane currents. To simulate the in vivo dynamics of the relay cell, the input to the model consisted of trains of synaptic potentials, recorded as S potentials in our physiological experiments. 6. When the resting membrane potential of the model neuron was relatively depolarized, the model's TMTF was low-pass, with no bursting evident in the simulated spike train. At hyperpolarized resting membrane potentials, however, the modeled TMTF was band-pass, with frequent burst discharges. Thus the biophysical model reproduced not only the range of dynamics seen in real LGN relay cells, but also the dependence of the overall dynamics on the burstiness of the spike train. However, neither of these phenomena could be simulated without the T channel. Thus the simulations demonstrated that the T-type Ca2+ channel was necessary and sufficient to explain the LGN dynamics observed in physiological experiments.(ABSTRACT TRUNCATED AT 400 WORDS)

Exact Event-Driven Implementation for Recurrent Networks of Stochastic Perfect Integrate-and-Fire Neurons

2012 ◽  
Vol 24 (12) ◽  
pp. 3145-3180 ◽  
Author(s):  
Thibaud Taillefumier ◽  
Jonathan Touboul ◽  
Marcelo Magnasco
Keyword(s):  
Intrinsic Noise ◽  
Spike Timing ◽  
Temporal Coding ◽  
Recurrent Networks ◽  
Neural Noise ◽  
Integrate And Fire ◽  
Discretization Schemes ◽  
Event Driven ◽  
Random Variability

In vivo cortical recording reveals that indirectly driven neural assemblies can produce reliable and temporally precise spiking patterns in response to stereotyped stimulation. This suggests that despite being fundamentally noisy, the collective activity of neurons conveys information through temporal coding. Stochastic integrate-and-fire models delineate a natural theoretical framework to study the interplay of intrinsic neural noise and spike timing precision. However, there are inherent difficulties in simulating their networks’ dynamics in silico with standard numerical discretization schemes. Indeed, the well-posedness of the evolution of such networks requires temporally ordering every neuronal interaction, whereas the order of interactions is highly sensitive to the random variability of spiking times. Here, we answer these issues for perfect stochastic integrate-and-fire neurons by designing an exact event-driven algorithm for the simulation of recurrent networks, with delayed Dirac-like interactions. In addition to being exact from the mathematical standpoint, our proposed method is highly efficient numerically. We envision that our algorithm is especially indicated for studying the emergence of polychronized motifs in networks evolving under spike-timing-dependent plasticity with intrinsic noise.

scholarly journals Sparse bursts optimize information transmission in a multiplexed neural code

2018 ◽  
Vol 115 (27) ◽  
pp. E6329-E6338 ◽  
Author(s):  
Richard Naud ◽  
Henning Sprekeler
Keyword(s):  
Firing Rate ◽  
Cortical Neurons ◽  
Neural Code ◽  
Spike Timing ◽  
Neural Ensemble ◽  
Multiple Input ◽  
Cortical Hierarchy ◽  
Connectivity Patterns ◽  
Rate Coding

Many cortical neurons combine the information ascending and descending the cortical hierarchy. In the classical view, this information is combined nonlinearly to give rise to a single firing-rate output, which collapses all input streams into one. We analyze the extent to which neurons can simultaneously represent multiple input streams by using a code that distinguishes spike timing patterns at the level of a neural ensemble. Using computational simulations constrained by experimental data, we show that cortical neurons are well suited to generate such multiplexing. Interestingly, this neural code maximizes information for short and sparse bursts, a regime consistent with in vivo recordings. Neurons can also demultiplex this information, using specific connectivity patterns. The anatomy of the adult mammalian cortex suggests that these connectivity patterns are used by the nervous system to maintain sparse bursting and optimal multiplexing. Contrary to firing-rate coding, our findings indicate that the physiology and anatomy of the cortex may be interpreted as optimizing the transmission of multiple independent signals to different targets.

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