Synaptic Democracy in Active Dendrites

2006 ◽  
Vol 96 (5) ◽  
pp. 2307-2318 ◽  
Author(s):  
Clifton C. Rumsey ◽  
L. F. Abbott
Keyword(s):  
Action Potentials ◽  
Dendritic Tree ◽  
Pyramidal Cells ◽  
Spike Timing ◽  
Dominant Factor ◽  
Dependent Plasticity ◽  
Ca1 Pyramidal Cells ◽  
Active Dendrites ◽  
Hebbian Plasticity ◽  
The Impact

Given the extensive attenuation that can occur along dendritic cables, location within the dendritic tree might appear to be a dominant factor in determining the impact of a synapse on the postsynaptic response. By this reasoning, distal synapses should have a smaller effect than proximal ones. However, experimental evidence from several types of neurons, such as CA1 pyramidal cells, indicates that a compensatory strengthening of synapses counteracts the effect of location on synaptic efficacy. A form of spike-timing-dependent plasticity (STDP), called anti-STDP, combined with non-Hebbian activity-dependent plasticity can account for the equalization of synaptic efficacies. This result, obtained originally in models with unbranched passive cables, also arises in multi-compartment models with branched and active dendrites that feature backpropagating action potentials, including models with CA1 pyramidal morphologies. Additionally, when dendrites support the local generation of action potentials, anti-STDP prevents runaway dendritic spiking and locally balances the numbers of dendritic and backpropagating action potentials. Thus in multiple ways, anti-STDP eliminates the location dependence of synapses and allows Hebbian plasticity to operate in a more “democratic” manner.

scholarly journals Computer modeling of hippocampal CA1 pyramidal cells - a tool for in silico experiments

2015 ◽  
Vol 61 (1) ◽  
pp. 15-24 ◽  
Author(s):  
Júlia Metz ◽  
T. Szilágyi ◽  
M. Perian ◽  
K. Orbán-Kis
Keyword(s):  
Pyramidal Cell ◽  
In Silico ◽  
Action Potentials ◽  
Firing Pattern ◽  
Cell Model ◽  
Pyramidal Cells ◽  
Spike Timing ◽  
Network Activity ◽  
Ca1 Pyramidal Cells ◽  
Neuronal Network Activity

Abstract Objective. In silico experiments use mathematical models that capture as much as possible from the properties of the biological system under investigation. Our aim was to test the publicly available CA1 pyramidal cell models using the same simulation tasks, to compare them, and provide a systematic overview of their properties in order to improve the usefulness of these models as a tool for in silico experiments. Methods. Parameters describing the morphology of the cells and the implemented biophysical mechanisms were collected from the Model DB database of Sense Lab Project. This data was analyzed in correlation with the purpose for which each particular model was developed. Multicompartmental simulations were run using the Neuron modeling platform. The properties of the action potentials generated in response to current injection, the firing pattern and the dendritic back-propagation were analyzed. Results. The studied models were optimized to explore different physiological and pathological properties of the CA1 pyramidal cells. We could identify four broad classes of models focusing on: (i) initiation of the action potential, firing pattern and spike timing, (ii) dendritic backpropagation, (iii) dendritic integration of synaptic inputs and (iv) neuronal network activity. Despite the large variation of the active conductances implemented in the models, the properties of the individual action potentials were quite similar, but even the most complex models could not reproduce all studied biological phenomena. Conclusions. At the moment the “perfect” pyramidal cell model is not yet available. Our work, hopefully, will help finding the best model for each scientific question under investigation.

Interregional synaptic competition in neurons with multiple STDP-inducing signals

2011 ◽  
Vol 105 (3) ◽  
pp. 989-998 ◽  
Author(s):  
Lital Bar Ilan ◽  
Albert Gidon ◽  
Idan Segev
Keyword(s):  
Computational Study ◽  
Dendritic Tree ◽  
Pyramidal Cells ◽  
Spike Timing ◽  
Detailed Model ◽  
Synaptic Competition ◽  
Hebbian Plasticity ◽  
Regional Population ◽  
Cell Firing ◽  
Spike Initiation

Neocortical layer 5 (L5) pyramidal cells have at least two spike initiation zones: Na+ spikes are generated near the soma, and Ca2+ spikes at the apical dendritic tuft. These spikes interact with each other and serve as signals for synaptic plasticity. The present computational study explores the implications of having two spike-timing-dependent plasticity (STDP) signals in a neuron, each with its respective regional population of synaptic “pupils.” In a detailed model of an L5 pyramidal neuron, competition emerges between synapses belonging to different regions, on top of the competition among synapses within each region, which characterizes the STDP mechanism. Interregional competition results in strengthening of one group of synapses, which ultimately dominates cell firing, at the expense of weakening synapses in other regions. This novel type of competition is inherent to dendrites with multiple regional signals for Hebbian plasticity. Surprisingly, such interregional competition exists even in a simplified model of two identical coupled compartments. We find that in a model of an L5 pyramidal cell, the different synaptic subpopulations “live in peace” when the induction of Ca2+ spikes requires the back-propagating action potential (BPAP). Thus we suggest a new key role for the BPAP, to maintain the balance between synaptic efficacies throughout the dendritic tree, thereby sustaining the functional integrity of the entire neuron.

Spike-Timing-Dependent Hebbian Plasticity as Temporal Difference Learning

2001 ◽  
Vol 13 (10) ◽  
pp. 2221-2237 ◽  
Author(s):  
Rajesh P. N. Rao ◽  
Terrence J. Sejnowski
Keyword(s):  
Cortical Neuron ◽  
Action Potentials ◽  
Learning Rule ◽  
Spike Timing ◽  
Biophysical Model ◽  
Excitatory Synapses ◽  
Temporal Difference ◽  
Temporal Difference Learning ◽  
Neocortical Neurons ◽  
Hebbian Plasticity

A spike-timing-dependent Hebbian mechanism governs the plasticity of recurrent excitatory synapses in the neocortex: synapses that are activated a few milliseconds before a postsynaptic spike are potentiated, while those that are activated a few milliseconds after are depressed. We show that such a mechanism can implement a form of temporal difference learning for prediction of input sequences. Using a biophysical model of a cortical neuron, we show that a temporal difference rule used in conjunction with dendritic backpropagating action potentials reproduces the temporally asymmetric window of Hebbian plasticity observed physiologically. Furthermore, the size and shape of the window vary with the distance of the synapse from the soma. Using a simple example, we show how a spike-timing-based temporal difference learning rule can allow a network of neocortical neurons to predict an input a few milliseconds before the input's expected arrival.

Network Interactions Mediated by New Excitatory Connections Between CA1 Pyramidal Cells in Rats With Kainate-Induced Epilepsy

2002 ◽  
Vol 87 (3) ◽  
pp. 1655-1658 ◽  
Author(s):  
Bret N. Smith ◽  
F. Edward Dudek
Keyword(s):  
Status Epilepticus ◽  
Action Potentials ◽  
Cell Layer ◽  
Hippocampal Slices ◽  
Pyramidal Cells ◽  
Ca1 Pyramidal Cells ◽  
Axon Sprouting ◽  
Synaptic Interactions ◽  
Network Bursts ◽  
Ca1 Area

Axon sprouting and synaptic reorganization in the hippocampus are associated with the development of seizures in temporal lobe epilepsy. Synaptic interactions among CA1 pyramidal cells were examined in fragments of hippocampal slices containing only the CA1 area from saline- and kainate-treated rats. Glutamate microapplication to the pyramidal cell layer increased excitatory postsynaptic current (EPSC) frequency, but only in rats with kainate-induced epilepsy. In bicuculline, action potentials evoked in single pyramidal cells increased the frequency of network bursts only in slices from rats with kainate-induced epilepsy. These data further support the hypothesis that excitatory connections between CA1 pyramidal cells increase after kainate-induced status epilepticus.

scholarly journals A Classification Method to Distinguish Cell-Specific Responses Elicited by Current Pulses in Hippocampal CA1 Pyramidal Cells

2008 ◽  
Vol 20 (6) ◽  
pp. 1512-1536 ◽  
Author(s):  
José Ambros-Ingerson ◽  
Lawrence M. Grover ◽  
William R. Holmes
Keyword(s):  
Distance Measure ◽  
Network Models ◽  
Pyramidal Cells ◽  
Spike Timing ◽  
Voltage Gradient ◽  
Neural Function ◽  
Ca1 Pyramidal Cells ◽  
Current Pulses ◽  
Point Distance

The suprathreshold electrophysiological responses of pyramidal cells have been grouped into large classes such as bursting and spiking. However, it is not known whether, within a class, response variability ranges uniformly across all cells or whether each cell has a unique and consistent profile that can be differentiated. A major difficulty when comparing suprathreshold responses is that slight variations in spike timing in otherwise very similar traces render traditional metrics ineffective. To address these issues, we developed a novel distance measure based on fiducial points to quantify the similarity among traces with trains of action potentials and applied it together with classification techniques to a set of in vitro patch clamp recordings from CA1 pyramidal cells. We tested if responses to repeated current stimulation of a given cell would cluster together yet remain distinct from those of other cells. We found that depolarizing and hyperpolarizing current pulses elicited responses in each cell that clustered and were systematically distinguishable from responses in other cells. The fiducial-point distance measure was more effective than other methods based on spike times and voltage-gradient phase planes. Depolarizing traces were more reliably differentiated than hyperpolarizing traces, and combining both scores was even more effective. These results suggest that each CA1 pyramidal cell has unique properties that can be detected and quantified with methods discussed here. This uniqueness may be due to slight variations in morphology or membrane channel densities and kinetics, or to large, coordinated variations in these elements. Ascertaining the actual sources and their degree of variability is important when constructing network models of neural function to ensure that key mechanisms are robust in the face of variations within these ranges. The analytical tools presented here can assist in constructing detailed cell models to match experimental records to elucidate the sources of electrophysiological variability in neurons.

Spike-Timing-Dependent Plasticity Using Biologically Realistic Action Potentials and Low-Temperature Materials

2013 ◽  
Vol 12 (3) ◽  
pp. 450-459 ◽  
Author(s):  
Anand Subramaniam ◽  
Kurtis D. Cantley ◽  
Gennadi Bersuker ◽  
David C. Gilmer ◽  
Eric M. Vogel
Keyword(s):  
Low Temperature ◽  
Action Potentials ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity
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