scholarly journals A New Fiji-Based Algorithm That Systematically Quantifies Nine Synaptic Parameters Provides Insights into Drosophila NMJ Morphometry

2016 ◽  
Vol 12 (3) ◽  
pp. e1004823 ◽  
Author(s):  
Bonnie Nijhof ◽  
Anna Castells-Nobau ◽  
Louis Wolf ◽  
Jolanda M. Scheffer-de Gooyert ◽  
Ignacio Monedero ◽  
...  
Keyword(s):  
Synaptic Parameters

A Multichip Neuromorphic System for Spike-Based Visual Information Processing

2007 ◽  
Vol 19 (9) ◽  
pp. 2281-2300 ◽  
Author(s):  
R. Jacob Vogelstein ◽  
Udayan Mallik ◽  
Eugenio Culurciello ◽  
Gert Cauwenberghs ◽  
Ralph Etienne-Cummings
Keyword(s):  
Visual Information ◽  
Reversal Potential ◽  
Multiple Components ◽  
Vision Processing ◽  
Look Up Table ◽  
Neural Spike ◽  
Vlsi System ◽  
Silicon Neurons ◽  
Neuromorphic System ◽  
Synaptic Parameters

We present a multichip, mixed-signal VLSI system for spike-based vision processing. The system consists of an 80 × 60 pixel neuromorphic retina and a 4800 neuron silicon cortex with 4,194,304 synapses. Its functionality is illustrated with experimental data on multiple components of an attention-based hierarchical model of cortical object recognition, including feature coding, salience detection, and foveation. This model exploits arbitrary and reconfigurable connectivity between cells in the multichip architecture, achieved by asynchronously routing neural spike events within and between chips according to a memory-based look-up table. Synaptic parameters, including conductance and reversal potential, are also stored in memory and are used to dynamically configure synapse circuits within the silicon neurons.

scholarly journals Determining synaptic parameters using high-frequency activation

2016 ◽  
Vol 264 ◽  
pp. 136-152 ◽  
Author(s):  
Monica S. Thanawala ◽  
Wade G. Regehr
Keyword(s):  
High Frequency ◽  
Synaptic Parameters

scholarly journals Heterogeneity of Functional Synaptic Parameters among Single Release Sites

Neuron ◽  
1997 ◽  
Vol 19 (1) ◽  
pp. 139-150 ◽  
Author(s):  
Céline Auger ◽  
Alain Marty
Keyword(s):  
Synaptic Parameters

What Determines the Frequency of Fast Network Oscillations With Irregular Neural Discharges? I. Synaptic Dynamics and Excitation-Inhibition Balance

2003 ◽  
Vol 90 (1) ◽  
pp. 415-430 ◽  
Author(s):  
Nicolas Brunel ◽  
Xiao-Jing Wang
Keyword(s):  
Local Field ◽  
Oscillation Frequency ◽  
Field Potential ◽  
Spike Trains ◽  
Feedback Mechanism ◽  
Time Constants ◽  
Gamma Range ◽  
Fast Network ◽  
Synaptic Dynamics ◽  
Synaptic Parameters

When the local field potential of a cortical network displays coherent fast oscillations (∼40-Hz gamma or ∼200-Hz sharp-wave ripples), the spike trains of constituent neurons are typically irregular and sparse. The dichotomy between rhythmic local field and stochastic spike trains presents a challenge to the theory of brain rhythms in the framework of coupled oscillators. Previous studies have shown that when noise is large and recurrent inhibition is strong, a coherent network rhythm can be generated while single neurons fire intermittently at low rates compared to the frequency of the oscillation. However, these studies used too simplified synaptic kinetics to allow quantitative predictions of the population rhythmic frequency. Here we show how to derive quantitatively the coherent oscillation frequency for a randomly connected network of leaky integrate-and-fire neurons with realistic synaptic parameters. In a noise-dominated interneuronal network, the oscillation frequency depends much more on the shortest synaptic time constants (delay and rise time) than on the longer synaptic decay time, and ∼200-Hz frequency can be realized with synaptic time constants taken from slice data. In a network composed of both interneurons and excitatory cells, the rhythmogenesis is a compromise between two scenarios: the fast purely interneuronal mechanism, and the slower feedback mechanism (relying on the excitatory-inhibitory loop). The properties of the rhythm are determined essentially by the ratio of time scales of excitatory and inhibitory currents and by the balance between the mean recurrent excitation and inhibition. Faster excitation than inhibition, or a higher excitation/inhibition ratio, favors the feedback loop and a much slower oscillation (typically in the gamma range).

Using Heterogeneity to Predict Inhibitory Network Model Characteristics

2005 ◽  
Vol 93 (4) ◽  
pp. 1898-1907 ◽  
Author(s):  
F. K. Skinner ◽  
J.Y.J. Chung ◽  
I. Ncube ◽  
P. A. Murray ◽  
S. A. Campbell
Keyword(s):  
Population Dynamics ◽  
Numerical Simulations ◽  
Experimental Studies ◽  
Cellular Model ◽  
The Face ◽  
Behavioral States ◽  
Synaptic Parameters ◽  
Optimal Set ◽  
Cell Networks ◽  
Inhibitory Networks

From modeling studies it has been known for >10 years that purely inhibitory networks can produce synchronous output given appropriate balances of intrinsic and synaptic parameters. Several experimental studies indicate that synchronous activity produced by inhibitory networks is critical to the production of population rhythms associated with various behavioral states. Heterogeneity of inputs to inhibitory networks strongly affect their ability to synchronize. In this paper, we explore how the amount of input heterogeneity to two-cell inhibitory networks affects their dynamics. Using numerical simulations and bifurcation analyses, we find that the ability of inhibitory networks to synchronize in the face of heterogeneity depends nonmonotonically on each of the synaptic time constant, synaptic conductance and external drive parameters. Because of this, an optimal set of parameters for a given cellular model with various biophysical characteristics can be determined. We suggest that this could be a helpful approach to use in determining the importance of different, underlying biophysical details. We further find that two-cell coherence properties are maintained in larger 10-cell networks. As such, we think that a strategy of “embedding” small network dynamics in larger networks is a useful way to understand the contribution of biophysically derived parameters to population dynamics in large networks.

Coding of Temporal Information by Activity-Dependent Synapses

2002 ◽  
Vol 87 (1) ◽  
pp. 140-148 ◽  
Author(s):  
Galit Fuhrmann ◽  
Idan Segev ◽  
Henry Markram ◽  
Misha Tsodyks
Keyword(s):  
Cortical Neurons ◽  
Temporal Structure ◽  
Temporal Information ◽  
Optimal Frequency ◽  
Significant Information ◽  
Dynamic Synapse ◽  
History Of ◽  
Presynaptic Spike ◽  
Synaptic Parameters ◽  
Activity Dependent

Synaptic transmission in the neocortex is dynamic, such that the magnitude of the postsynaptic response changes with the history of the presynaptic activity. Therefore each response carries information about the temporal structure of the preceding presynaptic input spike train. We quantitatively analyze the information about previous interspike intervals, contained in single responses of dynamic synapses, using methods from information theory applied to experimentally based deterministic and probabilistic phenomenological models of depressing and facilitating synapses. We show that for any given dynamic synapse, there exists an optimal frequency of presynaptic spike firing for which the information content is maximal; simple relations between this optimal frequency and the synaptic parameters are derived. Depressing neocortical synapses are optimized for coding temporal information at low firing rates of 0.5–5 Hz, typical to the spontaneous activity of cortical neurons, and carry significant information about the timing of up to four preceding presynaptic spikes. Facilitating synapses, however, are optimized to code information at higher presynaptic rates of 9–70 Hz and can represent the timing of over eight presynaptic spikes.

scholarly journals DESIGN OF DETERMINISTIC MODEL OF SIGNAL TRANSDUCTION BETWEEN NEURONAL CELLS

2015 ◽  
Vol 20 (1) ◽  
pp. 76-93 ◽  
Author(s):  
Maryna A. Hliatsevich ◽  
Pavel M. Bulai ◽  
Taras N. Pitlik ◽  
Andrey A. Denisov ◽  
Sergey N. Cherenkevich
Keyword(s):  
Experimental Data ◽  
Signal Transduction ◽  
Deterministic Model ◽  
Experimental Measurements ◽  
Nonlinear Ordinary Differential Equations ◽  
System Parameters ◽  
The Cauchy Problem ◽  
Synaptic Parameters ◽  
The Given ◽  
Fitting Model

Mathematical model describing signal transduction between neurons has been presented using the system of nonlinear ordinary differential equations. The Cauchy problem for the given system has been solved numerically and system parameters were adjusted to match the results of experimental measurements of extracellular postsynaptic potentials in rat hippocampus slices. While fitting model to the experimental data some values of synaptic parameters have been determined.

Computing the Optimally Fitted Spike Train for a Synapse

2001 ◽  
Vol 13 (11) ◽  
pp. 2477-2494 ◽  
Author(s):  
Thomas Natschläger ◽  
Wolfgang Maass
Keyword(s):  
Experimental Data ◽  
Spike Train ◽  
Computational Methods ◽  
Temporal Pattern ◽  
Neural Circuits ◽  
Spike Trains ◽  
Functional Explanation ◽  
Synaptic Dynamics ◽  
Synaptic Parameters

Experimental data have shown that synapses are heterogeneous: different synapses respond with different sequences of amplitudes of postsynaptic responses to the same spike train. Neither the role of synaptic dynamics itself nor the role of the heterogeneity of synaptic dynamics for computations in neural circuits is well understood. We present in this article two computational methods that make it feasible to compute for a given synapse with known synaptic parameters the spike train that is optimally fitted to the synapse in a certain sense. With the help of these methods, one can compute, for example, the temporal pattern of a spike train (with a given number of spikes) that produces the largest sum of postsynaptic responses for a specific synapse. Several other applications are also discussed. To our surprise, we find that most of these optimally fitted spike trains match common firing patterns of specific types of neurons that are discussed in the literature. Hence, our analysis provides a possible functional explanation for the experimentally observed regularity in the combination of specific types of synapses with specific types of neurons in neural circuits.

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