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The Effect of Synchronized Inputs at the Single Neuron Level

Öjvind Bernander, Christof Koch, Marius Usher
1994 Neural Computation  
We investigate here the role of synaptic synchronization for the leaky integrateand-fire neuron as well as for a biophysically and anatomically detailed compartmental model of a cortical pyramidal cell  ...  It is commonly assumed that temporal synchronization of excitatory synaptic inputs onto a single neuron increases its firing rate.  ...  Acknowledgments We wish to thank Ernst Niebur and William Softky for helpful comments.  ... 
doi:10.1162/neco.1994.6.4.622 fatcat:ay4gybljjnhdthmodsn67erbmq

Programmable neuromorphic circuits for spike-based neural dynamics

Mostafa Rahimi Azghadi, Saber Moradi, Giacomo Indiveri
2013 2013 IEEE 11th International New Circuits and Systems Conference (NEWCAS)  
In particular, we present both CMOS and hybrid memristor/CMOS synaptic circuits that have programmable synaptic weights and exhibit biologically plausible response properties.  ...  For the CMOS circuits, we present experimental results demonstrating that they operate correctly over a wide range input frequencies; for the hybrid memristor/CMOS circuits we present circuit simulation  ...  When there are synaptic inputs with various firing rates, the neuron and synapses should be tuned to act linearly for the whole possible input firing range of frequencies.  ... 
doi:10.1109/newcas.2013.6573600 dblp:conf/newcas/AzghadiMI13 fatcat:yh6dxsid2vfufe5kii445olehq

On how correlations between excitatory and inhibitory synaptic inputs maximize the information rate of neuronal firing

Pavel A. Puzerey, Roberto F. Galán
2014 Frontiers in Computational Neuroscience  
Conversely, inhibition that is too delayed broadens neuronal integration times, thereby diminishing spike-time precision and increasing the firing frequency.  ...  We tested this hypothesis by investigating the effect of such correlations on the information rate (IR) of spike trains using the Hodgkin-Huxley model in which both synaptic and membrane conductances are  ...  (B) Surface plot of firing rates of the model neuron in response to balanced synaptic conductances with varying synaptic kinetics (τ ) and relative lags between excitation and inhibition (δ).  ... 
doi:10.3389/fncom.2014.00059 pmid:24936182 pmcid:PMC4047963 fatcat:o6j4d3wykbf2hhvllz2l6p7n2y

Tuning of synaptic responses: an organizing principle for optimization of neural circuits

Cian O'Donnell, Matthew F. Nolan
2011 Trends in Neurosciences  
(b) Figure 4 . 4 Models for the optimization of neuronal function by tuning synaptic integration. A number of cellular functions may be targets for optimization by tuning synaptic integration.  ...  Identifying or ruling out the existence of sensors for activity-dependent optimization of information processing could be critical for distinguishing optimization models from homeostatic models.  ...  Tuning of synaptic responses When synaptic responses are configured according to the functional roles of a particular neuron, for example, by setting the density of non-synaptic ion channels that influence  ... 
doi:10.1016/j.tins.2010.10.003 pmid:21067825 fatcat:4fuf4d6el5galbancnojj7qv4e

Fast inference of couplings between integrate-and-fire neurons from their spiking activity

Simona Cocco, Stanislas Leibler, Rémi Monasson
2009 BMC Neuroscience  
J ij is the strength of the connection from neuron j onto neuron i and t j, k the time at which cell j fires its k th spike; we assume that synaptic inputs are instantaneously integrated i.e. the synaptic  ...  To be more precise, let us model cells as Leaky Integrate-and-Fire (LIF) neurons (see [2] and references therein) whose membrane potentials obey the differential equation (units are chosen so that the  ...  J ij is the strength of the connection from neuron j onto neuron i and t j, k the time at which cell j fires its k th spike; we assume that synaptic inputs are instantaneously integrated i.e. the synaptic  ... 
doi:10.1186/1471-2202-10-s1-p128 fatcat:cobtklmubbbudot4s2guek3lda

Conductance-Based Integrate-and-Fire Models

Alain Destexhe
1997 Neural Computation  
Similarities and differences among PB, IAF, and HH models are illustrated for three cases: high-frequency repetitive firing, spike timing following random synaptic inputs, and network behavior in the presence  ...  Unlike the classical integrate-andfire (IAF) approach, they take into account the changes of conductances during and after the spike, which have a determinant influence in shaping neuronal responses.  ...  Acknowledgments All simulations were run on a Sun Sparc 20 workstation using the NEURON simulator (Hines, 1993) or using programs written in C.  ... 
doi:10.1162/neco.1997.9.3.503 pmid:9097470 fatcat:cdvuysbt4narbotirgqj6zno6a

Analog-digital simulations of full conductance-based networks of spiking neurons with spike timing dependent plasticity

Quan Zou, Yannick Bornat, Sylvain SaÏghi, Jean Tomas, Sylvie Renaud, Alain Destexhe
2006 Network  
We introduce and test a system for simulating networks of conductance-based neuron models using analog circuits.  ...  Synaptic interactions are mediated by conductance-based synaptic currents described by kinetic models.  ...  Acknowledgments Research supported by CNRS, HFSP, the European Community (grant IST-2001-34712 and EIR-015879) and the French Government (ACI Neurosciences Intégratives et Computationnelles).  ... 
doi:10.1080/09548980600711124 pmid:17162612 fatcat:jkjorhpywrg3hk4bbsrnqdwe3a

Effects of Synaptic Synchrony on the Neuronal Input-Output Relationship

Xiaoshen Li, Giorgio A. Ascoli
2008 Neural Computation  
The firing rate of individual neurons depends on the firing frequency of their distributed synaptic inputs, with linear and non-linear relations subserving different computational functions.  ...  A reduced integrate-and-fire model suggested a mechanism explaining these results based on spatiotemporal integration, with fundamental implications relating synchrony to memory encoding.  ...  Julia Berzhanskaya and Paul So for their insightful feedback on an earlier version of this manuscript.  ... 
doi:10.1162/neco.2008.10-06-385 pmid:18254692 pmcid:PMC4339285 fatcat:bmtdlesmtzhplaf6p5uxrwkkwy

Adaptive Exponential Integrate-and-Fire Model as an Effective Description of Neuronal Activity

Romain Brette, Wulfram Gerstner
2005 Journal of Neurophysiology  
Brette, Romain and Wulfram Gerstner. Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. .  ...  We introduce a two-dimensional integrate-and-fire model that combines an exponential spike mechanism with an adaptation equation, based on recent theoretical findings.  ...  D: error in spike prediction of the standard integrate-and-fire model in LC, MC, and HC states (threshold is the same for all inputs).  ... 
doi:10.1152/jn.00686.2005 pmid:16014787 fatcat:r73ns2scqzbjte5qpbii2lbdkm

Generation of Spike Latency Tuning by Thalamocortical Circuits in Auditory Cortex

Y. Zhou, L. Mesik, Y. J. Sun, F. Liang, Z. Xiao, H. W. Tao, L. I. Zhang
2012 Journal of Neuroscience  
Dissecting of thalamocortical circuits and neural modeling further revealed that broadly tuned intracortical inhibition prolongs the integration time for spike generation preferentially at off-optimal  ...  The modulation of integration time by thalamocortical-like circuits may represent an efficient strategy for converting information spatially coded in synaptic strength to temporal representation.  ...  The synaptic inputs were fed into the integrate-and-fire neuron model described above to derive spike latencies. Thalamocortical network model.  ... 
doi:10.1523/jneurosci.1384-12.2012 pmid:22815511 pmcid:PMC3470470 fatcat:qwjhuqmafvgxtl64idbo2aikxa

Stability of the Memory of Eye Position in a Recurrent Network of Conductance-Based Model Neurons

H.Sebastian Seung, Daniel D. Lee, Ben Y. Reis, David W. Tank
2000 Neuron  
Figure 2 . 2 Repetitive Firing of the Conductance-Based Model Neuron (A) Dynamics of membrane voltage V and synaptic activation s during repetitive firing.  ...  The agreement between (Bottom) The instantaneous firing rate of the neuron increases after the conductance-based model and the reduced model is very good.  ...  (A/cm 2 ), and capacitance (F/cm 2 ). References Current Balance Equation Abeles, M. (1991). Corticonics: Neural Circuits of the Cerebral Cortex  ... 
doi:10.1016/s0896-6273(00)81155-1 pmid:10798409 fatcat:ie6mt35fubgtfgf6tlgm5zxbxq

Modeling Synaptic Effects of Anesthesia and its Cortical Cholinergic Reversal [article]

Bolaji P Enivaye, Victoria Booth, Anthony G Hudetz, Michal Zochowski
2021 bioRxiv   pre-print
Thus, our model results predict a possible neuronal mechanism for the induced reversal of the effects of anesthesia on post synaptic potentials, consistent with experimental behavioral observations.  ...  Here we apply a computational model of a network composed of excitatory and inhibitory neurons to simulate the network effects of changes in synaptic inhibition and excitation due to anesthesia and its  ...  of Biomedical Imaging and Bioengineering R01EB018297 (MZ, VB), by the National Science Foundation BCS-1749430 (VB, MZ), and by the Rackham Merit Fellowship (BPE).  ... 
doi:10.1101/2021.12.13.472343 fatcat:ejcq3ufd2fc2pigvunjywwauna

Efficient low-dimensional approximation of continuous attractor networks [article]

Alexander Seeholzer, Moritz Deger, Wulfram Gerstner
2017 arXiv   pre-print
Continuous "bump" attractors are an established model of cortical working memory for continuous variables and can be implemented using various neuron and network models.  ...  Here, we develop a generalizable approach for the approximation of bump states of continuous attractor networks implemented in networks of both rate-based and spiking neurons.  ...  For completeness we restate the definition of the model here. Neuron model Neurons are modeled by leaky integrate-and-fire dynamics with conductance based synaptic transmission [13, 27] .  ... 
arXiv:1711.08032v1 fatcat:jypi6z4op5dhpldtspwhrt5sxu

FPGA Simulation Engine for Customized Construction of Neural Microcircuit

Jason Cong, Hugh T. Blair, Di Wu
2013 2013 IEEE 21st Annual International Symposium on Field-Programmable Custom Computing Machines  
In this paper we describe an FPGA-based platform for high-performance and low-power simulation of neural microcircuits composed from integrate-and-fire (IAF) neurons.  ...  This approach bypasses high design complexity and enables easy optimization and design space exploration.  ...  Acknowledgments This work is supported by the Center for Domain-Specific Computing (CDSC) funded by NSF's "Expeditions in Computing" award 0926127, and in part by C-FAR, one of the six centers of STARnet  ... 
doi:10.1109/fccm.2013.22 pmid:25584120 pmcid:PMC4288851 dblp:conf/fccm/CongBW13 fatcat:whsmbyvzuvh6dbxappck5fpmdu

FPGA simulation engine for customized construction of neural microcircuits

Hugh T. Blair, Jason Cong, Di Wu
2013 2013 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)  
In this paper we describe an FPGA-based platform for high-performance and low-power simulation of neural microcircuits composed from integrate-and-fire (IAF) neurons.  ...  This approach bypasses high design complexity and enables easy optimization and design space exploration.  ...  ACKNOWLEDGEMENTS This work is supported by the Center for Domain-Specific Computing (CDSC) funded by NSF's "Expeditions in Computing" award 0926127, and in part by C-FAR, one of the six centers of STARnet  ... 
doi:10.1109/iccad.2013.6691179 dblp:conf/iccad/BlairCW13 fatcat:ngfqtq3spfffxilj66wesmwtka
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