scholarly journals Spike Timing Precision and Neural Error Correction: Local Behavior

2005 ◽  
Vol 17 (7) ◽  
pp. 1577-1601 ◽  
Author(s):  
Michael Stiber
Keyword(s):  
Error Correction ◽  
Dynamical Behavior ◽  
Error Recovery ◽  
Spike Timing ◽  
Local Behavior ◽  
Postsynaptic Spike ◽  
Timing Precision ◽  
Presynaptic Spike ◽  
Spike Timing Precision ◽  
High Degree

The effects of spike timing precision and dynamical behavior on error correction in spiking neurons were investigated. Stationary discharges—phase locked, quasiperiodic, or chaotic—were induced in a simulated neuron by presenting pacemaker presynaptic spike trains across a model of a prototypical inhibitory synapse. Reduced timing precision was modeled by jittering presynaptic spike times. Aftereffects of errors—in this communication, missed presynaptic spikes—were determined by comparing postsynaptic spike times between simulations identical except for the presence or absence of errors. Results show that the effects of an error vary greatly depending on the ongoing dynamical behavior. In the case of phase lockings, a high degree of presynaptic spike timing precision can provide significantly faster error recovery. For nonlocked behaviors, isolated missed spikes can have little or no discernible aftereffects (or even serve to paradoxically reduce uncertainty in postsynaptic spike timing), regardless of presynaptic imprecision. This suggests two possible categories of error correction: high-precision locking with rapid recovery and low-precision nonlocked with error immunity.

scholarly journals Temperature-induced changes of spike timing precision and network synchronisation

2015 ◽  
Vol 16 (S1) ◽  
Author(s):  
Jan-Hendrik Schleimer ◽  
Janina Hesse ◽  
Susanne Schreiber
Keyword(s):  
Spike Timing ◽  
Timing Precision ◽  
Spike Timing Precision ◽  
Induced Changes

scholarly journals Spike timing precision changes with spike rate adaptation in the owl's auditory space map

2015 ◽  
Vol 114 (4) ◽  
pp. 2204-2219 ◽  
Author(s):  
Clifford H. Keller ◽  
Terry T. Takahashi
Keyword(s):  
Carrier Frequency ◽  
Time Course ◽  
Rate Adaptation ◽  
Stimulus Level ◽  
Spike Timing ◽  
Spike Rate ◽  
Temporal Precision ◽  
Timing Precision ◽  
Spike Timing Precision

Spike rate adaptation (SRA) is a continuing change of responsiveness to ongoing stimuli, which is ubiquitous across species and levels of sensory systems. Under SRA, auditory responses to constant stimuli change over time, relaxing toward a long-term rate often over multiple timescales. With more variable stimuli, SRA causes the dependence of spike rate on sound pressure level to shift toward the mean level of recent stimulus history. A model based on subtractive adaptation (Benda J, Hennig RM. J Comput Neurosci 24: 113–136, 2008) shows that changes in spike rate and level dependence are mechanistically linked. Space-specific neurons in the barn owl's midbrain, when recorded under ketamine-diazepam anesthesia, showed these classical characteristics of SRA, while at the same time exhibiting changes in spike timing precision. Abrupt level increases of sinusoidally amplitude-modulated (SAM) noise initially led to spiking at higher rates with lower temporal precision. Spike rate and precision relaxed toward their long-term values with a time course similar to SRA, results that were also replicated by the subtractive model. Stimuli whose amplitude modulations (AMs) were not synchronous across carrier frequency evoked spikes in response to stimulus envelopes of a particular shape, characterized by the spectrotemporal receptive field (STRF). Again, abrupt stimulus level changes initially disrupted the temporal precision of spiking, which then relaxed along with SRA. We suggest that shifts in latency associated with stimulus level changes may differ between carrier frequency bands and underlie decreased spike precision. Thus SRA is manifest not simply as a change in spike rate but also as a change in the temporal precision of spiking.

scholarly journals Spike-Timing Precision and Neuronal Synchrony Are Enhanced by an Interaction between Synaptic Inhibition and Membrane Oscillations in the Amygdala

PLoS ONE ◽  
2012 ◽  
Vol 7 (4) ◽  
pp. e35320 ◽  
Author(s):  
Steven J. Ryan ◽  
David E. Ehrlich ◽  
Aaron M. Jasnow ◽  
Shabrine Daftary ◽  
Teresa E. Madsen ◽  
...  
Keyword(s):  
Spike Timing ◽  
Synaptic Inhibition ◽  
Neuronal Synchrony ◽  
Timing Precision ◽  
Spike Timing Precision ◽  
Membrane Oscillations

An Algorithm for Modifying Neurotransmitter Release Probability Based on Pre- and Postsynaptic Spike Timing

2001 ◽  
Vol 13 (1) ◽  
pp. 35-67 ◽  
Author(s):  
Walter Senn ◽  
Henry Markram ◽  
Misha Tsodyks
Keyword(s):  
Neurotransmitter Release ◽  
Learning Algorithm ◽  
Spike Trains ◽  
Spike Timing ◽  
Firing Rates ◽  
Release Probability ◽  
Postsynaptic Spike ◽  
Presynaptic Spike ◽  
The Mean ◽  
Synaptic Learning

The precise times of occurrence of individual pre- and postsynaptic action potentials are known to play a key role in the modification of synaptic efficacy. Based on stimulation protocols of two synaptically connected neurons, we infer an algorithm that reproduces the experimental data by modifying the probability of vesicle discharge as a function of the relative timing of spikes in the pre- and postsynaptic neurons. The primary feature of this algorithm is an asymmetry with respect to the direction of synaptic modification depending on whether the presynaptic spikes precede or follow the postsynaptic spike. Specifically, if the presynaptic spike occurs up to 50 ms before the postsynaptic spike, the probability of vesicle discharge is upregulated, while the probability of vesicle discharge is downregulated if the presynaptic spike occurs up to 50 ms after the postsynaptic spike. When neurons fire irregularly with Poisson spike trains at constant mean firing rates, the probability of vesicle discharge converges toward a characteristic value determined by the preand postsynaptic firing rates. On the other hand, if the mean rates of the Poisson spike trains slowly change with time, our algorithm predicts modifications in the probability of release that generalize Hebbian and Bienenstock-Cooper-Munro rules. We conclude that the proposed spike- based synaptic learning algorithm provides a general framework for regulating neurotransmitter release probability.

The spike timing precision of FitzHugh–Nagumo neuron network coupled by gap junctions

2006 ◽  
Vol 15 (10) ◽  
pp. 2450-2457 ◽  
Author(s):  
Zhang Su-Hua ◽  
Zhan Yong ◽  
Yu Hui ◽  
An Hai-Long ◽  
Zhao Tong-Jun
Keyword(s):  
Gap Junctions ◽  
Spike Timing ◽  
Neuron Network ◽  
Timing Precision ◽  
Spike Timing Precision
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