scholarly journals Distributed Phase Oscillatory Excitation Efficiently Produces Attractors Using Spike-Timing-Dependent Plasticity

2021 ◽  
pp. 1-22
Author(s):  
Eric C. Wong
Keyword(s):  
Oscillation Frequency ◽  
Firing Pattern ◽  
Spike Timing ◽  
Spiking Neurons ◽  
Driving Frequency ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Periodic Stimulation ◽  
The Brain ◽  
Stimulation Of

The brain is thought to represent information in the form of activity in distributed groups of neurons known as attractors. We show here that in a randomly connected network of simulated spiking neurons, periodic stimulation of neurons with distributed phase offsets, along with standard spike-timing-dependent plasticity (STDP), efficiently creates distributed attractors. These attractors may have a consistent ordered firing pattern or become irregular, depending on the conditions. We also show that when two such attractors are stimulated in sequence, the same STDP mechanism can create a directed association between them, forming the basis of an associative network. We find that for an STDP time constant of 20 ms, the dependence of the efficiency of attractor creation on the driving frequency has a broad peak centered around 8 Hz. Upon restimulation, the attractors self-oscillate, but with an oscillation frequency that is higher than the driving frequency, ranging from 10 to 100 Hz.

scholarly journals Distributed Phase Oscillatory Excitation Efficiently Produces Attractors Using Spike Timing Dependent Plasticity

2020 ◽  
Author(s):  
Eric C. Wong
Keyword(s):  
Oscillation Frequency ◽  
Firing Pattern ◽  
Spike Timing ◽  
Spiking Neurons ◽  
Driving Frequency ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Periodic Stimulation ◽  
The Brain ◽  
Stimulation Of

ABSTRACTThe brain is thought to represent information in the form of activity in distributed groups of neurons known as attractors, but it is not clear how attractors are formed or used in processing. We show here that in a randomly connected network of simulated spiking neurons, periodic stimulation of neurons with distributed phase offsets, along with standard spike timing dependent plasticity (STDP), efficiently creates distributed attractors. These attractors may have a consistent ordered firing pattern, or become disordered, depending on the conditions. We also show that when two such attractors are stimulated in sequence, the same STDP mechanism can create a directed association between them, forming the basis of an associative network. We find that for an STDP time constant of 20ms, the dependence of the efficiency of attractor creation on the driving frequency has a broad peak centered around 8Hz. Upon restimulation, the attractors selfoscillate, but with an oscillation frequency that is higher than the driving frequency, ranging from 10-100Hz.

scholarly journals Spike timing-dependent plasticity induces non-trivial topology in the brain

2017 ◽  
Vol 88 ◽  
pp. 58-64 ◽  
Author(s):  
R.R. Borges ◽  
F.S. Borges ◽  
E.L. Lameu ◽  
A.M. Batista ◽  
K.C. Iarosz ◽  
...  
Keyword(s):  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
The Brain

Inhibition of the slow afterhyperpolarization restores the classical spike timing-dependent plasticity rule obeyed in layer 2/3 pyramidal cells of the prefrontal cortex

2012 ◽  
Vol 107 (1) ◽  
pp. 205-215 ◽  
Author(s):  
Aleksey V. Zaitsev ◽  
Roger Anwyl
Keyword(s):  
Prefrontal Cortex ◽  
Pyramidal Cells ◽  
Long Term Potentiation ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Layer 2 ◽  
Stdp Rule ◽  
Stimulation Of

The induction of long-term potentiation (LTP) and long-term depression (LTD) of excitatory postsynaptic currents was investigated in proximal synapses of layer 2/3 pyramidal cells of the rat medial prefrontal cortex. The spike timing-dependent plasticity (STDP) induction protocol of negative timing, with postsynaptic leading presynaptic stimulation of action potentials (APs), induced LTD as expected from the classical STDP rule. However, the positive STDP protocol of presynaptic leading postsynaptic stimulation of APs predominantly induced a presynaptically expressed LTD rather than the expected postsynaptically expressed LTP. Thus the induction of plasticity in layer 2/3 pyramidal cells does not obey the classical STDP rule for positive timing. This unusual STDP switched to a classical timing rule if the slow Ca2+-dependent, K+-mediated afterhyperpolarization (sAHP) was inhibited by the selective blocker N-trityl-3-pyridinemethanamine (UCL2077), by the β-adrenergic receptor agonist isoproterenol, or by the cholinergic agonist carbachol. Thus we demonstrate that neuromodulators can affect synaptic plasticity by inhibition of the sAHP. These findings shed light on a fundamental question in the field of memory research regarding how environmental and behavioral stimuli influence LTP, thereby contributing to the modulation of memory.

scholarly journals Delay Selection by Spike-Timing-Dependent Plasticity in Recurrent Networks of Spiking Neurons Receiving Oscillatory Inputs

2013 ◽  
Vol 9 (2) ◽  
pp. e1002897 ◽  
Author(s):  
Robert R. Kerr ◽  
Anthony N. Burkitt ◽  
Doreen A. Thomas ◽  
Matthieu Gilson ◽  
David B. Grayden
Keyword(s):  
Spike Timing ◽  
Spiking Neurons ◽  
Recurrent Networks ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity

scholarly journals Predictive visual motion extrapolation emerges spontaneously and without supervision from a layered neural network with spike-timing-dependent plasticity

2020 ◽  
Author(s):  
Anthony N. Burkitt ◽  
Hinze Hogendoorn
Keyword(s):  
Neural Network ◽  
Neural Circuits ◽  
Spike Timing ◽  
Moving Object ◽  
Present Moment ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Neural Populations ◽  
Neural Transmission ◽  
The Brain

AbstractThe fact that the transmission and processing of visual information in the brain takes time presents a problem for the accurate real-time localisation of a moving object. One way this problem might be solved is extrapolation: using an object’s past trajectory to predict its location in the present moment. Here, we investigate how a simulated in silico layered neural network might implement such extrapolation mechanisms, and how the necessary neural circuits might develop. We allowed an unsupervised hierarchical network of velocity-tuned neurons to learn its connectivity through spike-timing dependent plasticity. We show that the temporal contingencies between the different neural populations that are activated by an object as it moves causes the receptive fields of higher-level neurons to shift in the direction opposite to their preferred direction of motion. The result is that neural populations spontaneously start to represent moving objects as being further along their trajectory than where they were physically detected. Due to the inherent delays of neural transmission, this effectively compensates for (part of) those delays by bringing the represented position of a moving object closer to its instantaneous position in the world. Finally, we show that this model accurately predicts the pattern of perceptual mislocalisation that arises when human observers are required to localise a moving object relative to a flashed static object (the flash-lag effect).Significance StatementOur ability to track and respond to rapidly changing visual stimuli, such as a fast moving tennis ball, indicates that the brain is capable of extrapolating the trajectory of a moving object in order to predict its current position, despite the delays that result from neural transmission. Here we show how the neural circuits underlying this ability can be learned through spike-timing dependent synaptic plasticity, and that these circuits emerge spontaneously and without supervision. This demonstrates how the neural transmission delays can, in part, be compensated to implement the extrapolation mechanisms required to predict where a moving object is at the present moment.

scholarly journals A STDP Rule that Favours Chaotic Spiking over Regular Spiking of Neurons

2021 ◽  
Vol 12 (03) ◽  
pp. 25-33
Author(s):  
Mario Antoine Aoun
Keyword(s):  
Neural Network ◽  
Chaos Theory ◽  
Spike Timing ◽  
Spiking Neurons ◽  
Spiking Neural Network ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Stdp Rule

We compare the number of states of a Spiking Neural Network (SNN) composed from chaotic spiking neurons versus the number of states of a SNN composed from regular spiking neurons while both SNNs implementing a Spike Timing Dependent Plasticity (STDP) rule that we created. We find out that this STDP rule favors chaotic spiking since the number of states is larger in the chaotic SNN than the regular SNN. This chaotic favorability is not general; it is exclusive to this STDP rule only. This research falls under our long-term investigation of STDP and chaos theory.

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