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

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 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 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 Long vs short-term synaptic learning rules after optogenetic spike-timing-dependent plasticity

2019 ◽  
Author(s):  
Margarita Anisimova ◽  
Bas van Bommel ◽  
Marina Mikhaylova ◽  
J. Simon Wiegert ◽  
Thomas G. Oertner ◽  
...  
Keyword(s):  
Spike Timing ◽  
Information Storage ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Neuronal Populations ◽  
Learning Rules ◽  
Blocking Activity ◽  
Synaptic Learning ◽  
The Brain

AbstractSpike-timing-dependent plasticity (STDP) is a candidate mechanism for information storage in the brain. However, it has been practically impossible to assess the long-term consequences of STDP because recordings from postsynaptic neurons last at most one hour. Here we introduce an optogenetic method to, with millisecond precision, independently control action potentials in two neuronal populations with light. We apply this method to study spike-timing-dependent plasticity (oSTDP) in the hippocampus and reproduce previous findings that depression or potentiation depend on the sequence of pre- and postsynaptic spiking. However, 3 days after induction, oSTDP results in potentiation regardless of the exact temporal sequence, frequency or number of pairings. Blocking activity between induction and readout prevented the synaptic potentiation, indicating that strengthened synapses have to be used to get strong. Our findings indicate that STDP potentiates synapses and that the change in synaptic strength persist to behaviorally relevant timescales.

Stable Competitive Dynamics Emerge from Multispike Interactions in a Stochastic Model of Spike-Timing-Dependent Plasticity

2006 ◽  
Vol 18 (10) ◽  
pp. 2414-2464 ◽  
Author(s):  
Peter A. Appleby ◽  
Terry Elliott
Keyword(s):  
Synaptic Plasticity ◽  
Stochastic Model ◽  
Higher Order ◽  
Spike Timing ◽  
Competitive Dynamics ◽  
Spike Timing Dependent Plasticity ◽  
Interaction Function ◽  
Dependent Plasticity ◽  
Order Interaction ◽  
Synaptic Dynamics

In earlier work we presented a stochastic model of spike-timing-dependent plasticity (STDP) in which STDP emerges only at the level of temporal or spatial synaptic ensembles. We derived the two-spike interaction function from this model and showed that it exhibits an STDP-like form. Here, we extend this work by examining the general n-spike interaction functions that may be derived from the model. A comparison between the two-spike interaction function and the higher-order interaction functions reveals profound differences. In particular, we show that the two-spike interaction function cannot support stable, competitive synaptic plasticity, such as that seen during neuronal development, without including modifications designed specifically to stabilize its behavior. In contrast, we show that all the higher-order interaction functions exhibit a fixed-point structure consistent with the presence of competitive synaptic dynamics. This difference originates in the unification of our proposed “switch” mechanism for synaptic plasticity, coupling synaptic depression and synaptic potentiation processes together. While three or more spikes are required to probe this coupling, two spikes can never do so. We conclude that this coupling is critical to the presence of competitive dynamics and that multispike interactions are therefore vital to understanding synaptic competition.

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