scholarly journals Spike-Timing-Dependent Plasticity and Relevant Mutual Information Maximization

2003 ◽  
Vol 15 (7) ◽  
pp. 1481-1510 ◽  
Author(s):  
Gal Chechik
Keyword(s):  
Single Neuron ◽  
Current Model ◽  
Learning Rule ◽  
Optimal Level ◽  
Spike Timing ◽  
Relative Timing ◽  
Depression Curve ◽  
Information Maximization ◽  
Order Of Magnitude ◽  
Dependent Learning

Synaptic plasticity was recently shown to depend on the relative timing of the pre- and postsynaptic spikes. This article analytically derives a spike-dependent learning rule based on the principle of information maximization for a single neuron with spiking inputs. This rule is then transformed into a biologically feasible rule, which is compared to the experimentally observed plasticity. This comparison reveals that the biological rule increases information to a near-optimal level and provides insights into the structure of biological plasticity. It shows that the time dependency of synaptic potentiation should be determined by the synaptic transfer function and membrane leak. Potentiation consists of weight-dependent and weight-independent components whose weights are of the same order of magnitude. It further suggests that synaptic depression should be triggered by rare and relevant inputs but at the same time serves to unlearn the baseline statistics of the network's inputs. The optimal depression curve is uniformly extended in time, but biological constraints that cause the cell to forget past events may lead to a different shape, which is not specified by our current model. The structure of the optimal rule thus suggests a computational account for several temporal characteristics of the biological spike-timing-dependent rules.

scholarly journals Memory Retention and Spike-Timing-Dependent Plasticity

2009 ◽  
Vol 101 (6) ◽  
pp. 2775-2788 ◽  
Author(s):  
Guy Billings ◽  
Mark C. W. van Rossum
Keyword(s):  
Retention Time ◽  
Single Neuron ◽  
Receptive Fields ◽  
Learning Rule ◽  
Spike Timing ◽  
Memory Retention ◽  
Neuron Models ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Dependent Learning

Memory systems should be plastic to allow for learning; however, they should also retain earlier memories. Here we explore how synaptic weights and memories are retained in models of single neurons and networks equipped with spike-timing-dependent plasticity. We show that for single neuron models, the precise learning rule has a strong effect on the memory retention time. In particular, a soft-bound, weight-dependent learning rule has a very short retention time as compared with a learning rule that is independent of the synaptic weights. Next, we explore how the retention time is reflected in receptive field stability in networks. As in the single neuron case, the weight-dependent learning rule yields less stable receptive fields than a weight-independent rule. However, receptive fields stabilize in the presence of sufficient lateral inhibition, demonstrating that plasticity in networks can be regulated by inhibition and suggesting a novel role for inhibition in neural circuits.

scholarly journals Temporal Event Association and Output-Dependent Learning: A Proposed Scheme of Neural Molecular Connections

1999 ◽  
Vol 3 (4) ◽  
pp. 234-244 ◽  
Author(s):  
Yukifumi Shigematsu ◽  
◽  
Hiroshi Okamoto ◽  
Kazuhisa Ichikawa ◽  
Gen Matsumoto ◽  
...  
Keyword(s):  
Action Potentials ◽  
Single Neuron ◽  
Calcium Concentration ◽  
Molecular Mechanisms ◽  
Learning Algorithm ◽  
Learning Rule ◽  
Voltage Dependent ◽  
Temporal Events ◽  
Dependent Learning ◽  
The Brain

We introduce a model of temporal-event-associated and output-dependent learning rule, genetically acquired and expressed in a single neuron. This is essentially indispensable for the brain to acquire algorithms, how to process its self-selected information, by itself. This proposed learning rule is revised-Hebbian with a synaptic history trace to correlate one temporal event to others. Temporal events are memorized to be expressed at the synaptic site of inputs and in the form of the asymmetric neural strength corrections associated with temporal events. This learning algorithm has an advantage to associate one temporal event with others, resulting in the neuron with predictability but also makes recalling flexible. Re ’ calling is, according to this learning, independent of timing, supposed to be crucial in learning. Underlying molecular mechanisms for our proposed learning rule are discussed and we identify three important factors: 1) the back-propagating action potentials experimentally observed a single neuron play a crucial role for outputdependent learning, 2) temporally associated, nonlinear couplings are modeled at molecular levels with glutamate receptors, voltage-dependent channels, intracellular calcium concentration, protein kinases and phosphatase, and 3) intracellular concentration of inositol-tri-phosphate [IP3] is the memory substrate of synaptic history.

scholarly journals Effects of memristive synapse radiation interactions on learning in spiking neural networks

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
Sumedha Gandharava Dahl ◽  
Robert C. Ivans ◽  
Kurtis D. Cantley
Keyword(s):  
Action Potentials ◽  
Radiation Effects ◽  
Synaptic Weight ◽  
Learning Rule ◽  
Spike Timing ◽  
Spiking Neural Network ◽  
Synaptic Action ◽  
Drift Model ◽  
Spatio Temporal ◽  
Post Synaptic Neuron

AbstractThis study uses advanced modeling and simulation to explore the effects of external events such as radiation interactions on the synaptic devices in an electronic spiking neural network. Specifically, the networks are trained using the spike-timing-dependent plasticity (STDP) learning rule to recognize spatio-temporal patterns (STPs) representing 25 and 100-pixel characters. Memristive synapses based on a TiO2 non-linear drift model designed in Verilog-A are utilized, with STDP learning behavior achieved through bi-phasic pre- and post-synaptic action potentials. The models are modified to include experimentally observed state-altering and ionizing radiation effects on the device. It is found that radiation interactions tend to make the connection between afferents stronger by increasing the conductance of synapses overall, subsequently distorting the STDP learning curve. In the absence of consistent STPs, these effects accumulate over time and make the synaptic weight evolutions unstable. With STPs at lower flux intensities, the network can recover and relearn with constant training. However, higher flux can overwhelm the leaky integrate-and-fire post-synaptic neuron circuits and reduce stability of the network.

Spike-Timing-Dependent Hebbian Plasticity as Temporal Difference Learning

2001 ◽  
Vol 13 (10) ◽  
pp. 2221-2237 ◽  
Author(s):  
Rajesh P. N. Rao ◽  
Terrence J. Sejnowski
Keyword(s):  
Cortical Neuron ◽  
Action Potentials ◽  
Learning Rule ◽  
Spike Timing ◽  
Biophysical Model ◽  
Excitatory Synapses ◽  
Temporal Difference ◽  
Temporal Difference Learning ◽  
Neocortical Neurons ◽  
Hebbian Plasticity

A spike-timing-dependent Hebbian mechanism governs the plasticity of recurrent excitatory synapses in the neocortex: synapses that are activated a few milliseconds before a postsynaptic spike are potentiated, while those that are activated a few milliseconds after are depressed. We show that such a mechanism can implement a form of temporal difference learning for prediction of input sequences. Using a biophysical model of a cortical neuron, we show that a temporal difference rule used in conjunction with dendritic backpropagating action potentials reproduces the temporally asymmetric window of Hebbian plasticity observed physiologically. Furthermore, the size and shape of the window vary with the distance of the synapse from the soma. Using a simple example, we show how a spike-timing-based temporal difference learning rule can allow a network of neocortical neurons to predict an input a few milliseconds before the input's expected arrival.

scholarly journals Sequential neuromodulation of Hebbian plasticity offers mechanism for effective reward-based navigation

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Zuzanna Brzosko ◽  
Sara Zannone ◽  
Wolfram Schultz ◽  
Claudia Clopath ◽  
Ole Paulsen
Keyword(s):  
Time Window ◽  
Hippocampal Slices ◽  
Learning Rule ◽  
Spike Timing ◽  
Synaptic Potentiation ◽  
Behavioral Learning ◽  
Hebbian Plasticity ◽  
Subsequent Application ◽  
Behavioral States ◽  
Synaptic Learning

Spike timing-dependent plasticity (STDP) is under neuromodulatory control, which is correlated with distinct behavioral states. Previously, we reported that dopamine, a reward signal, broadens the time window for synaptic potentiation and modulates the outcome of hippocampal STDP even when applied after the plasticity induction protocol (Brzosko et al., 2015). Here, we demonstrate that sequential neuromodulation of STDP by acetylcholine and dopamine offers an efficacious model of reward-based navigation. Specifically, our experimental data in mouse hippocampal slices show that acetylcholine biases STDP toward synaptic depression, whilst subsequent application of dopamine converts this depression into potentiation. Incorporating this bidirectional neuromodulation-enabled correlational synaptic learning rule into a computational model yields effective navigation toward changing reward locations, as in natural foraging behavior. Thus, temporally sequenced neuromodulation of STDP enables associations to be made between actions and outcomes and also provides a possible mechanism for aligning the time scales of cellular and behavioral learning.

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