Removing Time Variation with the Anti-Hebbian Differential Synapse

1991 ◽  
Vol 3 (3) ◽  
pp. 312-320 ◽  
Author(s):  
Graeme Mitchison
Keyword(s):  
Temporal Variation ◽  
Time Variation ◽  
Synaptic Weight ◽  
Receptive Fields ◽  
Learning Rule ◽  
Temporal Changes ◽  
Short Term ◽  
Conservation Condition ◽  
Rule Changes ◽  
Synaptic Learning

I describe a local synaptic learning rule that can be used to remove the effects of certain types of systematic temporal variation in the inputs to a unit. According to this rule, changes in synaptic weight result from a conjunction of short-term temporal changes in the inputs and the output. Formally, This is like the differential rule proposed by Klopf (1986) and Kosko (1986), except for a change of sign, which gives it an anti-Hebbian character. By itself this rule is insufficient. A weight conservation condition is needed to prevent the weights from collapsing to zero, and some further constraint—implemented here by a biasing term—to select particular sets of weights from the subspace of those which give minimal variation. As an example, I show that this rule will generate center-surround receptive fields that remove temporally varying linear gradients from the inputs.

EXCITEMENT AND SYNCHRONIZATION OF SMALL-WORLD NEURONAL NETWORKS WITH SHORT-TERM SYNAPTIC PLASTICITY

2011 ◽  
Vol 21 (05) ◽  
pp. 415-425 ◽  
Author(s):  
FANG HAN ◽  
MARIAN WIERCIGROCH ◽  
JIAN-AN FANG ◽  
ZHIJIE WANG
Keyword(s):  
Synaptic Plasticity ◽  
Neuronal Network ◽  
Coupling Strength ◽  
Neuronal Networks ◽  
Learning Rule ◽  
Small World ◽  
Learning Rate ◽  
Short Term ◽  
Synaptic Learning

Excitement and synchronization of electrically and chemically coupled Newman-Watts (NW) small-world neuronal networks with a short-term synaptic plasticity described by a modified Oja learning rule are investigated. For each type of neuronal network, the variation properties of synaptic weights are examined first. Then the effects of the learning rate, the coupling strength and the shortcut-adding probability on excitement and synchronization of the neuronal network are studied. It is shown that the synaptic learning suppresses the over-excitement, helps synchronization for the electrically coupled network but impairs synchronization for the chemically coupled one. Both the introduction of shortcuts and the increase of the coupling strength improve synchronization and they are helpful in increasing the excitement for the chemically coupled network, but have little effect on the excitement of the electrically coupled one.

Temporal Changes in Pollen Grain Concentration Predict Short-Term Clinical Outcomes in Acute Coronary Syndromes

2021 ◽  
Vol 30 ◽  
pp. S112
Author(s):  
O. Al-mukhtar ◽  
S. Vogrin ◽  
S. Noaman ◽  
E. Lampugnani ◽  
D. Dinh ◽  
...  
Keyword(s):  
Clinical Outcomes ◽  
Acute Coronary Syndromes ◽  
Temporal Changes ◽  
Pollen Grain ◽  
Short Term ◽  
Coronary Syndromes

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.

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.

scholarly journals Symbol Grounding Association in Multimodal Sequences with Missing Elements

2018 ◽  
Vol 61 ◽  
pp. 787-806
Author(s):  
Federico Raue ◽  
Andreas Dengel ◽  
Thomas M. Breuel ◽  
Marcus Liwicki
Keyword(s):  
Dynamic Time Warping ◽  
Learning Rule ◽  
Abstract Concepts ◽  
Time Warping ◽  
Short Term ◽  
Recent Approach ◽  
Extra Step ◽  
The Common ◽  
Dynamic Time ◽  
General Scope

In this paper, we extend a symbolic association framework for being able to handle missing elements in multimodal sequences. The general scope of the work is the symbolic associations of object-word mappings as it happens in language development in infants. In other words, two different representations of the same abstract concepts can associate in both directions. This scenario has been long interested in Artificial Intelligence, Psychology, and Neuroscience. In this work, we extend a recent approach for multimodal sequences (visual and audio) to also cope with missing elements in one or both modalities. Our method uses two parallel Long Short-Term Memories (LSTMs) with a learning rule based on EM-algorithm. It aligns both LSTM outputs via Dynamic Time Warping (DTW). We propose to include an extra step for the combination with the max operation for exploiting the common elements between both sequences. The motivation behind is that the combination acts as a condition selector for choosing the best representation from both LSTMs. We evaluated the proposed extension in the following scenarios: missing elements in one modality (visual or audio) and missing elements in both modalities (visual and sound). The performance of our extension reaches better results than the original model and similar results to individual LSTM trained in each modality.

Developmental Changes in the Fidelity and Short-Term Plasticity of GABAergic Synapses in the Neonatal Rat Dorsal Horn

2008 ◽  
Vol 99 (6) ◽  
pp. 3144-3150 ◽  
Author(s):  
Rachel A. Ingram ◽  
Maria Fitzgerald ◽  
Mark L. Baccei
Keyword(s):  
Spinal Cord ◽  
Dorsal Horn ◽  
Receptive Fields ◽  
Neuronal Firing ◽  
Neonatal Rat ◽  
Superficial Dorsal Horn ◽  
Short Term ◽  
Dorsal Horn Neurons ◽  
Gabaergic Synapses

The lower thresholds and increased excitability of dorsal horn neurons in the neonatal rat suggest that inhibitory processing is less efficient in the immature spinal cord. This is unlikely to be explained by an absence of functional GABAergic inhibition because antagonism of γ-aminobutyric acid (GABA) type A receptors augments neuronal firing in vivo from the first days of life. However, it is possible that more subtle deficits in GABAergic signaling exist in the neonate, such as decreased reliability of transmission or greater depression during repetitive stimulation, both of which could influence the relative excitability of the immature spinal cord. To address this issue we examined monosynaptic GABAergic inputs onto superficial dorsal horn neurons using whole cell patch-clamp recordings made in spinal cord slices at a range of postnatal ages (P3, P10, and P21). The amplitudes of evoked inhibitory postsynaptic currents (IPSCs) were significantly lower and showed greater variability in younger animals, suggesting a lower fidelity of GABAergic signaling at early postnatal ages. Paired-pulse ratios were similar throughout the postnatal period, whereas trains of stimuli (1, 5, 10, and 20 Hz) revealed frequency-dependent short-term depression (STD) of IPSCs at all ages. Although the magnitude of STD did not differ between ages, the recovery from depression was significantly slower at immature GABAergic synapses. These properties may affect the integration of synaptic inputs within developing superficial dorsal horn neurons and thus contribute to their larger receptive fields and enhanced afterdischarge.

Short-term growth (RNA/DNA ratio) of yellow perch (Perca flavescens) in relation to environmental influences and spatio-temporal variation in a shallow fluvial lake

2007 ◽  
Vol 64 (12) ◽  
pp. 1646-1655 ◽  
Author(s):  
Hélène Glémet ◽  
Marco A Rodríguez
Keyword(s):  
Temporal Variation ◽  
Water Level ◽  
Yellow Perch ◽  
Perca Flavescens ◽  
Negative Influence ◽  
Water Levels ◽  
Short Term ◽  
Ecological Processes ◽  
Flood Pulse Concept ◽  
Spatio Temporal

Shallow fluvial lakes are heterogeneous ecosystems in which marked spatio-temporal variation renders difficult the analysis of key ecological processes, such as growth. In this study, we used generalized additive modelling of the RNA/DNA ratio, an index of short-term growth, to investigate the influence of environmental variables and spatio-temporal variation on growth of yellow perch (Perca flavescens) in Lake St. Pierre, Quebec, Canada. Temperature and water level had seemingly stronger effects on short-term growth than seasonal change or spatial variation between and along the lakeshores. Consistent with previous studies, the maximum RNA/DNA ratio was found at 20.5 °C, suggesting that our approach provides a useful tool for estimating thermal optima for growth in the field. The RNA/DNA ratio showed a positive relationship with water level, as predicted by the flood pulse concept, a finding with implications for ecosystem productivity in fluvial lakes. The RNA/DNA ratio was more variable along the north than the south shore, possibly reflecting exposure to more differentiated water masses. The negative influence of both high temperatures and low water levels on growth points to potential impacts of climatic change on fish production in shallow fluvial lakes.

scholarly journals Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning

2020 ◽  
Vol 117 (47) ◽  
pp. 29948-29958
Author(s):  
Maxwell Gillett ◽  
Ulises Pereira ◽  
Nicolas Brunel
Keyword(s):  
Storage Capacity ◽  
Synaptic Weight ◽  
Hebbian Learning ◽  
Mean Field Theory ◽  
Mean Field ◽  
Learning Rule ◽  
Network Activity ◽  
Synaptic Connectivity ◽  
Random Input ◽  
Low Dimensional

Sequential activity has been observed in multiple neuronal circuits across species, neural structures, and behaviors. It has been hypothesized that sequences could arise from learning processes. However, it is still unclear whether biologically plausible synaptic plasticity rules can organize neuronal activity to form sequences whose statistics match experimental observations. Here, we investigate temporally asymmetric Hebbian rules in sparsely connected recurrent rate networks and develop a theory of the transient sequential activity observed after learning. These rules transform a sequence of random input patterns into synaptic weight updates. After learning, recalled sequential activity is reflected in the transient correlation of network activity with each of the stored input patterns. Using mean-field theory, we derive a low-dimensional description of the network dynamics and compute the storage capacity of these networks. Multiple temporal characteristics of the recalled sequential activity are consistent with experimental observations. We find that the degree of sparseness of the recalled sequences can be controlled by nonlinearities in the learning rule. Furthermore, sequences maintain robust decoding, but display highly labile dynamics, when synaptic connectivity is continuously modified due to noise or storage of other patterns, similar to recent observations in hippocampus and parietal cortex. Finally, we demonstrate that our results also hold in recurrent networks of spiking neurons with separate excitatory and inhibitory populations.

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