A Dynamic Spatial Gradient of Hebbian Learning in Dendrites

Bender, Vanessa A.; Feldman, Daniel E.

doi:10.1016/j.neuron.2006.07.003

Cited by 7 publications

(6 citation statements)

References 11 publications

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“…The prediction of the neural network is not just a static map of the external world, but a prediction of the dynamical interactions between the animal and its environment (including other animals). The neural network acquires knowledge of the real world by exploratory action using the Hebbian learning rule [32], which is one of the basic neural mechanisms for learning in animals [7,14,15] and in computational neural network models [16,41,[54][55][56]. Using such Hebbian associative learning mechanism, time-dependent correlation between the sensory input and motor output can be established by the correlation function coefficients embedded in the synaptic weights [78,79].…”

Section: Minimalistic Model Using Minimal Assumptionsmentioning

confidence: 99%

Computation in Emotional Processing: Quantitative Confirmation of Proportionality Hypothesis for Angry Unhappy Emotional Intensity to Perceived Loss

Tam

2011

Cogn Comput

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A computational model of emotion is derived (using minimalistic assumptions) to quantify how emotions are evolved to estimate the accuracy of an internally generated brain model that predicts the external world. In this model, emotion is an emergent property serving as a selfderived feedback that monitors the accuracy of the internal model via the discrepancy (error measure) between the (internal) subjective reality and (external) objective reality-reality-check subconsciously. Minimization of error (computed by the ''gain'' toward the desired outcome) will optimize congruency between internal and external worlds-resulting in happy emotion. Unhappy emotion is resulted from the discrepancy between internal and external worlds, which can serve as feedback for self-correction to minimize the ''loss'' (error) between desired and actual outcomes. Unhappiness provides the internal guide to selfidentify whether the cause of error is due to input (sensory perception) error, output (motor execution) error, or modeling (internal model) error. Experimental validation of the hypothesis using the ultimatum game paradigm confirmed the inverse proportional relationship of anger to perceived gain (or direct proportionality to loss) that estimates the discrepancy between what we want and what we get. It also characterizes specific emotional biases by shifting the emotional intensity curve quantitatively.

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Section: Minimalistic Model Using Minimal Assumptionsmentioning

confidence: 99%

Computation in Emotional Processing: Quantitative Confirmation of Proportionality Hypothesis for Angry Unhappy Emotional Intensity to Perceived Loss

Tam

2011

Cogn Comput

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“…However, stability is not always guaranteed by STDP because various types of plasticity exist across different neurons and even at the same neuron, depending on the location of the synapses (Frömke et al, 2005; Bender and Feldman, 2006; Sjöström and Häusser, 2006). Weight dependent STDP has been introduced to solve this issue (Van Rossum et al, 2000; Gütig et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Synaptic scaling in combination with many generic plasticity mechanisms stabilizes circuit connectivity

Tetzlaff

Kolodziejski

Timme

et al. 2011

Front. Comput. Neurosci.

161

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Synaptic scaling is a slow process that modifies synapses, keeping the firing rate of neural circuits in specific regimes. Together with other processes, such as conventional synaptic plasticity in the form of long term depression and potentiation, synaptic scaling changes the synaptic patterns in a network, ensuring diverse, functionally relevant, stable, and input-dependent connectivity. How synaptic patterns are generated and stabilized, however, is largely unknown. Here we formally describe and analyze synaptic scaling based on results from experimental studies and demonstrate that the combination of different conventional plasticity mechanisms and synaptic scaling provides a powerful general framework for regulating network connectivity. In addition, we design several simple models that reproduce experimentally observed synaptic distributions as well as the observed synaptic modifications during sustained activity changes. These models predict that the combination of plasticity with scaling generates globally stable, input-controlled synaptic patterns, also in recurrent networks. Thus, in combination with other forms of plasticity, synaptic scaling can robustly yield neuronal circuits with high synaptic diversity, which potentially enables robust dynamic storage of complex activation patterns. This mechanism is even more pronounced when considering networks with a realistic degree of inhibition. Synaptic scaling combined with plasticity could thus be the basis for learning structured behavior even in initially random networks.

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“…Note, in many cases only one (D or BP) of the two influences will be active at a synapse (Sjöström and Häusser, 2006;Letzkus et al, 2006, see also commentary by Bender and Feldman (2006)) Detailed equations for the pre-and post-synaptic signals will be given below.…”

Section: Methodsmentioning

confidence: 99%

Self-influencing synaptic plasticity: Recurrent changes of synaptic weights can lead to specific functional properties

2007

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Recent experimental results suggest that dendritic and back-propagating spikes can influence synaptic plasticity in different ways (Holthoff, 2004; Holthoff et al., 2005). In this study we investigate how these signals could interact at dendrites in space and time leading to changing plasticity properties at local synapse clusters. Similar to a previous study (Saudargiene et al., 2004) we employ a differential Hebbian learning rule to emulate spike-timing dependent plasticity and investigate how the interaction of dendritic and back-propagating spikes, as the post-synaptic signals, could influence plasticity. Specifically, we will show that local synaptic plasticity driven by spatially confined dendritic spikes can lead to the emergence of synaptic clusters with different properties. If one of these clusters can drive the neuron into spiking, plasticity may change and the now arising global influence of a back-propagating spike can lead to a further segregation of the clusters and possibly the dying-off of some of them leading to more functional specificity. These results suggest that through plasticity being a spatial and temporal local process, the computational properties of dendrites or complete neurons can be substantially augmented.

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A Dynamic Spatial Gradient of Hebbian Learning in Dendrites

Cited by 7 publications

References 11 publications

Computation in Emotional Processing: Quantitative Confirmation of Proportionality Hypothesis for Angry Unhappy Emotional Intensity to Perceived Loss

Computation in Emotional Processing: Quantitative Confirmation of Proportionality Hypothesis for Angry Unhappy Emotional Intensity to Perceived Loss

Synaptic scaling in combination with many generic plasticity mechanisms stabilizes circuit connectivity

Self-influencing synaptic plasticity: Recurrent changes of synaptic weights can lead to specific functional properties

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