Capacity-Enhancing Synaptic Learning Rules in a Medial Temporal Lobe Online Learning Model

Wu, Xundong; Mel, Bartlett W.

doi:10.1016/j.neuron.2009.02.021

Cited by 58 publications

(66 citation statements)

References 79 publications

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“…In conclusion, our findings lend further support to the view that dendrites are key integrative subunits in various types of neurons (8, 9, 13-15, 21, 22, 29, 37, 52-55) and may play a variety of computational roles including the representation of different receptive field subunits (20,21,(56)(57)(58)(59), the mediation of attentional and/or contextual modulation effects (60,61), the encoding of higher-order stimulus features for learning and memory (19,25,(62)(63)(64)(65)(66)(67)(68)(69), the representation of multiple place fields within the same cell (27) (although see ref. 70), and the generation of graded persistent activity in the context of working memory (71,72) or motor output (73,74).…”

Section: Discussionmentioning

confidence: 84%

Mechanisms underlying subunit independence in pyramidal neuron dendrites

Behabadi¹,

Mel

2013

Proc. Natl. Acad. Sci. U.S.A.

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Pyramidal neuron (PN) dendrites compartmentalize voltage signals and can generate local spikes, which has led to the proposal that their dendrites act as independent computational subunits within a multilayered processing scheme. However, when a PN is strongly activated, back-propagating action potentials (bAPs) sweeping outward from the soma synchronize dendritic membrane potentials many times per second. How PN dendrites maintain the independence of their voltage-dependent computations, despite these repeated voltage resets, remains unknown. Using a detailed compartmental model of a layer 5 PN, and an improved method for quantifying subunit independence that incorporates a more accurate model of dendritic integration, we first established that the output of each dendrite can be almost perfectly predicted by the intensity and spatial configuration of its own synaptic inputs, and is nearly invariant to the rate of bAP-mediated "cross-talk" from other dendrites over a 100-fold range. Then, through an analysis of conductance, voltage, and current waveforms within the model cell, we identify three biophysical mechanisms that together help make independent dendritic computation possible in a firing neuron, suggesting that a major subtype of neocortical neuron has been optimized for layered, compartmentalized processing under in-vivo-like spiking conditions.O n the path to understanding the diverse information processing functions of the brain, it is essential to develop simplified models of individual neurons. The classical view that a neuron collects excitatory and inhibitory influences from across its dendritic arbor and passively funnels them to a single spikegenerating zone near the soma has been challenged by the finding that dendrites generate local spikes (1-15) and can support a variety of compartmentalized computations (16-28). We previously showed that the input-output (i/o) behavior of a dendritic subtree, whose branches emanate from a main trunk or soma, can be described by a two-layer model (2LM), where the first layer consists of multiple independent dendritic "subunits" with stereotyped nonlinear i/o functions. In the second layer, corresponding to the soma, the dendritic outputs are summed and fed through the cell's somatic firing rate-current (f-I) curve (8,21,22,29) (Fig. 1A). The core assumption of the 2LM is that each dendrite's output depends only on its synaptic inputs, and is independent of the activity of other dendrites or the cell as a whole. The 2LM was previously tested in both experimental and modeling studies, outperforming passive dendrite (one-layer) models in predicting pyramidal neuron (PN) responses both to brief inputs leading to subthreshold somatic responses (8,29) and to high-frequency stimulation that produced output trains lasting hundreds of milliseconds (21,22). Notwithstanding the improved description of PN responses provided by two-layer models, a substantial fraction of the response variance remained unexplained by 2LM predictions in those previous studies (21, 22), leav...

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Section: Discussionmentioning

confidence: 84%

Mechanisms underlying subunit independence in pyramidal neuron dendrites

Behabadi¹,

Mel

2013

Proc. Natl. Acad. Sci. U.S.A.

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“…The resulting ''dendritic spike'' increases the contribution of these synapses to somatic firing. Early modeling studies predicted that neurons using such non-linear dendritic integration could significantly enhance their computational capacity (Poirazi and Mel, 2001;Wu and Mel, 2009). Indeed, several recent experimental studies have shown that non-linear dendritic integration facilitates sensory processing in live animals (Lavzin et al, 2012;Palmer et al, 2014;Smith et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Spontaneous Activity Drives Local Synaptic Plasticity In Vivo

Winnubst

Cheyne

Niculescu

et al. 2015

Neuron

173

206

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Spontaneous activity fine-tunes neuronal connections in the developing brain. To explore the underlying synaptic plasticity mechanisms, we monitored naturally occurring changes in spontaneous activity at individual synapses with whole-cell patch-clamp recordings and simultaneous calcium imaging in the mouse visual cortex in vivo. Analyzing activity changes across large populations of synapses revealed a simple and efficient local plasticity rule: synapses that exhibit low synchronicity with nearby neighbors (<12 μm) become depressed in their transmission frequency. Asynchronous electrical stimulation of individual synapses in hippocampal slices showed that this is due to a decrease in synaptic transmission efficiency. Accordingly, experimentally increasing local synchronicity, by stimulating synapses in response to spontaneous activity at neighboring synapses, stabilized synaptic transmission. Finally, blockade of the high-affinity proBDNF receptor p75(NTR) prevented the depression of asynchronously stimulated synapses. Thus, spontaneous activity drives local synaptic plasticity at individual synapses in an "out-of-sync, lose-your-link" fashion through proBDNF/p75(NTR) signaling to refine neuronal connectivity. VIDEO ABSTRACT.

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“…Nonlinear dendritic behavior has been proposed as a plausible basis for single-neuron computation (Häusser and Mel, 2003;Mel, 2007;Morita, 2008;Wu and Mel, 2009). However, it is not clear how such functionality could emerge in single neurons in a self-organized manner.…”

Section: Discussionmentioning

confidence: 99%

“…It has been hypothesized that such dendritic nonlinearities enhance processing capabilities at the single-neuron level (Mel, 1994;Häusser and Mel, 2003) that could be important for the binding of object features, for the participation of single neurons in several ensembles, for various memory processes (Morita, 2008;Wu and Mel, 2009), and for sequence detection (Branco et al, 2010). However, the computational advantage of nonlinear branches for the organism is unclear, because similar functional properties could be achieved by networks of neurons without dendritic nonlinearities.…”

Section: Introductionmentioning

confidence: 99%

Branch-Specific Plasticity Enables Self-Organization of Nonlinear Computation in Single Neurons

Legenstein¹,

Maass²

2011

Journal of Neuroscience

162

152

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It has been conjectured that nonlinear processing in dendritic branches endows individual neurons with the capability to perform complex computational operations that are needed to solve for example the binding problem. However, it is not clear how single neurons could acquire such functionality in a self-organized manner, because most theoretical studies of synaptic plasticity and learning concentrate on neuron models without nonlinear dendritic properties. In the meantime, a complex picture of information processing with dendritic spikes and a variety of plasticity mechanisms in single neurons has emerged from experiments. In particular, new experimental data on dendritic branch strength potentiation in rat hippocampus have not yet been incorporated into such models. In this article, we investigate how experimentally observed plasticity mechanisms, such as depolarization-dependent spike-timing-dependent plasticity and branch-strength potentiation, could be integrated to self-organize nonlinear neural computations with dendritic spikes. We provide a mathematical proof that, in a simplified setup, these plasticity mechanisms induce a competition between dendritic branches, a novel concept in the analysis of single neuron adaptivity. We show via computer simulations that such dendritic competition enables a single neuron to become member of several neuronal ensembles and to acquire nonlinear computational capabilities, such as the capability to bind multiple input features. Hence, our results suggest that nonlinear neural computation may self-organize in single neurons through the interaction of local synaptic and dendritic plasticity mechanisms.

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Capacity-Enhancing Synaptic Learning Rules in a Medial Temporal Lobe Online Learning Model

Cited by 58 publications

References 79 publications

Mechanisms underlying subunit independence in pyramidal neuron dendrites

Mechanisms underlying subunit independence in pyramidal neuron dendrites

Spontaneous Activity Drives Local Synaptic Plasticity In Vivo

Branch-Specific Plasticity Enables Self-Organization of Nonlinear Computation in Single Neurons

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