Storing covariance with nonlinearly interacting neurons

Sejnowski, Terrence J.

doi:10.1007/bf00275079

Cited by 488 publications

(229 citation statements)

References 25 publications

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“…Coordination between pre-and postsynaptic determinants of synaptic strength is featured in both theoretical approaches (22,45) and experimental studies of cortical networks (46,47). We have previously reported correlations between increases in presynaptic P r and postsynaptic GluA1 (7) but the mechanistic basis of such coordination is unclear.…”

Section: Role Of Rapid Ampar-dependent Retrograde Signaling To Presynmentioning

confidence: 99%

Postsynaptic GluA1 enables acute retrograde enhancement of presynaptic function to coordinate adaptation to synaptic inactivity

Lindskog

Groth

et al. 2010

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

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Prolonged blockade of AMPA-type glutamate receptors in hippocampal neuron cultures leads to homeostatic enhancements of preand postsynaptic function that appear correlated at individual synapses, suggesting some form of transsynaptic coordination. The respective modifications are important for overall synaptic strength but their interrelationship, dynamics, and molecular underpinnings are unclear. Here we demonstrate that adaptation begins postsynaptically but is ultimately communicated to presynaptic terminals and expressed as an accelerated turnover of synaptic vesicles. Critical postsynaptic modifications occur over hours, but enable retrograde communication within minutes once AMPA receptor (AMPAR) blockade is removed, causing elevation of both spontaneous and evoked vesicle fusion. The retrograde signaling does not require spiking activity and can be interrupted by NBQX, philanthotoxin, postsynaptic BAPTA, or external sequestration of BDNF, consistent with the acute release of retrograde messenger, triggered by postsynaptic Ca 2+ elevation via Ca 2+ -permeable AMPARs.homeostasis | synaptic scaling | calcium signaling | miniature excitatory postsynaptic currents P rolonged perturbations in the level of activity of neuronal circuits initiate major changes in excitatory transmission. Such retuning is generally homeostatic and is proposed to counterbalance other forms of plasticity and to help keep neuronal firing rate within an optimal range for efficient transfer of information (1-3). There is growing agreement about the functional significance of adaptation to inactivity (2, 4), but much uncertainty remains about where and how such adaptation is expressed.The increase in postsynaptic function in response to prolonged inactivity, evident as an increase in the amplitude of unitary synaptic events (miniature excitatory postsynaptic currents, mEPSCs, or minis), is by now well-established (2, 4). The enlargement of mEPSCs is mediated by an accumulation of AMPA receptors (AMPARs) at postsynaptic sites (5-8). Sometimes the increase is attributed to an increase in Ca 2+ -permeable AMPARs that lack GluA2 subunits (7, 9, 10), but in other cases comparable elevations in both GluA1 and GluA2 have been seen (6,8,11,12). Furthermore, adaptation to inactivity induced by postsynaptic blockade may also involve presynaptic changes, reflected by an elevated frequency of mEPSCs and increased vesicular turnover (7,(13)(14)(15)(16).Although evidence is mounting for both pre-and postsynaptic modifications, the fundamental nature of such alterations remains incompletely understood. On one hand, neuronal inactivity causing cellwide changes in transmitter release and receptivity (2) would fit with descriptions of synaptic homeostasis as a global phenomena. This idea is supported by evidence showing that glial cells can serve as general activity sensors and modulate all synapses in an area (17). On the other hand, findings of tight coordination between closely neighboring synapses (18) and of differential regulation of different types...

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Section: Role Of Rapid Ampar-dependent Retrograde Signaling To Presynmentioning

confidence: 99%

Postsynaptic GluA1 enables acute retrograde enhancement of presynaptic function to coordinate adaptation to synaptic inactivity

Lindskog

Groth

et al. 2010

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

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“…In the classical Hebb model [11], the curve relating the increment of synaptic weight to postsynaptic activation is a straight line without synaptic depression. In Sejnowski's covariance model [23,24], regions of potentiation and depression are separated by a LTP threshold. In [25] Abraham and Bear consider it as a homosynaptic property (i.e.…”

Section: Discussionmentioning

confidence: 99%

On the Biological Plausibility of Artificial Metaplasticity

Andina

Ropero-Peláez

2011

Lecture Notes in Computer Science

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Abstract. The training algorithm studied in this paper is inspired by the biological metaplasticity property of neurons. Tested on different multidisciplinary applications, it achieves a more efficient training and improves Artificial Neural Network Performance. The algorithm has been recently proposed for Artificial Neural Networks in general, although for the purpose of discussing its biological plausibility, a Multilayer Perceptron has been used. During the training phase, the artificial metaplasticity multilayer perceptron could be considered a new probabilistic version of the presynaptic rule, as during the training phase the algorithm assigns higher values for updating the weights in the less probable activations than in the ones with higher probability.

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“…We call this plasticity rule spike-timing dependent covariance plasticity (STCP) because it is a temporally asymmetric, event-driven, version of covariance learning [11]. In this way, the relative strength of potentiation is proportional to the statistical support for the causal chain of activity from pre-synaptic to post-synaptic neuron.…”

Section: Synaptic State Matchingmentioning

confidence: 99%

Synaptic state matching: a dynamical architecture for predictive internal representation and feature perception

Tavazoie¹

2012

Nature Precedings

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Here we consider the possibility that a fundamental function of sensory cortex is the generation of an internal simulation of sensory environment in real-time. A logical elaboration of this idea leads to a dynamical neural architecture that oscillates between two fundamental network states, one driven by external input, and the other by recurrent synaptic drive in the absence of sensory input. Synaptic strength is modified by a proposed synaptic state matching (SSM) process that ensures equivalence of spike statistics between the two network states. Remarkably, SSM, operating locally at individual synapses, generates accurate and stable network-level predictive internal representations, enabling pattern completion and unsupervised feature detection from noisy sensory input. SSM is a biologically plausible substrate for learning and memory because it brings together sequence learning, feature detection, synaptic homeostasis, and network oscillations under a single parsimonious computational framework.

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Storing covariance with nonlinearly interacting neurons

Cited by 488 publications

References 25 publications

Postsynaptic GluA1 enables acute retrograde enhancement of presynaptic function to coordinate adaptation to synaptic inactivity

Postsynaptic GluA1 enables acute retrograde enhancement of presynaptic function to coordinate adaptation to synaptic inactivity

On the Biological Plausibility of Artificial Metaplasticity

Synaptic state matching: a dynamical architecture for predictive internal representation and feature perception

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