Dynamics of Self-Organized Delay Adaptation

Eurich, Christian W.; Pawelzik, Klaus; Ernst, Udo; Cowan, Jack D.; Milton, John

doi:10.1103/physrevlett.82.1594

Cited by 64 publications

(44 citation statements)

References 14 publications

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“…We have shown that the formation of a temporal-feature map with a submillisecond precision can be based on an interplay of local time-resolved Hebbian learning (20,(24)(25)(26)(27)(28)(29)(30)(31)(46)(47)(48)(49) and its propagation along a presynaptic axon (34)(35)(36)(37)(38)(39)50). Our results are in agreement with physiological and anatomical data on the barn owl's laminar nucleus, and they explain the formation of a temporalfeature map.…”

Section: Discussionsupporting

confidence: 80%

“…Besides our proposed delay-selection mechanism for map formation, an alternative explanation might be the adaptation of conduction velocity of presynaptic axons (47,54). A possible target for such a learning rule are portions of axons outside the NL, because the conduction velocities within the nucleus are almost identical (13)-which is as it should be to explain the neurophonic.…”

Section: Discussionmentioning

confidence: 99%

“…In spike-based Hebbian learning at the level of a single neuron (20,(26)(27)(28)(29)(30)(31)(32)(33)(46)(47)(48)(49), synaptic strengths are increased or decreased if presynaptic spikes arrive before or after, respectively, the postsynaptic neuron fires. The temporal constraint is implemented by means of a learning window W that is several milliseconds wide ( Fig.…”

Section: Methodsmentioning

confidence: 99%

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Formation of temporal-feature maps by axonal propagation of synaptic learning

Kempter

Leibold

Wagner

et al. 2001

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

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Computational maps are of central importance to a neuronal representation of the outside world. In a map, neighboring neurons respond to similar sensory features. A well studied example is the computational map of interaural time differences (ITDs), which is essential to sound localization in a variety of species and allows resolution of ITDs of the order of 10 s. Nevertheless, it is unclear how such an orderly representation of temporal features arises. We address this problem by modeling the ontogenetic development of an ITD map in the laminar nucleus of the barn owl. We show how the owl's ITD map can emerge from a combined action of homosynaptic spike-based Hebbian learning and its propagation along the presynaptic axon. In spike-based Hebbian learning, synaptic strengths are modified according to the timing of pre-and postsynaptic action potentials. In unspecific axonal learning, a synapse's modification gives rise to a factor that propagates along the presynaptic axon and affects the properties of synapses at neighboring neurons. Our results indicate that both Hebbian learning and its presynaptic propagation are necessary for map formation in the laminar nucleus, but the latter can be orders of magnitude weaker than the former. We argue that the algorithm is important for the formation of computational maps, when, in particular, time plays a key role.C omputational maps (1, 2) transform information about the outside world into topographic neuronal representations. They are a key concept in information processing by the nervous system. Many results point to an ontogenetic development that is critically driven by sensory experience (3). Here we focus on the development of maps of exclusively temporal features (4-10), where the time course of stimuli carries relevant information, in contrast to previous studies that have shown the emergence of orderly representations of spatial or spatiotemporal features. A prominent example is the map of interaural time differences (ITDs) that is used for sound localization (11-13).Sound localization is important to the survival of many animal species-in particular, to those that hunt in the dark. ITDs often are used as a spatial cue. Because the range of available ITDs depends on the size of the head, ITDs are normally well below 1 ms; they can be measured with a precision of up to a few microseconds. In this article, we address the question of how temporal information from both ears can be transmitted to a site of comparison where neurons are tuned to ITDs, and how those ITD-tuned neurons can be organized in a map. A first step toward a solution was made by Jeffress (1) as early as 1948. He proposed a scheme (Fig. 1A) that combines axonal delay lines from both ears and neuronal coincidence detectors to convert ITDs into a place code where a spatial pattern of neuronal activity codes the ITD. His scheme has had a profound impact on understanding sound localization. Neurons tuned to ITDs and maps resembling the circuit envisioned by Jeffress have been observed in many animals...

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Section: Discussionsupporting

confidence: 80%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Formation of temporal-feature maps by axonal propagation of synaptic learning

Kempter

Leibold

Wagner

et al. 2001

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

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“…Two models of TAH plasticity have been studied recently that differ in the way that they implement the weight dependence of the synaptic changes and the boundaries of the allowed range of synaptic efficacies. The additive model (Abbott and Blum, 1996;Gerstner et al, 1996;Eurich et al, 1999;Kempter et al, 1999Kempter et al, , 2001Roberts, 1999;Song et al, 2000;Levy et al, 2001;Câteau et al, 2002) assumes that changes in synaptic efficacies do not scale with synaptic strength, and the boundaries are imposed as hard constraints. This model retains inherently unstable dynamics while exhibiting strong competition between afferent synapses.…”

Section: Introductionmentioning

confidence: 99%

Learning Input Correlations through Nonlinear Temporally Asymmetric Hebbian Plasticity

Aharonov²,

et al. 2003

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Triggered by recent experimental results, temporally asymmetric Hebbian (TAH) plasticity is considered as a candidate model for the biological implementation of competitive synaptic learning, a key concept for the experience-based development of cortical circuitry. However, because of the well known positive feedback instability of correlation-based plasticity, the stability of the resulting learning process has remained a central problem. Plagued by either a runaway of the synaptic efficacies or a greatly reduced sensitivity to input correlations, the learning performance of current models is limited. Here we introduce a novel generalized nonlinear TAH learning rule that allows a balance between stability and sensitivity of learning. Using this rule, we study the capacity of the system to learn patterns of correlations between afferent spike trains. Specifically, we address the question of under which conditions learning induces spontaneous symmetry breaking and leads to inhomogeneous synaptic distributions that capture the structure of the input correlations. To study the efficiency of learning temporal relationships between afferent spike trains through TAH plasticity, we introduce a novel sensitivity measure that quantifies the amount of information about the correlation structure in the input, a learning rule capable of storing in the synaptic weights. We demonstrate that by adjusting the weight dependence of the synaptic changes in TAH plasticity, it is possible to enhance the synaptic representation of temporal input correlations while maintaining the system in a stable learning regime. Indeed, for a given distribution of inputs, the learning efficiency can be optimized.

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“…Second, we linearize the dynamics about the fixed point and analyze the system's temporal evolution by means of a spectral analysis of the first derivative DN [13][14][15].…”

mentioning

confidence: 99%

Temporal Map Formation in the Barn Owl’s Brain

Leibold

Kempter²,

Hemmen³

2001

Phys. Rev. Lett.

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Barn owls provide an experimentally well-specified example of a temporal map, a neuronal representation of the outside world in the brain by means of time. Their laminar nucleus exhibits a place code of interaural time differences, a cue which is used to determine the azimuthal location of a sound stimulus, e.g., prey. We analyze a model of synaptic plasticity that explains the formation of such a representation in the young bird and show how in a large parameter regime a combination of local and nonlocal synaptic plasticity yields the temporal map as found experimentally. Our analysis includes the effect of nonlinearities as well as the influence of neuronal noise. DOI: 10.1103/PhysRevLett.87.248101 PACS numbers: 87.18. -h, 43.64. +r, 87.19.La Many animals have in their brain neuronal representations of the outside world, which we call maps. These representations are due to sensory organs. Vision, for instance, provides a direct map through the lens of the eye onto the retina and, accordingly, is dominantly spatial. Audition, on the other hand, has far fewer cues but time is one of them, a very important one. Here we face the question of how a temporal map can arise and provide an answer by considering a specific example, the barn owl's sound localization.Barn owls are nocturnal predators that are able to catch mice in complete darkness. In so doing they reach an amazing azimuthal resolution of 2 ± . Interaural (inter-ear) time differences have been shown [1] to be their only cue. The spatial resolution implies a temporal precision at least as good as 40 ms [2,3].We focus on the laminar nucleus as the first station in the brain receiving input from both ears. Interaural time differences (ITDs) are represented there by means of a place code. That is to say, the azimuthal position is mapped one-to-one onto corresponding neuronal sites in the laminar nucleus, each neuron signaling its preferred azimuthal location through a maximal firing rate. As the stimulus location is varied, so is the maximal ITD response in the laminar nucleus [2]; cf. Fig. 1. Henceforth the resulting map is a neuronal representation of azimuthal stimulus locations. We can now ask two questions. How does it function, in particular, why is the precision that good (40 ms), and how does it arise? As we will see, the answers to both questions are interrelated in that the map as it evolves leads to firing precision.Here we analyze how a novel mechanism of synaptic plasticity [5] gives rise to a temporal map. As was indicated by a numerical study [5], local so-called Hebbian learning depending on spike timing of the pre-and postsynaptic neuron [6,7], which precede and follow the synapse under consideration (hence Hebbian), and small, axonally propagated, synaptic modifications [8] together induce a temporal map. One may call the underlying mechanism "axon-mediated spike-based learning" (AMSL). We now present an analytic solution to the proposed synaptic dynamics and explain the model's performance in dependence upon the learning procedure, biol...

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Dynamics of Self-Organized Delay Adaptation

Cited by 64 publications

References 14 publications

Formation of temporal-feature maps by axonal propagation of synaptic learning

Formation of temporal-feature maps by axonal propagation of synaptic learning

Learning Input Correlations through Nonlinear Temporally Asymmetric Hebbian Plasticity

Temporal Map Formation in the Barn Owl’s Brain

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