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

Abstract

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...

Temporal Map Formation in the Barn Owl’s Brain

“…The spatial eigenvalue with largest real part l 2p͞T p (the minimal best frequency of laminar neurons is about 1 kHz, which yields v 6 kHz), we find the maximal real part for m 61 and, therefore, in the D mn -m plane of Fig. 3, the prominent eigenvector is f mn exp͑2piD mn ͞T p ͒, which is consistent with numerical simulations [5] and explains experimental findings [2].…”

“…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.…”

“…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).…”

“…Axonal selection is implemented by means of a learning rule depending on spike timing initiated by (1) but such [5] that a synapse-specific weight change ͑ d dt J mn ͒ local , due to pre-and postsynaptic spikes at the very same synapse, also has a small effect on all other (m 0 fi m) synaptic weights J m 0 n connected to the same axon n. The total change dJ mn ͞dt is then given by…”

Spike-Timing–Dependent Synaptic Plasticity and Synaptic Democracy in Dendrites

“…These experimental studies were followed by theoretical efforts to understand the biophysical foundation of STDP (Shouval et al 2002), as well as its possible functional consequences (Gütig et al 2003;Kempter et al 2001;Kistler and van Hemmen 2000;Roberts 2000;Roberts and Bell 2000;Rubin 2001;Rubin et al 2001;Song and Abbott 2001;Song et al 2000;van Rossum et al 2000;Williams et al 2003 and see a whole issue of Biological Cybernetics in December 2002 dedicated to STDP). Among the possible functional roles attributed to STDP are the emergence of functional maps during development (Song and Abbott 2001), enhancement of correlated inputs (Gütig et al 2003;Meffin et al 2006;van Rossum et al 2000), enhancement of input temporal precision (Kistler and van Hemmen 2000), and the generation of a negative sensory image (Roberts and Bell 2000).…”