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Andrew P Davison

Publications and source records attributed to Andrew P Davison.

5 recordsLinked to original sources
PubMed2006

Learning cross-modal spatial transformations through spike timing-dependent plasticity.

A common problem in tasks involving the integration of spatial information from multiple senses, or in sensorimotor coordination, is that different modalities represent space in different frames of reference. Coordinate transformations between different reference frames are therefore required. One way to achieve this relies on the encoding of spatial information with population codes. The set of network responses to stimuli in different locations (tuning curves) constitutes a set of basis functions that can be combined linearly through weighted synaptic connections to approximate nonlinear transformations of the input variables. The question then arises: how is the appropriate synaptic connectivity obtained? Here we show that a network of spiking neurons can learn the coordinate transformation from one frame of reference to another, with connectivity that develops continuously in an unsupervised manner, based only on the correlations available in the environment and with a biologically realistic plasticity mechanism (spike timing-dependent plasticity).

Action Potentials↗
PubMed2006

Biophysical and phenomenological models of multiple spike interactions in spike-timing dependent plasticity.

Spike-timing dependent plasticity (STDP) is a form of associative synaptic modification which depends on the respective timing of pre- and post-synaptic spikes. The biophysical mechanisms underlying this form of plasticity are currently not known. We present here a biophysical model which captures the characteristics of STDP, such as its frequency dependency, and the effects of spike pair or spike triplet interactions. We also make links with other well-known plasticity rules. A simplified phenomenological model is also derived, which should be useful for fast numerical simulation and analytical investigation of the impact of STDP at the network level.

Action Potentials↗
PubMed2004

Semi-automated population of an online database of neuronal models (ModelDB) with citation information, using PubMed for validation.

Citations play an important role in medical and scientific databases by indicating the authoritative source of the data. Manual citation entry is tedious and prone to errors. We describe a method and make available computer scripts which automate the process of citation entry. We use an open citation project PERL module (PARSER) for parsing citation data that is then used to retrieve PubMed records to supply the (validated) reference. Our PERL scripts are available via a link in the web references section of this article.

Databases, Bibliographic↗
PubMed2003

Dendrodendritic inhibition and simulated odor responses in a detailed olfactory bulb network model.

In the olfactory bulb, both the spatial distribution and the temporal structure of neuronal activity appear to be important for processing odor information, but it is currently impossible to measure both of these simultaneously with high resolution and in all layers of the bulb. We have developed a biologically realistic model of the mammalian olfactory bulb, incorporating the mitral and granule cells and the dendrodendritic synapses between them, which allows us to observe the network behavior in detail. The cell models were based on previously published work. The attributes of the synapses were obtained from the literature. The pattern of synaptic connections was based on the limited experimental data in the literature on the statistics of connections between neurons in the bulb. The results of simulation experiments with electrical stimulation agree closely in most details with published experimental data. This gives confidence that the model is capturing features of network interactions in the real olfactory bulb. The model predicts that the time course of dendrodendritic inhibition is dependent on the network connectivity as well as on the intrinsic parameters of the synapses. In response to simulated odor stimulation, strongly activated mitral cells tend to suppress neighboring cells, the mitral cells readily synchronize their firing, and increasing the stimulus intensity increases the degree of synchronization. Preliminary experiments suggest that slow temporal changes in the degree of synchronization are more useful in distinguishing between very similar odorants than is the spatial distribution of mean firing rate.

Dendrites↗