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Tim C Pearce

Publications and source records attributed to Tim C Pearce.

5 recordsLinked to original sources
PubMed2006

Programmable logic construction kits for hyper-real-time neuronal modeling.

Programmable logic designs are presented that achieve exact integration of leaky integrate-and-fire soma and dynamical synapse neuronal models and incorporate spike-time dependent plasticity and axonal delays. Highly accurate numerical performance has been achieved by modifying simpler forward-Euler-based circuitry requiring minimal circuit allocation, which, as we show, behaves equivalently to exact integration. These designs have been implemented and simulated at the behavioral and physical device levels, demonstrating close agreement with both numerical and analytical results. By exploiting finely grained parallelism and single clock cycle numerical iteration, these designs achieve simulation speeds at least five orders of magnitude faster than the nervous system, termed here hyper-real-time operation, when deployed on commercially available field-programmable gate array (FPGA) devices. Taken together, our designs form a programmable logic construction kit of commonly used neuronal model elements that supports the building of large and complex architectures of spiking neuron networks for real-time neuromorphic implementation, neurophysiological interfacing, or efficient parameter space investigations.

Action Potentials↗
PubMed2003

Wiring the olfactory bulb--activity-dependent models of axonal targeting in the developing olfactory pathway.

Two recent experimental studies /20,21/ revealed that odorant-evoked activity-dependent competition is significant in the organisation and maintenance of the olfactory system. In this paper, we investigate the generation of a chemotopic sensory map in the olfactory bulb through three models driven by high-density optical chemosensor arrays which have similar properties to olfactory receptor neurons. By exposing the sensor arrays to various odours, these models were subjected to Hebbian learning to achieve self-organisation, potentially explaining the activity-dependent competition demonstrated by these recent studies. Our final model also predicts a role for periglomerular cells in the formation of the chemotopic sensory map in the olfactory bulb.

Animals↗
PubMed2002

Automatic decoding of sensor types within randomly ordered, high-density optical sensor arrays.

In this paper automatic sensor identification of sensor classes within a high-density randomized array, without a priori knowledge of sensor locations, is demonstrated. Two different fluorescence-based sensor types, with hundreds of replicates each, were randomly distributed into an optical imaging fiber array platform. The sensor element types were vapor-sensitive microspheres with the environmentally-sensitive fluorescent dye Nile Red adsorbed on their surface. Nile Red undergoes spectral changes when exposed to different microenvironmental polarity conditions, e.g. microsphere surface polarity or odor exposure. These reproducible sensor spectral changes, or sensor-response profiles, enable sensors within a randomized array to be grouped into categories by optical decoding methods. Two computational decoding methods (supervised and unsupervised) are introduced; equal classification rates were achieved for both. By comparing sensor responses from a randomized array with those obtained from known (control) arrays, 587 sensors were correctly classified with 99.32% accuracy. Although both methods were equally effective, the unsupervised method, which uses sensor response changes to odor exposure, is a better decoding model for the vapor-sensitive arrays studied, because it relies only on the odor-response profiles. Another decoding technique employed the emission spectra of the sensors and is more applicable to other types of multiplexed fluorescence-based arrays and assays. The sensor-decoding techniques are compared to demonstrate that sensors within high-density optical chemosensor arrays can be positionally-registered, or decoded, with no additional overhead in time or expense other than collecting the sensor-response profiles.

Journal Article↗
PubMedDate not supplied

Why do olfactory neurons have unspecific receptive fields?

Biological olfactory neurons are deployed as a population, most responding to a large variety of chemical compounds, that is, they possess unspecific receptive fields. The question of whether this unspecificity results from some physical constraint placed upon chemical transduction, or on the other hand, is beneficial to system performance is unclear. In this paper we employ the notion of Fisher information to address this question by quantifying how both the distribution and the tunings of the receptive fields within olfactory receptor populations affect the optimal estimation performance of the system. Our results show that overlapping sensory neuron tunings that respond to common chemical compounds have better estimation performance than perfectly specific tunings. Our results suggest two phenomena that might represent general principles of organization within biological sensory systems responding to multiple stimuli: maximization of the diversity of tunings and homogeneity in the distribution of these different receptive fields across the stimulus space (independent of the statistics of the input stimuli). Our model predicts that a local randomized mechanism controlling receptor specificities generates optimal multidimensional stimulus estimation, for which there is some experimental evidence from the biology.

Olfactory Receptor Neurons↗