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Multimodal neuronal maps, combining input from two or more sensory systems, play a key role in the processing of sensory and motor information. For such maps to be of any use, the input from all participating modalities must be calibrated so that a stimulus at a specific spatial location is represented at an unambiguous position in the multimodal map. Here we discuss two methods based on supervised spike-timing-dependent plasticity (STDP) to gauge input from different sensory modalities so as to ensure a proper map alignment. The first uses an excitatory teacher input. It is therefore called excitation-mediated learning. The second method is based on an inhibitory teacher signal, as found in the barn owl, and is called inhibition-mediated learning. Using detailed analytical calculations and numerical simulations, we demonstrate that inhibitory teacher input is essential if high-quality multimodal integration is to be learned rapidly. Furthermore, we show that the quality of the resulting map is not so much limited by the quality of the teacher signal but rather by the accuracy of the input from other sensory modalities.

The great promise of biological science is not its 'mathematization' per se, but the creative interaction between experimental biology and what one, in analogy to physics, may simply call theoretical biology. The key to, and also the great challenge in, fulfilling this promise is to find the correct fundamental notions to mathematically describe biological reality.

The clawed frog Xenopus is an aquatic predator catching prey at night by detecting water movements caused by its prey. We present a general method, a "minimal model" based on a minimum-variance estimator, to explain prey detection through the frog's many lateral-line organs, even in case several of them are defunct. We show how waveform reconstruction allows Xenopus' neuronal system to determine both the direction and the character of the prey and even to distinguish two simultaneous wave sources. The results can be applied to many aquatic amphibians, fish, or reptiles such as crocodiles.

The present article discusses computational hypotheses on corticothalamic feedback and modulation of cortical response properties. We have recently proposed that the two phenomena are related, hypothesizing that neuronal velocity preference in the visual cortex is altered by feedback to the lateral geniculate nucleus. We now contrast the common view that response adaptation to stimuli subserves a function of redundancy reduction with the idea that it may enhance cortical representation of objects. Our arguments lead to the concept that the corticothalamic loop is involved in reducing sensory input to behaviourally relevant aspects, a pre-attentive gating.

A temporal-feature map is a topographic neuronal representation of temporal attributes of phenomena or objects that occur in the outside world. We explain the evolution of such maps by means of a spike-based Hebbian learning rule in conjunction with a presynaptically unspecific contribution in that, if a synapse changes, then all other synapses connected to the same axon change by a small fraction as well. The learning equation is solved for the case of an array of Poisson neurons. We discuss the evolution of a temporal-feature map and the synchronization of the single cells' synaptic structures, in dependence upon the strength of presynaptic unspecific learning. We also give an upper bound for the magnitude of the presynaptic interaction by estimating its impact on the noise level of synaptic growth. Finally, we compare the results with those obtained from a learning equation for nonlinear neurons and show that synaptic structure formation may profit from the nonlinearity.

The decision of the immune system to trigger immune responses that are, respectively, induced by Th1 or Th2 effectors is a critical one, because it profoundly influences disease outcome. We have recently constructed a mathematical model of Th1-Th2-pathogen interactions that shows that the major decisional events can often be successfully determined by the intrinsic behaviour of the T helper system itself. For certain dangerous types of pathogens, however, which replicate rapidly or have developed strategies to evade the immune response, additional stimuli may be necessary. As a possible mechanism for the decision-making process innate immune recognition has been proposed. Here we present an enlarged version of our model, which incorporates signals created from the innate immune system after pathogen recognition. The model analysis suggests that there is fault-tolerance of the T helper system to incorrect Th1 signals. In the presence of incorrect Th1 stimuli an initial Th1 response is shifted to the correct Th2-dominated response owing to the intrinsic T helper dynamics. By contrast, according to our model there is no fault-tolerance for incorrect Th2 signals. In fact, if timing is unimportant then Th2 signals are superfluous since the intrinsic T helper dynamics provide an automatic switch to Th2 if Th1 effectors fail to control the pathogen. Th2 signals may, however, be required to accelerate the onset of the Th2 response. Additionally, we discuss the role of feedback where successful pathogen destruction leads to up-regulation of activation of the effective T helper type. As one possibility we examine the role of CpG motifs as indicators for successful pathogen destruction. Differences between instructive and feedback mechanisms are highlighted.