The evidence for neural information processing with precise spike-times: A survey

This paper surveys recent findings in neuroscience regarding the behavioral relevancy of the precise timing with which real spiking neurons emit spikes. The literature suggests that in almost any system where the processing-speed of a neural (sub)-system is required to be high, the timing of single spikes can be very precise and reliable. Additionally, new, more refined methods are finding precisely timed spikes where previously none where found. This line of evidence thus provides additional motivation for researching the computational properties of networks of artificial spiking neurons that compute with more precisely timed spikes. This revised version was published online in June 2006 with corrections to the Cover Date.     

Early recognition of Streptococcus pneumoniae in patients with community-acquired pneumonia

In order to elucidate the cause of osteonecrosis of the femoral head in spontaneously hypertensive rats (SHRs), which resembles the osteonecrosis of Perthes' disease, we observed the three-dimensional structure of vascular casts of the blood vessels feeding the femoral head using both optical and scanning electron microscopes. During the period of 9-15 weeks after birth, when osteonecrosis of the femoral heads in SHRs occurred frequently, the lateral epiphyseal vessels (LEVs), which were the main feeding vessels, entered from the lateral of the femoral heads. Anastomosing branches of LEVs between the epiphysis and the femoral neck were scarce even in the femoral heads showing normal ossification. It seemed that the development of LEVs in SHRs did not proceed normally in this period. Furthermore, remarkable segmental stenosis and the obstruction of LEVs were often recognized near the lateral of the femoral heads. These results suggest that LEVs in growing SHRs have the vascular structure that could cause an interruption of the blood supply to the femoral heads.     

Unsupervised clustering with spiking neurons by sparse temporalcoding and multilayer RBF networks

We demonstrate that spiking neural networks encoding information in the timing of single spikes are capable of computing and learning clusters from realistic data. We show how a spiking neural network based on spike-time coding and Hebbian learning can successfully perform unsupervised clustering on real-world data, and we demonstrate how temporal synchrony in a multilayer network can induce hierarchical clustering. We develop a temporal encoding of continuously valued data to obtain adjustable clustering capacity and precision with an efficient use of neurons: input variables are encoded in a population code by neurons with graded and overlapping sensitivity profiles. We also discuss methods for enhancing scale-sensitivity of the network and show how the induced synchronization of neurons within early RBF layers allows for the subsequent detection of complex clusters.     

Multi-agent Pareto appointment exchanging in hospital patient scheduling

We present a dynamic and distributed approach to the hospital patient scheduling problem, in which patients can have multiple appointments that have to be scheduled to different resources. To efficiently solve this problem we develop a multi-agent Pareto-improvement appointment exchanging algorithm: MPAEX. It respects the decentralization of scheduling authorities and continuously improves patient schedules in response to the dynamic environment. We present models of the hospital patient scheduling problem in terms of the health care cycle where a doctor repeatedly orders sets of activities to diagnose and/or treat a patient. We introduce the Theil index to the health care domain to characterize different hospital patient scheduling problems in terms of the degree of relative workload inequality between required resources. In experiments that simulate a broad range of hospital patient scheduling problems, we extensively compare the performance of MPAEX to a set of scheduling benchmarks. The distributed and dynamic MPAEX performs almost as good as the best centralized and static scheduling heuristic, and is robust for variations in the model settings. A preliminary version of this work has appeared as [1].     

Reducing the variability of neural responses: a computational theory of spike-timing-dependent plasticity

Experimental studies have observed synaptic potentiation when a presynaptic neuron fires shortly before a postsynaptic neuron and synaptic depression when the presynaptic neuron fires shortly after. The dependence of synaptic modulation on the precise timing of the two action potentials is known as spike-timing dependent plasticity (STDP). We derive STDP from a simple computational principle: synapses adapt so as to minimize the postsynaptic neuron's response variability to a given presynaptic input, causing the neuron's output to become more reliable in the face of noise. Using an objective function that minimizes response variability and the biophysically realistic spike-response model of Gerstner (2001), we simulate neurophysiological experiments and obtain the characteristic STDP curve along with other phenomena, including the reduction in synaptic plasticity as synaptic efficacy increases. We compare our account to other efforts to derive STDP from computational principles and argue that our account provides the most comprehensive coverage of the phenomena. Thus, reliability of neural response in the face of noise may be a key goal of unsupervised cortical adaptation.     

The effects of pair-wise and higher order correlations on the firing rate of a post-synaptic neuron

Coincident firing of neurons projecting to a common target cell is likely to raise the probability of firing of this postsynaptic cell. Therefore, synchronized firing constitutes a significant event for postsynaptic neurons and is likely to play a role in neuronal information processing. Physiological data on synchronized firing in cortical networks are based primarily on paired recordings and cross-correlation analysis. However, pair-wise correlations among all inputs onto a postsynaptic neuron do not uniquely determine the distribution of simultaneous postsynaptic events. We develop a framework in order to calculate the amount of synchronous firing that, based on maximum entropy, should exist in a homogeneous neural network in which the neurons have known pair-wise correlations and higher-order structure is absent. According to the distribution of maximal entropy, synchronous events in which a large proportion of the neurons participates should exist even in the case of weak pair-wise correlations. Network simulations also exhibit these highly synchronous events in the case of weak pair-wise correlations. If such a group of neurons provides input to a common postsynaptic target, these network bursts may enhance the impact of this input, especially in the case of a high postsynaptic threshold. The proportion of neurons participating in synchronous bursts can be approximated by our method under restricted conditions. When these conditions are not fulfilled, the spike trains have less than maximal entropy, which is indicative of the presence of higher-order structure. In this situation, the degree of synchronicity cannot be derived from the pair-wise correlations.