Computational object recognition: a biologically motivated approach

We propose a conceptual framework for artificial object recognition systems based on findings from neurophysiological and neuropsychological research on the visual system in primate cortex. We identify some essential questions, which have to be addressed in the course of designing object recognition systems. As answers, we review some major aspects of biological object recognition, which are then translated into the technical field of computer vision. The key suggestions are the use of incremental and view-based approaches together with the ability of online feature selection and the interconnection of object-views to form an overall object representation. The effectiveness of the computational approach is estimated by testing a possible realization in various tasks and conditions explicitly designed to allow for a direct comparison with the biological counterpart. The results exhibit excellent performance with regard to recognition accuracy, the creation of sparse models and the selection of appropriate features.     

Encoding and processing biologically relevant temporal information in electrosensory systems

Wave-type weakly electric fish are specialists in time-domain processing: behaviors in these animals are often tightly correlated with the temporal structure of electrosensory signals. Behavioral responses in these fish can be dependent on differences in the temporal structure of electrosensory signals alone. This feature has facilitated the study of temporal codes and processing in central nervous system circuits of these animals. The temporal encoding and mechanisms used to transform temporal codes in the brain have been identified and characterized in several species, including South American gymnotid species and in the African mormyrid genus Gymnarchus. These distantly related groups use similar strategies for neural computations of information on the order of microseconds, milliseconds, and seconds. Here, we describe a suite of mechanisms for behaviorally relevant computations of temporal information that have been elucidated in these systems. These results show the critical role that behavioral experiments continue to have in the study of the neural control of behavior and its evolution.     

Amines and motivated behaviors: a simpler systems approach to complex behavioral phenomena

Recent investigations in invertebrate neurobiology have opened up new lines of research into the basic roles of behavioral, neurochemical, and physiological effects in complex behavioral phenomena, such as aggression and drug-sensitive reward. This review summarizes a body of quantitative work, which identifies biogenic amines as a pharmacological substrate for motivated behaviors in the crayfish, Orconectes rusticus. Specifically, this paper details progress that has (1) explored links between serotonin and an individuals aggressive state, and (2) demonstrated the existence of crayfish reward systems that are sensitive to human drugs of abuse, such as psychostimulants. First, we summarize a set of experimental approaches that explore aggression in crayfish and the significance of aminergic systems in its control. Agonistic behavior in crustaceans can be characterized within a quantitative framework; different types of behavioral plasticity in aggressive behavior are in need of physiological explanation, and pharmacological intervention involving serotonergic systems bring about characteristic changes in behavior. A second set of experiments demonstrates that psychostimulants (cocaine and D-amphetamine) serve as rewards when an intra-circulatory infusion is coupled to a distinct visual environment. Work in novel model systems such as crayfish constitutes a useful comparative approach to the study of aggression and drug addiction.     

Gustatory processing: a dynamic systems approach

Recent gustatory studies have provided a growing body of evidence that taste processing is dynamic and distributed, and the taste system too complex to be adequately described by traditional feed-forward models of taste coding. Current research demonstrates that neuronal responses throughout the gustatory neuroaxis are broad, variable and temporally structured, as a result of the fact that the taste network is extensive and heavily interconnected, containing modulatory pathways, many of which are reciprocal. Multimodal influences (e.g. olfactory and somatosensory) and effects of internal state (e.g. attention and expectation), shown in both behavioral and neuronal responses to taste stimuli, add further complexity to neural taste responses. Future gustatory research should extend to more brain regions, incorporate more connections, and analyze behaviors and neuronal responses in both time- and state-dependent manners.     

Online learning of objects in a biologically motivated visual architecture

We present a biologically motivated architecture for object recognition that is capable of online learning of several objects based on interaction with a human teacher. The system combines biological principles such as appearance-based representation in topographical feature detection hierarchies and context-driven transfer between different levels of object memory. Training can be performed in an unconstrained environment by presenting objects in front of a stereo camera system and labeling them by speech input. The learning is fully online and thus avoids an artificial separation of the interaction into training and test phases. We demonstrate the performance on a challenging ensemble of 50 objects.     

Lung cancer risk in mice: analysis of fractionation effects and neutron RBE with a biologically motivated model

Data from Argonne National Laboratory on lung cancer in 15,975 mice with acute and fractionated exposures to gamma rays and neutrons are analyzed with a biologically motivated model with two rate-limiting steps and clonal expansion. Fractionation effects and effects of radiation quality can be explained well by the estimated kinetic parameters. Both an initiating and a promoting action of neutrons and gamma rays are suggested. While for gamma rays the initiating event is described well with a linear dose-rate dependence, for neutrons a nonlinear term is needed, with less effectiveness at higher dose rates. For the initiating event, the neutron RBE compared to gamma rays is about 10 when the dose rate during each fraction is low. For higher dose rates this RBE decreases strongly. The estimated lifetime relative risk for radiation-induced lung cancers from 1 Gy of acute gamma-ray exposure at an age of 110 days is 1.27 for male mice and 1.53 for female mice. For doses less than 1 Gy, the effectiveness of fractionated exposure to gamma rays compared to acute exposure is between 0.4 and 0.7 in both sexes. For lifetime relative risk, the RBE from acute neutrons at low doses is estimated at about 10 relative to acute gamma-ray exposure. It decreases strongly with dose. For fractionated neutrons, it is lower, down to about 4 for male mice.     

Networked buffering: a basic mechanism for distributed robustness in complex adaptive systems

A generic mechanism - networked buffering - is proposed for the generation of robust traits in complex systems. It requires two basic conditions to be satisfied: 1) agents are versatile enough to perform more than one single functional role within a system and 2) agents are degenerate, i.e. there exists partial overlap in the functional capabilities of agents. Given these prerequisites, degenerate systems can readily produce a distributed systemic response to local perturbations. Reciprocally, excess resources related to a single function can indirectly support multiple unrelated functions within a degenerate system. In models of genome:proteome mappings for which localized decision-making and modularity of genetic functions are assumed, we verify that such distributed compensatory effects cause enhanced robustness of system traits. The conditions needed for networked buffering to occur are neither demanding nor rare, supporting the conjecture that degeneracy may fundamentally underpin distributed robustness within several biotic and abiotic systems. For instance, networked buffering offers new insights into systems engineering and planning activities that occur under high uncertainty. It may also help explain recent developments in understanding the origins of resilience within complex ecosystems.     

SPSRG: a prediction approach for correlated failures in distributed computing systems

Failure instances in distributed computing systems (DCSs) have exhibited temporal and spatial correlations, where a single failure instance can trigger a set of failure instances simultaneously or successively within a short time interval. In this work, we propose a correlated failure prediction approach (CFPA) to predict correlated failures of computing elements in DCSs. The approach models correlated-failure patterns using the concept of probabilistic shared risk groups and makes a prediction for correlated failures by exploiting an association rule mining approach in a parallel way. We conduct extensive experiments to evaluate the feasibility and effectiveness of CFPA using both failure traces from Los Alamos National Lab and simulated datasets. The experimental results show that the proposed approach outperforms other approaches in both the failure prediction performance and the execution time, and can potentially provide better prediction performance in a larger system.     

Biomedical implications of information processing in chemical systems: non-classical approach to photochemistry of coordination compounds

Analogies between photoactive nitric oxide generators and various electronic devices: logic gates and operational amplifiers are presented. These analogies have important biological consequences: application of control parameters allows for better targeting and control of nitric oxide drugs. The same methodology may be applied in the future for other therapeutic strategies and at the same time helps to understand natural regulatory and signaling processes in biological systems.     

Macroreticulate buffers: a novel approach to pH control in living systems.

It is possible to control the pH of growing living systems in vitro by adding, to the growth media, macroreticulate buffers, i.e. amphoteric resins made with buffering and titrant groups simultaneously affixed to the matrix. Such beads possess a very precise isoelectric point (pI) and are able to maintain the solutions' pH close to their pI values for extended growth periods. These pearls are made of a neutral polyacrylamide backbone containing up to 200 mM grafted weak acrylamido acids and bases. It is possible to produce such buffers with any desired pH value in the pH 2.5-11 scale. An example is given of conditioning the pH of endive plants grown hydroponically.     

The neural mechanisms of gustation: a distributed processing code

Whenever food is placed in the mouth, taste receptors are stimulated. Simultaneously, different types of sensory fibre that monitor several food attributes such as texture, temperature and odour are activated. Here, we evaluate taste and oral somatosensory peripheral transduction mechanisms as well as the multi-sensory integrative functions of the central pathways that support the complex sensations that we usually associate with gustation. On the basis of recent experimental data, we argue that these brain circuits make use of distributed ensemble codes that represent the sensory and post-ingestive properties of tastants.     

The control of shoot branching: an example of plant information processing

Throughout their life cycle, plants adjust their body plan to suit the environmental conditions in which they are growing. A good example of this is in the regulation of shoot branching. Axillary meristems laid down in each leaf formed from the primary shoot apical meristem can remain dormant, or activate to produce a branch. The decision whether to activate an axillary meristem involves the assessment of a wide range of external environmental, internal physiological and developmental factors. Much of this information is conveyed to the axillary meristem via a network of interacting hormonal signals that can integrate inputs from diverse sources, combining multiple local signals to generate a rich source of systemically transmitted information. Local interpretation of the information provides another layer of control, ensuring that appropriate decisions are made. Rapid progress in molecular biology is uncovering the component parts of this signalling network, and combining this with physiological studies and mathematical modelling will allow the operation of the system to be better understood.     

A biologically motivated and analytically soluble model of collective oscillations in the cortex

A model of an associative network of spiking neurons with stationary states, globally locked oscillations, and weakly locked oscillatory states is presented and analyzed. The network is close to biology in the following sense. First, the neurons spike and our model includes an absolute refractory period after each spike. Second, we consider a distribution of axonal delay times. Finally, we describe synaptic signal transmission by excitatory and inhibitory potentials (EPSP and IPSP) with a realistic shape, that is, through a response kernel. During retrieval of a pattern, all active neurons exhibit periodic spike bursts which may or may not be synchronized (locked) into a coherent oscillation. We derive an analytical condition of locking and calculate the period of collective activity during oscillatory retrieval. In a stationary retrieval state, the overlap assumes a constant value proportional to the mean firing rate of the neurons. It is argued that in a biological network an intermediate scenario of weak locking is most likely.     

System of distributed storage and analysis of genomic information

A distributed computing system is developed to search and analyze genetic databases using parallel computing technologies. Queries are processed by a local network PC cluster. A universal task and data exchange format is developed for effective query processing. A multilevel hierarchic task batching procedure is elaborated to generate multiple subtasks and distribute them over cluster units under dynamic priority levels and with dynamic distribution of replicated source data subbases. Primary source data preparation and generation of annotation word indices are used to significantly reduce query processing time.     

Regime changes in ecological systems: an information theory approach

We present our efforts at developing an ecological system index using information theory. Specifically, we derive an expression for Fisher Information based on sampling of the system trajectory as it evolves in the space defined by the state variables of the system, i.e. its state space. The Fisher Information index, as we have derived it, is a measure of system order, and captures the characteristic variation in speed and acceleration along the system's periodic steady-state trajectories. When calculated repeatedly over the system period, this index tracks steady states and transient behavior. We believe that such an index could be useful in detecting system 'flips' associated with a regime change, i.e. determining when systems are in a transient between one steady state and another. We illustrate the concepts using model ecosystems.