Towards a biomolecular computer.

A new version of computing and information processing devices may result from major principles of information processing at molecular level. Non-discrete biomolecular computers based on these principles seems to be capable of solving problems of high computational complexity. One of the possible ways to implement these devices is based on biochemical non-linear dynamical systems. Means and ways to materialize biomolecular computers are discussed.     

Efficient Physical Embedding of Topologically Complex Information Processing Networks in Brains and Computer Circuits

Nervous systems are information processing networks that evolved by natural selection, whereas very large scale integrated (VLSI) computer circuits have evolved by commercially driven technology development. Here we follow historic intuition that all physical information processing systems will share key organizational properties, such as modularity, that generally confer adaptivity of function. It has long been observed that modular VLSI circuits demonstrate an isometric scaling relationship between the number of processing elements and the number of connections, known as Rent''s rule, which is related to the dimensionality of the circuit''s interconnect topology and its logical capacity. We show that human brain structural networks, and the nervous system of the nematode C. elegans, also obey Rent''s rule, and exhibit some degree of hierarchical modularity. We further show that the estimated Rent exponent of human brain networks, derived from MRI data, can explain the allometric scaling relations between gray and white matter volumes across a wide range of mammalian species, again suggesting that these principles of nervous system design are highly conserved. For each of these fractal modular networks, the dimensionality of the interconnect topology was greater than the 2 or 3 Euclidean dimensions of the space in which it was embedded. This relatively high complexity entailed extra cost in physical wiring: although all networks were economically or cost-efficiently wired they did not strictly minimize wiring costs. Artificial and biological information processing systems both may evolve to optimize a trade-off between physical cost and topological complexity, resulting in the emergence of homologous principles of economical, fractal and modular design across many different kinds of nervous and computational networks.     

Integrated information increases with fitness in the evolution of animats

One of the hallmarks of biological organisms is their ability to integrate disparate information sources to optimize their behavior in complex environments. How this capability can be quantified and related to the functional complexity of an organism remains a challenging problem, in particular since organismal functional complexity is not well-defined. We present here several candidate measures that quantify information and integration, and study their dependence on fitness as an artificial agent ("animat") evolves over thousands of generations to solve a navigation task in a simple, simulated environment. We compare the ability of these measures to predict high fitness with more conventional information-theoretic processing measures. As the animat adapts by increasing its "fit" to the world, information integration and processing increase commensurately along the evolutionary line of descent. We suggest that the correlation of fitness with information integration and with processing measures implies that high fitness requires both information processing as well as integration, but that information integration may be a better measure when the task requires memory. A correlation of measures of information integration (but also information processing) and fitness strongly suggests that these measures reflect the functional complexity of the animat, and that such measures can be used to quantify functional complexity even in the absence of fitness data.     

Finding paths in a labyrinth based on reaction-diffusion media.

During the past few decades, many proposals were made on how to take an effective solution for finding a path in a labyrinth, one of the most well known problems of high computational complexity inherent in information processing by biomolecular and biological entities. In particular, attempts were made to use a technique attractive enough for solving this problem based on wave processes in reaction-diffusion media. Trigger waves in reaction diffusion systems spread simultaneously through all paths of the labyrinth in a highly parallel mode. Regretfully, the velocity of these waves is extremely low which gave no way for the practical implementation of this technique until now. An effective 'hardware' system was designed which was capable of finding a path in a labyrinth using fast phase waves. Three principal points were assumed as a basis for this design, i.e. (1) hybrid architecture that combined an information processing reaction-diffusion medium which performs operations of high computational complexity with a digital computer carrying out supplementary image processing operations; (2) light-sensitive information processing media of Belousov-Zhabotinsky type that enables the simulation of the labyrinth and spreading wave evolution by their images stored in the medium and reduces the problem to the image processing operations; (3) fast light-induced phase wave processes that spreads through the labyrinth in several seconds instead of hours which is typical for trigger waves inherent in reaction-diffusion media. These fundamentals along with the additional procedure of testing for labyrinth fragment connectness provided us with the opportunity to solve labyrinth problems.     

Balance between Noise and Information Flow Maximizes Set Complexity of Network Dynamics

Boolean networks have been used as a discrete model for several biological systems, including metabolic and genetic regulatory networks. Due to their simplicity they offer a firm foundation for generic studies of physical systems. In this work we show, using a measure of context-dependent information, set complexity, that prior to reaching an attractor, random Boolean networks pass through a transient state characterized by high complexity. We justify this finding with a use of another measure of complexity, namely, the statistical complexity. We show that the networks can be tuned to the regime of maximal complexity by adding a suitable amount of noise to the deterministic Boolean dynamics. In fact, we show that for networks with Poisson degree distributions, all networks ranging from subcritical to slightly supercritical can be tuned with noise to reach maximal set complexity in their dynamics. For networks with a fixed number of inputs this is true for near-to-critical networks. This increase in complexity is obtained at the expense of disruption in information flow. For a large ensemble of networks showing maximal complexity, there exists a balance between noise and contracting dynamics in the state space. In networks that are close to critical the intrinsic noise required for the tuning is smaller and thus also has the smallest effect in terms of the information processing in the system. Our results suggest that the maximization of complexity near to the state transition might be a more general phenomenon in physical systems, and that noise present in a system may in fact be useful in retaining the system in a state with high information content.     

Parsimony,Exhaustivity and Balanced Detection in Neocortex

The layout of sensory brain areas is thought to subtend perception. The principles shaping these architectures and their role in information processing are still poorly understood. We investigate mathematically and computationally the representation of orientation and spatial frequency in cat primary visual cortex. We prove that two natural principles, local exhaustivity and parsimony of representation, would constrain the orientation and spatial frequency maps to display a very specific pinwheel-dipole singularity. This is particularly interesting since recent experimental evidences show a dipolar structures of the spatial frequency map co-localized with pinwheels in cat. These structures have important properties on information processing capabilities. In particular, we show using a computational model of visual information processing that this architecture allows a trade-off in the local detection of orientation and spatial frequency, but this property occurs for spatial frequency selectivity sharper than reported in the literature. We validated this sharpening on high-resolution optical imaging experimental data. These results shed new light on the principles at play in the emergence of functional architecture of cortical maps, as well as their potential role in processing information.     

Processing power limits social group size: computational evidence for the cognitive costs of sociality

Sociality is primarily a coordination problem. However, the social (or communication) complexity hypothesis suggests that the kinds of information that can be acquired and processed may limit the size and/or complexity of social groups that a species can maintain. We use an agent-based model to test the hypothesis that the complexity of information processed influences the computational demands involved. We show that successive increases in the kinds of information processed allow organisms to break through the glass ceilings that otherwise limit the size of social groups: larger groups can only be achieved at the cost of more sophisticated kinds of information processing that are disadvantageous when optimal group size is small. These results simultaneously support both the social brain and the social complexity hypotheses.     

“Neural” computation of decisions in optimization problems

Highly-interconnected networks of nonlinear analog neurons are shown to be extremely effective in computing. The networks can rapidly provide a collectively-computed solution (a digital output) to a problem on the basis of analog input information. The problems to be solved must be formulated in terms of desired optima, often subject to constraints. The general principles involved in constructing networks to solve specific problems are discussed. Results of computer simulations of a network designed to solve a difficult but well-defined optimization problem-the Traveling-Salesman Problem-are presented and used to illustrate the computational power of the networks. Good solutions to this problem are collectively computed within an elapsed time of only a few neural time constants. The effectiveness of the computation involves both the nonlinear analog response of the neurons and the large connectivity among them. Dedicated networks of biological or microelectronic neurons could provide the computational capabilities described for a wide class of problems having combinatorial complexity. The power and speed naturally displayed by such collective networks may contribute to the effectiveness of biological information processing.     

Nonlinear dynamics of the distributed biochemical systems functioning in the dissipative structure formation mode

Nonlinear dynamical biomolecular systems can evidently be considered as prototypes of information processing devices at molecular level capable to solve problems of high computational complexity. Keeping in mind this goal the dynamics of biochemical system based on enzymatic oxidation of uric acid was considered. The system was studied in the version of distributed biomolecular structure having predetermined geometry of enzyme distribution on a porous planar medium. Being in the regime of stepwise dissipative structure formation this system demonstrated complicated modes of behaviour.     

Knowledge

We investigate the basic principles of structural knowledge. Structural knowledge underlies cognition, and it organizes, selects and assigns meaning to information. It is the result of evolutionary, cultural and developmental processes. Because of its own constraints, it needs to discover and exploit regularities and thereby achieve a complexity reduction.     

Emotional Picture and Word Processing: An fMRI Study on Effects of Stimulus Complexity

Neuroscientific investigations regarding aspects of emotional experiences usually focus on one stimulus modality (e.g., pictorial or verbal). Similarities and differences in the processing between the different modalities have rarely been studied directly. The comparison of verbal and pictorial emotional stimuli often reveals a processing advantage of emotional pictures in terms of larger or more pronounced emotion effects evoked by pictorial stimuli. In this study, we examined whether this picture advantage refers to general processing differences or whether it might partly be attributed to differences in visual complexity between pictures and words. We first developed a new stimulus database comprising valence and arousal ratings for more than 200 concrete objects representable in different modalities including different levels of complexity: words, phrases, pictograms, and photographs. Using fMRI we then studied the neural correlates of the processing of these emotional stimuli in a valence judgment task, in which the stimulus material was controlled for differences in emotional arousal. No superiority for the pictorial stimuli was found in terms of emotional information processing with differences between modalities being revealed mainly in perceptual processing regions. While visual complexity might partly account for previously found differences in emotional stimulus processing, the main existing processing differences are probably due to enhanced processing in modality specific perceptual regions. We would suggest that both pictures and words elicit emotional responses with no general superiority for either stimulus modality, while emotional responses to pictures are modulated by perceptual stimulus features, such as picture complexity.     

The mystery of structure and function of sensory processing areas of the neocortex: a resolution

Many different neural models have been proposed to account for major characteristics of the memory phenomenon family in primates. However, in spite of the large body of neurophysiological, anatomical and behavioral data, there is no direct evidence for supporting one model while falsifying the others. And yet, we can discriminate models based on their complexity and/or their predictive power. In this paper we present a computational framework with our basic assumption that neural information processing is performed by generative networks. A complex architecture is 'derived' by using information-theoretic principles. We find that our approach seems to uncover possible relations among the functional memory units (declarative and implicit memory) and the process of information encoding in primates. The architecture can also be related to the entorhinal-hippocampal loop. An effort is made to form a prototype of this computational architecture and to map it onto the functional units of the neocortex. This mapping leads us to claim that one may gain a better understanding by considering that anatomical and functional layers of the cortex differ. Philosophical consequences regarding the homunculus fallacy are also considered.     

The olfactory circuit of the fruit fly Drosophila melanogaster

The olfactory circuit of the fruit fly Drosophila melanogaster has emerged in recent years as an excellent paradigm for studying the principles and mechanisms of information processing in neuronal circuits. We discuss here the organizational principles of the olfactory circuit that make it an attractive model for experimental manipulations, the lessons that have been learned, and future challenges.     

Information processing mechanisms in microtubules at physiological temperature: Model predictions for experimental tests

Both direct and indirect experimental evidence has shown signaling, communication and conductivity in microtubules (MTs). Theoretical models have predicted that MTs can be potentially used for both classical and quantum information processing although controversies arose in regard to physiological temperature effects on these capabilities. In this paper, MTs have been studied using well-established principles of classical statistical physics as applied to information processing, information storage and signal propagation. To investigate the existence of information processing in MTs we used cellular automata (CA) models with neighbor rules based on the electrostatic properties of the molecular structure of tubulin, and both synchronous and asynchronous updating methods. We obtained a phase diagram of possible dynamic behaviors in MTs that depend on the values of characteristic physical parameters that can be experimentally verified.     

How Can We Identify and Communicate the Ecological Value of Deep-Sea Ecosystem Services?

Submarine canyons are considered biodiversity hotspots which have been identified for their important roles in connecting the deep sea with shallower waters. To date, a huge gap exists between the high importance that scientists associate with deep-sea ecosystem services and the communication of this knowledge to decision makers and to the wider public, who remain largely ignorant of the importance of these services. The connectivity and complexity of marine ecosystems makes knowledge transfer very challenging, and new communication tools are necessary to increase understanding of ecological values beyond the science community. We show how the Ecosystem Principles Approach, a method that explains the importance of ocean processes via easily understandable ecological principles, might overcome this challenge for deep-sea ecosystem services. Scientists were asked to help develop a list of clear and concise ecosystem principles for the functioning of submarine canyons through a Delphi process to facilitate future transfers of ecological knowledge. These ecosystem principles describe ecosystem processes, link such processes to ecosystem services, and provide spatial and temporal information on the connectivity between deep and shallow waters. They also elucidate unique characteristics of submarine canyons. Our Ecosystem Principles Approach was successful in integrating ecological information into the ecosystem services assessment process. It therefore has a high potential to be the next step towards a wider implementation of ecological values in marine planning. We believe that successful communication of ecological knowledge is the key to a wider public support for ocean conservation, and that this endeavour has to be driven by scientists in their own interest as major deep-sea stakeholders.     

一种感知视觉运动信息的简化脑模型

视觉运动信息的感知过程,包括从局域运动检测到对模式整体运动的感知过程.我们以蝇视觉系统的图形-背景相对运动分辨的神经回路网络为基本框架,采用初级运动检测器的六角形阵列作为输入层,构造了一种感知视觉运动信息的简化脑模型,模拟了运动信息应该神经计算模型各个层次上的处理.该模型对差分行为实验结果作出了正确预测.本文并对空间生理整合的神经机制作了讨论.     

Sensory control of behavior in electric fish

The electrosensory system is ideally suited for the integration of behavioral and cellular approaches and, therefore, has led to the most detailed explanations of natural behaviors at the single-cell level. The electric sense shares basic principles in the coding of sensory information with more advanced sensory modalities and thus provides a convenient model system for studying neuronal mechanisms of information processing in general.     

A holographic model for associative memory chains

Holographic brain models are well suited to describe specific brain functions. Central nervous systems and holographic systems both show parallel information processing and non-localized storage in common. To process information both systems use correlation functions suggesting to develop cybernetical brain models in terms of holography. Associative holographic storage is done with two simultaneously existing patterns. They may reconstruct each other mutually. Time-sequentially existing patterns are connected to associative chains, if every two succeeding patterns do exist within a common period of time in order to be stored in pairs. Read out (recall) of associative chains—reconstructing coupled patterns which didn't exist simultaneously—requires advanced holographic techniques. Three different methods are described and tested experimentally. The underlying principles are feedback mechanisms, nonlinearities of the storage material and tridimensional architecture of the voluminous recording medium. Those principles evidently occur in neural storage systems supporting analogous information processing in neural- and holographic systems.     

Knowledge-based potential functions in protein design

Predicting protein sequences that fold into specific native three-dimensional structures is a problem of great potential complexity. Although the complete solution is ultimately rooted in understanding the physical chemistry underlying the complex interactions between amino acid residues that determine protein stability, recent work shows that empirical information about these first principles is embedded in the statistics of protein sequence and structure databases. This review focuses on the use of 'knowledge-based' potentials derived from these databases in designing proteins. In addition, the data suggest how the study of these empirical potentials might impact our fundamental understanding of the energetic principles of protein structure.     

On the increase in complexity in evolution II. The relativity of complexity and the principle of minimum increase

The complexity of a system cannot be measured in absolute terms, but only relative to a specified observer. As a result, the operational definition of complexity will be different for different sorts of systems and problems. In many cases it will not be as a real valued function.For the study of evolution we adopt as our definition the information content of the instructions required to build the system. Because we take into account factors such as the properties of the epigenetic system and the laws of physics and chemistry, this is not the same as the information content of the genome as measured in terms of codon or amino acid frequencies. Using this definition, we derive a principle of minimum increase in complexity, and we apply it to explain two well-known phenomena in evolution, Williston's law and parallelism. It is unlikely that any definite trends in the evolution of complex systems can be accounted for by simple optimization principles such as the law of natural selection, as these contain no arrow of time.