168 research outputs found
Neuroevolution on the Edge of Chaos
Echo state networks represent a special type of recurrent neural networks.
Recent papers stated that the echo state networks maximize their computational
performance on the transition between order and chaos, the so-called edge of
chaos. This work confirms this statement in a comprehensive set of experiments.
Furthermore, the echo state networks are compared to networks evolved via
neuroevolution. The evolved networks outperform the echo state networks,
however, the evolution consumes significant computational resources. It is
demonstrated that echo state networks with local connections combine the best
of both worlds, the simplicity of random echo state networks and the
performance of evolved networks. Finally, it is shown that evolution tends to
stay close to the ordered side of the edge of chaos.Comment: To appear in Proceedings of the Genetic and Evolutionary Computation
Conference 2017 (GECCO '17
A framework for the local information dynamics of distributed computation in complex systems
The nature of distributed computation has often been described in terms of
the component operations of universal computation: information storage,
transfer and modification. We review the first complete framework that
quantifies each of these individual information dynamics on a local scale
within a system, and describes the manner in which they interact to create
non-trivial computation where "the whole is greater than the sum of the parts".
We describe the application of the framework to cellular automata, a simple yet
powerful model of distributed computation. This is an important application,
because the framework is the first to provide quantitative evidence for several
important conjectures about distributed computation in cellular automata: that
blinkers embody information storage, particles are information transfer agents,
and particle collisions are information modification events. The framework is
also shown to contrast the computations conducted by several well-known
cellular automata, highlighting the importance of information coherence in
complex computation. The results reviewed here provide important quantitative
insights into the fundamental nature of distributed computation and the
dynamics of complex systems, as well as impetus for the framework to be applied
to the analysis and design of other systems.Comment: 44 pages, 8 figure
Information flow in a kinetic ising model peaks in the disordered phase
There is growing evidence that for a range of dynamical systems featuring complex interactions between large ensembles of interacting elements, mutual information peaks at order-disorder phase transitions. We conjecture that, by contrast, information flow in such systems will generally peak strictly on the disordered side of a phase transition. This conjecture is verified for a ferromagnetic 2D lattice Ising model with Glauber dynamics and a transfer entropy-based measure of systemwide information flow. Implications of the conjecture are considered, in particular, that for a complex dynamical system in the process of transitioning from disordered to ordered dynamics (a mechanism implicated, for example, in financial market crashes and the onset of some types of epileptic seizures); information dynamics may be able to predict an imminent transition
Partial information decomposition as a unified approach to the specification of neural goal functions
In many neural systems anatomical motifs are present repeatedly, but despite their structural similarity they can serve very different tasks. A prime example for such a motif is the canonical microcircuit of six-layered neo-cortex, which is repeated across cortical areas, and is involved in a number of different tasks (e.g. sensory, cognitive, or motor tasks). This observation has spawned interest in finding a common underlying principle, a ‘goal function’, of information processing implemented in this structure. By definition such a goal function, if universal, cannot be cast in processing-domain specific language (e.g. ‘edge filtering’, ‘working memory’). Thus, to formulate such a principle, we have to use a domain-independent framework. Information theory offers such a framework. However, while the classical framework of information theory focuses on the relation between one input and one output (Shannon’s mutual information), we argue that neural information processing crucially depends on the combination of multiple inputs to create the output of a processor. To account for this, we use a very recent extension of Shannon Information theory, called partial information decomposition (PID). PID allows to quantify the information that several inputs provide individually (unique information), redundantly (shared information) or only jointly (synergistic information) about the output. First, we review the framework of PID. Then we apply it to reevaluate and analyze several earlier proposals of information theoretic neural goal functions (predictive coding, infomax and coherent infomax, efficient coding). We find that PID allows to compare these goal functions in a common framework, and also provides a versatile approach to design new goal functions from first principles. Building on this, we design and analyze a novel goal function, called ‘coding with synergy’, which builds on combining external input and prior knowledge in a synergistic manner. We suggest that this novel goal function may be highly useful in neural information processing
Local information transfer as a spatiotemporal filter for complex systems
We present a measure of local information transfer, derived from an existing
averaged information-theoretical measure, namely transfer entropy. Local
transfer entropy is used to produce profiles of the information transfer into
each spatiotemporal point in a complex system. These spatiotemporal profiles
are useful not only as an analytical tool, but also allow explicit
investigation of different parameter settings and forms of the transfer entropy
metric itself. As an example, local transfer entropy is applied to cellular
automata, where it is demonstrated to be a novel method of filtering for
coherent structure. More importantly, local transfer entropy provides the first
quantitative evidence for the long-held conjecture that the emergent traveling
coherent structures known as particles (both gliders and domain walls, which
have analogues in many physical processes) are the dominant information
transfer agents in cellular automata.Comment: 12 page
On the information-theoretic formulation of network participation
The participation coefficient is a widely used metric of the diversity of a
node's connections with respect to a modular partition of a network. An
information-theoretic formulation of this concept of connection diversity,
referred to here as participation entropy, has been introduced as the Shannon
entropy of the distribution of module labels across a node's connected
neighbors. While diversity metrics have been studied theoretically in other
literatures, including to index species diversity in ecology, many of these
results have not previously been applied to networks. Here we show that the
participation coefficient is a first-order approximation to participation
entropy and use the desirable additive properties of entropy to develop new
metrics of connection diversity with respect to multiple labelings of nodes in
a network, as joint and conditional participation entropies. The
information-theoretic formalism developed here allows new and more subtle types
of nodal connection patterns in complex networks to be studied
Identification of a class of non-conventional ER-stress-response-derived immunogenic peptides
Efforts to overcome resistance to immune checkpoint blockade therapy have focused on vaccination strategies using neoepitopes, although they cannot be applied on a large scale due to the “private” nature of cancer mutations. Here, we show that infection of tumor cells with Salmonella induces the opening of membrane hemichannels and the extracellular release of proteasome-generated peptides by the exacerbation of endoplasmic reticulum (ER) stress. Peptides released by cancer cells foster an antitumor response in vivo, both in mice bearing B16F10 melanomas and in dogs suffering from osteosarcoma. Mass spectrometry analysis on the supernatant of human melanoma cells revealed 12 peptides capable of priming healthy-donor CD8+ T cells that recognize and kill human melanoma cells in vitro and when xenotransplanted in vivo. Hence, we identified a class of shared tumor antigens that are generated in ER-stressed cells, such as tumor cells, that do not induce tolerance and are not presented by healthy cells
Identification of a class of non-conventional ER-stress-response-derived immunogenic peptides
Efforts to overcome resistance to immune checkpoint blockade therapy have focused on vaccination strategies using neoepitopes, although they cannot be applied on a large scale due to the “private” nature of cancer mutations. Here, we show that infection of tumor cells with Salmonella induces the opening of membrane hemichannels and the extracellular release of proteasome-generated peptides by the exacerbation of endoplasmic reticulum (ER) stress. Peptides released by cancer cells foster an antitumor response in vivo, both in mice bearing B16F10 melanomas and in dogs suffering from osteosarcoma. Mass spectrometry analysis on the supernatant of human melanoma cells revealed 12 peptides capable of priming healthy-donor CD8+ T cells that recognize and kill human melanoma cells in vitro and when xenotransplanted in vivo. Hence, we identified a class of shared tumor antigens that are generated in ER-stressed cells, such as tumor cells, that do not induce tolerance and are not presented by healthy cells
Cellular automaton supercolliders
Gliders in one-dimensional cellular automata are compact groups of
non-quiescent and non-ether patterns (ether represents a periodic background)
translating along automaton lattice. They are cellular-automaton analogous of
localizations or quasi-local collective excitations travelling in a spatially
extended non-linear medium. They can be considered as binary strings or symbols
travelling along a one-dimensional ring, interacting with each other and
changing their states, or symbolic values, as a result of interactions. We
analyse what types of interaction occur between gliders travelling on a
cellular automaton `cyclotron' and build a catalog of the most common
reactions. We demonstrate that collisions between gliders emulate the basic
types of interaction that occur between localizations in non-linear media:
fusion, elastic collision, and soliton-like collision. Computational outcomes
of a swarm of gliders circling on a one-dimensional torus are analysed via
implementation of cyclic tag systems
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