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Found 20 papers, 4 papers with code
Detecting Information Relays in Deep Neural Networks
Deep learning of artificial neural networks (ANNs) is creating highly functional processes that are, unfortunately, nearly as hard to interpret as their biological counterparts.
Emergence of functional information from multivariate correlations
The information content of symbolic sequences (such as nucleic- or amino acid sequences, but also neuronal firings or strings of letters) can be calculated from an ensemble of such sequences, but because information cannot be assigned to single sequences, we cannot correlate information to other observables attached to the sequence.
Information Fragmentation, Encryption and Information Flow in Complex Biological Networks
Assessing where and how information is stored in biological networks (such as neuronal and genetic networks) is a central task both in neuroscience and in molecular genetics, but most available tools focus on the network's structure as opposed to its function.
Can Transfer Entropy Infer Information Flow in Neuronal Circuits for Cognitive Processing?
To infer information flow in any network of agents, it is important first and foremost to establish causal temporal relations between the nodes.
Evolution leads to a diversity of motion-detection neuronal circuits
A central goal of evolutionary biology is to explain the origins and distribution of diversity across life.
The structure of evolved representations across different substrates for artificial intelligence
Artificial neural networks (ANNs), while exceptionally useful for classification, are vulnerable to misdirection.
The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities
Biological evolution provides a creative fount of complex and subtle adaptations, often surprising the scientists who discover them.
The mind as a computational system
The present document is an excerpt of an essay that I wrote as part of my application material to graduate school in Computer Science (with a focus on Artificial Intelligence), in 1986.
Markov Brains: A Technical Introduction
Markov Brains are a class of evolvable artificial neural networks (ANN).
Evolution of active categorical image classification via saccadic eye movement
Pattern recognition and classification is a central concern for modern information processing systems.
Flies as Ship Captains? Digital Evolution Unravels Selective Pressures to Avoid Collision in Drosophila
Flies that walk in a covered planar arena on straight paths avoid colliding with each other, but which of the two flies stops is not random.
Exploring the coevolution of predator and prey morphology and behavior
In this model, we observe a coevolutionary cycle between prey swarming behavior and the predator's visual system, where the predator and prey continually adapt their visual system and behavior, respectively, over evolutionary time in reaction to one another due to the well-known "predator confusion effect."
Information-theoretic considerations concerning the origin of life
Research investigating the origins of life usually focuses on exploring possible life-bearing chemistries in the pre-biotic Earth, or else on synthetic approaches.
Populations and Evolution Information Theory Information Theory Adaptation and Self-Organizing Systems Biomolecules
Exploring the evolution of a trade-off between vigilance and foraging in group-living organisms
Despite the fact that grouping behavior has been actively studied for over a century, the relative importance of the numerous proposed fitness benefits of grouping remain unclear.
More Bang For Your Buck: Quorum-Sensing Capabilities Improve the Efficacy of Suicidal Altruism
Suicidal altruists could use quorum sensing to determine when exploding is most beneficial, but it is challenging to study the selective forces at work in microbes.
Leveraging Evolutionary Search to Discover Self-Adaptive and Self-Organizing Cellular Automata
These results demonstrate that CA are a viable platform for studying the integration of self-adaptation and self-organization, and strengthen the case for using evolutionary algorithms as a component of SASO systems.
Risk aversion as an evolutionary adaptation
We hypothesize that risk aversion in the equivalent mean payoff gamble is beneficial as an adaptation to living in small groups, and find that a preference for risk averse strategies only evolves in small populations of less than 1, 000 individuals, while agents exhibit no such strategy preference in larger populations.
Evolution of swarming behavior is shaped by how predators attack
Using an evolutionary model of a predator-prey system, we show that how predators attack is critical to the evolution of the selfish herd.
Predator confusion is sufficient to evolve swarming behavior
Using an evolutionary model of a predator-prey system, we show that predator confusion provides a sufficient selection pressure to evolve swarming behavior in prey.
The evolution of representation in simple cognitive networks
Representations are internal models of the environment that can provide guidance to a behaving agent, even in the absence of sensory information.
