Complexity-calibrated benchmarks for machine learning reveal when prediction algorithms succeed and mislead.

Recurrent neural networks are used to forecast time series in finance, climate, language, and from many other domains. Reservoir computers are a particularly easily trainable form of recurrent neural network. Recently, a "next-generation" reservoir computer was introduced in which the memory trace involves only a finite number of previous symbols.

From reinforcement learning to agency: Frameworks for understanding basal cognition.

Organisms play, explore, and mimic those around them. Is there a purpose to this behavior? Are organisms just behaving, or are they trying to achieve goals? We believe this is a false dichotomy. To that end, to understand organisms, we attempt to unify two approaches for understanding complex agents, whether evolved or engineered.

Prediction in cultured cortical neural networks.

Theory suggest that networks of neurons may predict their input. Prediction may underlie most aspects of information processing and is believed to be involved in motor and cognitive control and decision-making. Retinal cells have been shown to be capable of predicting visual stimuli, and there is some evidence for prediction of input in the visual cortex and hippocampus.

How Well Can We Infer Selection Benefits and Mutation Rates from Allele Frequencies?

Experimentalists observe allele frequency distributions and try to infer mutation rates and selection coefficients. How easy is this? We calculate limits to their ability in the context of the Wright-Fisher model by first finding the maximal amount of information that can be acquired using allele frequencies about the mutation rate and selection coefficient- at least 2 bits per allele- and then by finding how the organisms would have shaped their mutation rates and selection coefficients so as to maximize the information transfer.

Inference, Prediction, & Entropy-Rate Estimation of Continuous-Time, Discrete-Event Processes.

Inferring models, predicting the future, and estimating the entropy rate of discrete-time, discrete-event processes is well-worn ground. However, a much broader class of discrete-event processes operates in continuous-time. Here, we provide new methods for inferring, predicting, and estimating them.

Reconstituting and Characterizing Actin-Microtubule Composites with Tunable Motor-Driven Dynamics and Mechanics.

The composite cytoskeleton, comprising interacting networks of semiflexible actin filaments and rigid microtubules, restructures and generates forces using motor proteins such as myosin II and kinesin to drive key processes such as migration, cytokinesis, adhesion, and mechanosensing. While actin-microtubule interactions are key to the cytoskeleton's versatility and adaptability, an understanding of their interplay with myosin and kinesin activity is still nascent. This work describes how to engineer tunable three-dimensional composite networks of co-entangled actin filaments and microtubules that undergo active restructuring and ballistic motion, driven by myosin II and kinesin motors, and are tuned by the relative concentrations of actin, microtubules, motor proteins, and passive crosslinkers.

Maximum entropy models provide functional connectivity estimates in neural networks.

Tools to estimate brain connectivity offer the potential to enhance our understanding of brain functioning. The behavior of neuronal networks, including functional connectivity and induced connectivity changes by external stimuli, can be studied using models of cultured neurons. Cultured neurons tend to be active in groups, and pairs of neurons are said to be functionally connected when their firing patterns show significant synchronicity.

Motor-Driven Restructuring of Cytoskeleton Composites Leads to Tunable Time-Varying Elasticity.

The composite cytoskeleton, comprising interacting networks of semiflexible actin and rigid microtubules, generates forces and restructures by using motor proteins such as myosins to enable key processes including cell motility and mitosis. Yet, how motor-driven activity alters the mechanics of cytoskeleton composites remains an open challenge. Here, we perform optical tweezers microrheology and confocal imaging of composites with varying actin-tubulin molar percentages (25-75, 50-50, and 75-25), driven by light-activated myosin II motors, to show that motor activity increases the elastic plateau modulus by over 2 orders of magnitude by active restructuring of both actin and microtubules that persists for hours after motor activation has ceased.

Probabilistic Deterministic Finite Automata and Recurrent Networks, Revisited.

Reservoir computers (RCs) and recurrent neural networks (RNNs) can mimic any finite-state automaton in theory, and some workers demonstrated that this can hold in practice. We test the capability of generalized linear models, RCs, and Long Short-Term Memory (LSTM) RNN architectures to predict the stochastic processes generated by a large suite of probabilistic deterministic finite-state automata (PDFA) in the small-data limit according to two metrics: predictive accuracy and distance to a predictive rate-distortion curve. The latter provides a sense of whether or not the RNN is a lossy predictive feature extractor in the information-theoretic sense.

A Proposed Probabilistic Method for Distinguishing Between Delusions and Other Environmental Judgements, With Applications to Psychotherapy.

How can individuals with schizophrenia best be equipped to distinguish delusions from accurate judgements about their environment? This study presents an approach based on the principles of Bayesian probability and presents the results of a series of tests in which a simulated observer classifies randomly generated data characteristic of a simulated environment. The complexity of the data ranges from scalars to vectors of variable lengths, and the simulated observer makes its decisions based on either perfect or imperfect models of its environment. We find that when a low-dimensional observation is considered characteristic of both real observations and delusions, the prior probabilities of any observation being real or fake are of greater importance to the final decision than the attributes of the observation.

Neurophysiological coordination of duet singing.

Coordination of behavior for cooperative performances often relies on linkages mediated by sensory cues exchanged between participants. How neurophysiological responses to sensory information affect motor programs to coordinate behavior between individuals is not known. We investigated how plain-tailed wrens () use acoustic feedback to coordinate extraordinary duet performances in which females and males rapidly take turns singing.

Configurable lipid membrane gradients quantify diffusion, phase separations and binding densities.

Single-experiment analysis of phospholipid compositional gradients reveals diffusion coefficients, phase separation parameters, and binding densities as a function of localized lipid mixture. Compositional gradients are formed by directed self assembly where rapid-prototyping techniques (i.e.