Revealing networks from dynamics: an introduction

What can we learn from the collective dynamics of a complex network about its interaction topology? Taking the perspective from nonlinear dynamics, we briefly review recent progress on how to infer structural connectivity (direct interactions) from accessing the dynamics of the units. Potential applications range from interaction networks in physics, to chemical and metabolic reactions, protein and gene regulatory networks as well as neural circuits in biology and electric power grids or wireless sensor networks in engineering. Moreover, we briefly mention some standard ways of inferring effective or functional connectivity.Comment: Topical review, 48 pages, 7 figure

The kynurenine pathway as a therapeutic target in cognitive and neurodegenerative disorders

Understanding the neurochemical basis for cognitive function is one of the major goals of neuroscience, with a potential impact on the diagnosis, prevention and treatment of a range of psychiatric and neurological disorders. In this review, the focus will be on a biochemical pathway that remains under-recognised in its implications for brain function, even though it can be responsible for moderating the activity of two neurotransmitters fundamentally involved in cognition – glutamate and acetylcholine. Since this pathway – the kynurenine pathway of tryptophan metabolism - is induced by immunological activation and stress it also stands in an unique position to mediate the effects of environmental factors on cognition and behaviour. Targetting the pathway for new drug development could, therefore, be of value not only for the treatment of existing psychiatric conditions, but also for preventing the development of cognitive disorders in response to environmental pressures

Neuronal assembly dynamics in supervised and unsupervised learning scenarios

The dynamic formation of groups of neurons—neuronal assemblies—is believed to mediate cognitive phenomena at many levels, but their detailed operation and mechanisms of interaction are still to be uncovered. One hypothesis suggests that synchronized oscillations underpin their formation and functioning, with a focus on the temporal structure of neuronal signals. In this context, we investigate neuronal assembly dynamics in two complementary scenarios: the first, a supervised spike pattern classification task, in which noisy variations of a collection of spikes have to be correctly labeled; the second, an unsupervised, minimally cognitive evolutionary robotics tasks, in which an evolved agent has to cope with multiple, possibly conflicting, objectives. In both cases, the more traditional dynamical analysis of the system’s variables is paired with information-theoretic techniques in order to get a broader picture of the ongoing interactions with and within the network. The neural network model is inspired by the Kuramoto model of coupled phase oscillators and allows one to fine-tune the network synchronization dynamics and assembly configuration. The experiments explore the computational power, redundancy, and generalization capability of neuronal circuits, demonstrating that performance depends nonlinearly on the number of assemblies and neurons in the network and showing that the framework can be exploited to generate minimally cognitive behaviors, with dynamic assembly formation accounting for varying degrees of stimuli modulation of the sensorimotor interactions

Benchmarking Cerebellar Control

Cerebellar models have long been advocated as viable models for robot dynamics control. Building on an increasing insight in and knowledge of the biological cerebellum, many models have been greatly refined, of which some computational models have emerged with useful properties with respect to robot dynamics control. Looking at the application side, however, there is a totally different picture. Not only is there not one robot on the market which uses anything remotely connected with cerebellar control, but even in research labs most testbeds for cerebellar models are restricted to toy problems. Such applications hardly ever exceed the complexity of a 2 DoF simulated robot arm; a task which is hardly representative for the field of robotics, or relates to realistic applications. In order to bring the amalgamation of the two fields forwards, we advocate the use of a set of robotics benchmarks, on which existing and new computational cerebellar models can be comparatively tested. It is clear that the traditional approach to solve robotics dynamics loses ground with the advancing complexity of robotic structures; there is a desire for adaptive methods which can compete as traditional control methods do for traditional robots. In this paper we try to lay down the successes and problems in the fields of cerebellar modelling as well as robot dynamics control. By analyzing the common ground, a set of benchmarks is suggested which may serve as typical robot applications for cerebellar models

Improvements in optical techniques to investigate the behavior and neuronal network dynamics over long timescales

Developments in optical technology have produced an important shift in experimental neuroscience from electrophysiological methods for observation and stimulation to all-optical solutions. One expects this trend to continue as future developments continue to deliver, and improve upon, the original promises of the technology: 1) minimally invasive actuation and recording of neurons, and 2) a drastic increase in targets that can be treated simultaneously. Moreover, as the high costs of the technology are reduced, one may expect its larger-scale adoption in the neuroscience community. In this thesis, I describe the development and implementation of two alloptical solutions for the analysis of behavior, neuronal signaling, and stimulation, which improve on previous state-of-the-art: (1) A minimally-invasive, high signal-to-noise twophoton microscopy setup capable of simultaneous, live-imaging of a large subset of sensory neurons post activation, and (2) a low-cost tracking solution to stimulate and record behavior. I begin this thesis with a review of recent advances in optical neuroscience techniques for the study of neuronal networks with the focus on work done in Caenorhabditis elegans. Then, in chapter 2, I describe my implementation of a two-photon temporal focusing microscopy setup and show significant improvements through the use of a high power/ high pulse repetition rate excitation system, enabling live imaging with high resolution for extended periods of time. I model temperature increase during a physiological imaging scenario for different repetition rates at fixed peak intensities and find range centered around 1 MHz to be optimal. Lastly, I describe the low-cost tracking setup with the ability to stimulate and record behavior over the course of hours. The setup is capable of two-color stimulation of optogenetic proteins over the area of the behavioral arena in combination with volatile chemicals. To showcase the utility of the system, I demonstrate behavioral analysis of integration of contradictory cues. In summary, I present a set of techniques for the interrogation of neural networks from animal behavior to neuronal activity, over timescales of potentially hours and days. These techniques can be used to address a new dimension of scientific questions.Okinawa Institute of Science and Technology Graduate Universit

A Fokker-Planck formalism for diffusion with finite increments and absorbing boundaries

Gaussian white noise is frequently used to model fluctuations in physical systems. In Fokker-Planck theory, this leads to a vanishing probability density near the absorbing boundary of threshold models. Here we derive the boundary condition for the stationary density of a first-order stochastic differential equation for additive finite-grained Poisson noise and show that the response properties of threshold units are qualitatively altered. Applied to the integrate-and-fire neuron model, the response turns out to be instantaneous rather than exhibiting low-pass characteristics, highly non-linear, and asymmetric for excitation and inhibition. The novel mechanism is exhibited on the network level and is a generic property of pulse-coupled systems of threshold units.Comment: Consists of two parts: main article (3 figures) plus supplementary text (3 extra figures

Functional identification of biological neural networks using reservoir adaptation for point processes

The complexity of biological neural networks does not allow to directly relate their biophysical properties to the dynamics of their electrical activity. We present a reservoir computing approach for functionally identifying a biological neural network, i.e. for building an artificial system that is functionally equivalent to the reference biological network. Employing feed-forward and recurrent networks with fading memory, i.e. reservoirs, we propose a point process based learning algorithm to train the internal parameters of the reservoir and the connectivity between the reservoir and the memoryless readout neurons. Specifically, the model is an Echo State Network (ESN) with leaky integrator neurons, whose individual leakage time constants are also adapted. The proposed ESN algorithm learns a predictive model of stimulus-response relations in in vitro and simulated networks, i.e. it models their response dynamics. Receiver Operating Characteristic (ROC) curve analysis indicates that these ESNs can imitate the response signal of a reference biological network. Reservoir adaptation improved the performance of an ESN over readout-only training methods in many cases. This also held for adaptive feed-forward reservoirs, which had no recurrent dynamics. We demonstrate the predictive power of these ESNs on various tasks with cultured and simulated biological neural networks