Oscillations, metastability and phase transitions in brain and models of cognition

Neuroscience is being practiced in many different forms and at many different organizational levels of the Nervous System. Which of these levels and associated conceptual frameworks is most informative for elucidating the association of neural processes with processes of Cognition is an empirical question and subject to pragmatic validation. In this essay, I select the framework of Dynamic System Theory. Several investigators have applied in recent years tools and concepts of this theory to interpretation of observational data, and for designing neuronal models of cognitive functions. I will first trace the essentials of conceptual development and hypotheses separately for discerning observational tests and criteria for functional realism and conceptual plausibility of the alternatives they offer. I will then show that the statistical mechanics of phase transitions in brain activity, and some of its models, provides a new and possibly revealing perspective on brain events in cognition

Metastability, Criticality and Phase Transitions in brain and its Models

This essay extends the previously deposited paper "Oscillations, Metastability and Phase Transitions" to incorporate the theory of Self-organizing Criticality. The twin concepts of Scaling and Universality of the theory of nonequilibrium phase transitions is applied to the role of reentrant activity in neural circuits of cerebral cortex and subcortical neural structures

Push recovery with stepping strategy based on time-projection control

In this paper, we present a simple control framework for on-line push recovery with dynamic stepping properties. Due to relatively heavy legs in our robot, we need to take swing dynamics into account and thus use a linear model called 3LP which is composed of three pendulums to simulate swing and torso dynamics. Based on 3LP equations, we formulate discrete LQR controllers and use a particular time-projection method to adjust the next footstep location on-line during the motion continuously. This adjustment, which is found based on both pelvis and swing foot tracking errors, naturally takes the swing dynamics into account. Suggested adjustments are added to the Cartesian 3LP gaits and converted to joint-space trajectories through inverse kinematics. Fixed and adaptive foot lift strategies also ensure enough ground clearance in perturbed walking conditions. The proposed structure is robust, yet uses very simple state estimation and basic position tracking. We rely on the physical series elastic actuators to absorb impacts while introducing simple laws to compensate their tracking bias. Extensive experiments demonstrate the functionality of different control blocks and prove the effectiveness of time-projection in extreme push recovery scenarios. We also show self-produced and emergent walking gaits when the robot is subject to continuous dragging forces. These gaits feature dynamic walking robustness due to relatively soft springs in the ankles and avoiding any Zero Moment Point (ZMP) control in our proposed architecture.Comment: 20 pages journal pape

A roadmap to integrate astrocytes into Systems Neuroscience.

Systems neuroscience is still mainly a neuronal field, despite the plethora of evidence supporting the fact that astrocytes modulate local neural circuits, networks, and complex behaviors. In this article, we sought to identify which types of studies are necessary to establish whether astrocytes, beyond their well-documented homeostatic and metabolic functions, perform computations implementing mathematical algorithms that sub-serve coding and higher-brain functions. First, we reviewed Systems-like studies that include astrocytes in order to identify computational operations that these cells may perform, using Ca2+ transients as their encoding language. The analysis suggests that astrocytes may carry out canonical computations in a time scale of subseconds to seconds in sensory processing, neuromodulation, brain state, memory formation, fear, and complex homeostatic reflexes. Next, we propose a list of actions to gain insight into the outstanding question of which variables are encoded by such computations. The application of statistical analyses based on machine learning, such as dimensionality reduction and decoding in the context of complex behaviors, combined with connectomics of astrocyte-neuronal circuits, is, in our view, fundamental undertakings. We also discuss technical and analytical approaches to study neuronal and astrocytic populations simultaneously, and the inclusion of astrocytes in advanced modeling of neural circuits, as well as in theories currently under exploration such as predictive coding and energy-efficient coding. Clarifying the relationship between astrocytic Ca2+ and brain coding may represent a leap forward toward novel approaches in the study of astrocytes in health and disease

LEGaTO: first steps towards energy-efficient toolset for heterogeneous computing

LEGaTO is a three-year EU H2020 project which started in December 2017. The LEGaTO project will leverage task-based programming models to provide a software ecosystem for Made-in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC.Peer ReviewedPostprint (author's final draft

Course grained low power design flow using UPF

Increased system complexity has led to the substitution of the traditional bottom-up design flow by systematic hierarchical design flow. The main motivation behind the evolution of such an approach is the increasing difficulty in hardware realization of complex systems. With decreasing channel lengths, few key problems such as timing closure, design sign-off, routing complexity, signal integrity, and power dissipation arise in the design flows. Specifically, minimizing power dissipation is critical in several high-end processors. In high-end processors, the design complexity contributes to the overall dynamic power while the decreasing transistor size results in static power dissipation. This research aims at optimizing the design flow for power and timing using the unified power format (UPF). UPF provides a strategic format to specify power-aware design information at every stage in the flow. The low power reduction techniques enforced in this research are multi-voltage, multi-threshold voltage (Vth), and power gating with state retention. An inherent design challenge addressed in this research is the choice of power optimization techniques as the flow advances from synthesis to physical design. A top-down digital design flow for a 32 bit MIPS RISC processor has been implemented with and without UPF synthesis flow for 65nm technology. The UPF synthesis is implemented with two voltages, 1.08V and 0.864V (Multi-VDD). Area, power and timing metrics are analyzed for the flows developed. Power savings of about 20 % are achieved in the design flow with \u27multi-threshold\u27 power technique compared to that of the design flow with no low power techniques employed. Similarly, 30 % power savings are achieved in the design flow with the UPF implemented when compared to that of the design flow with \u27multi-threshold\u27 power technique employed. Thus, a cumulative power savings of 42% has been achieved in a complete power efficient design flow (UPF) compared to that of the generic top-down standard flow with no power saving techniques employed. This is substantiated by the low voltage operation of modules in the design, reduction in clock switching power by gating clocks in the design and extensive use of HVT and LVT standard cells for implementation. The UPF synthesis flow saw the worst timing slack and more area when compared to those of the `multi-threshold\u27 or the generic flow. Percentage increase in the area with UPF is approximately 15%; a significant source for this increase being the additional power controlling logic added