Biology & Computation

There is growing evidence that several fundamental processes in biology are inherently computational and are best viewed from the standpoint of computation. Indeed, proteins, cells, the brain, swarms of ants/birds, and even evolution can be viewed as running computational processes (algorithms) on inter-connected agents which are often selfish and strategic. Once a process is formulated in this manner, we can leverage the insights obtained from decades of Computer Science research in order to obtain fundamental insight into these areas. Such a computational viewpoint also facilitates developing an understanding of data and the dynamics that govern its generation; ultimately resulting in improved computational methods.

Computation, Nature and Society Think Tank

A creative space where computer scientists, biologists and social scientists work together towrds a sustainable synthesis of modern society. This Think Tank is anchored in the academic setting of Ecole Polytechnique Federal de Lausanne (EPFL), Switzerland. We meet regularly in an open minded and interdisciplinary setting to discuss fundamental problems at the interface of computation, biology and society. We are open to new members — so please do get in touch with us!

Think Tank Web Page: cns.epfl.ch

Evolution and Computation

Evolution can be viewed as a form of computation. This viewpoint allows us to study questions related to the nature of the steady state of evolution, its robustness and efficiency using tools and techniques from computer science. This allows us to further our understanding of evolutionary mechanisms that enable organisms fast adaptations and have generated the diversity of life we see today.

References:

Ioannis Panageas, Piyush Srivastava, Nisheeth K. Vishnoi, 2016., Evolutionary dynamics on finite populations mix rapidly. (PDF)

Nisheeth K. Vishnoi, 2015., The speed of evolution. (PDF)

Nisheeth K. Vishnoi , 2013. Making evolution rigorous- the error threshold. (PDF)

Narendra M. Dixit, Piyush Srivastava, Nisheeth K. Vishnoi, 2012., A finite population model of molecular evolution: Theory and computation. (PDF)

Kushal Tripathi, Rajesh Balagam, Nisheeth K. Vishnoi, Narendra M. Dixit, 2012., Stochastic Simulations suggest that HIV-1 survives close to its error threshold. (PDF)

Research lab: prof. Nisheeth Vishnoi, Theory of Computation Laboratory 3

Bio-inspired Algortihms

Simple life forms implicitly employ algorithms in their quest for survival. These processes lead to algorithmic insights for fundamental problems in computer science and optimization. Consider the case of the slime mold Physarum polycephalum, a single celled organism that has been a source of much excitement among biologists and computer scientists due to its ability to solve complex optimization problems. The process that this slime mold uses to forage for food has led us to devise novel algorithms for fundamental graph problems, linear programming and finding sparse solutions to underdetermined linear systems of equations.

References:

Damian Straszak, Nisheeth K. Vishnoi, 2016., Natural Algorithms for Flow Problems (PDF)

Damian Straszak, Nisheeth K. Vishnoi, 2016., On a Natural Dynamics for Linear Programming (PDF)
Damian Straszak, Nisheeth K. Vishnoi, 2016., IRLS and Slime Mold: Equivalence and Convergence (PDF)

Research lab: prof. Nisheeth Vishnoi, Theory of Computation Laboratory 3

Bio-inspired Neuron Algortihms

Bio-inspired and Neuromorphic Circuits

The operation of the central nervous system offers a very contrastive example to classical processor architectures’. Consuming approximately 20W, the brain accomplishes task that are yet accepted very difficult to classical processors, such as complex and multimodal pattern memorization and classification, complex decision making and consciousness. Classical processors architectures appear to lend themselves to solving simple arithmetic or logic sequential tasks, in an aggressively repetitive approach. Novel bio-inspired computational methods may take benefit of the silicon media implementation to carry out complex tasks in a power-efficient manner. The intrinsic characteristics of current or future devices may be used, circuits and systems developed, and finally processor architectures may be optimized to support bio-inspired algorithms.

On the other side, electronic systems have taken benefit of the stable CMOS fabrication technology, as well as suitable processor architectures to expand into ubiquitous industrial, medical and consumer products. In spite of an evident mature technology, the microelectronic industry must prepare to face stringent limits related to the power dissipation for a given operation, with strong impact on the circuit size, the operation speed, the global required energy, and eventually the marketability of new products.

The research and projects cover areas from the usage of new devices to emulating bio-inspired behavior, microelectronic circuits and systems realizing the hardware implementation of nature-inspired computational principles (e.g., early image processing at the sensor level, stochastic resonance, unconventional computing), architectures for artificial neural networks and deep learning, and reliability of neural operators realized in microelectronic hardware.

Research lab: dr. Alexandre Schmid, Microelectronic Systems Laboratory

 

Neural mechanisms of movement and learning for novel robots capable of agile locomotion in complex environments

We work on the computational aspects of movement control, sensorimotor coordination, and learning in animals and in robots. We are interested in using robots and numerical simulation to study the neural mechanisms underlying movement control and learning in animals, and in return to take inspiration from animals to design new control methods for robotics as well as novel robots capable of agile locomotion in complex environments.

Research Lab: prof. Auke Ijspeert, Biorobotics Laboratory