Solving difficult problems creatively: a role for energy optimised deterministic/stochastic hybrid computing

How is the brain configured for creativity? What is the computational substrate for ‘eureka’ moments of insight? Here we argue that creative thinking arises ultimately from a synergy between low-energy stochastic and energy-intensive deterministic processing, and is a by-product of a nervous system whose signal-processing capability per unit of available energy has become highly energy optimised. We suggest that the stochastic component has its origin in thermal (ultimately quantum decoherent) noise affecting the activity of neurons. Without this component, deterministic computational models of the brain are incomplete.

[1]  F. Wolf,et al.  Dynamical entropy production in spiking neuron networks in the balanced state. , 2010, Physical review letters.

[2]  A. Aldo Faisal,et al.  Stochastic Simulations on the Reliability of Action Potential Propagation in Thin Axons , 2007, PLoS Comput. Biol..

[3]  K. Palem,et al.  Inexactness and a future of computing , 2014, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[4]  Cristian S. Calude,et al.  Experimental Evidence of Quantum Randomness Incomputability , 2010, ArXiv.

[5]  S. Laughlin,et al.  An Energy Budget for Signaling in the Grey Matter of the Brain , 2001, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[6]  N. McGlynn.  Thinking fast and slow. , 2014, Australian veterinary journal.

[7]  H. Sompolinsky,et al.  Chaos in Neuronal Networks with Balanced Excitatory and Inhibitory Activity , 1996, Science.

[8]  Krishna V. Palem,et al.  Ultra-Efficient (Embedded) SOC Architectures based on Probabilistic CMOS (PCMOS) Technology , 2006, Proceedings of the Design Automation & Test in Europe Conference.

[9]  Biswa Sengupta,et al.  The effect of cell size and channel density on neuronal information encoding and energy efficiency , 2013, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[10]  L. Pinneo.  On noise in the nervous system. , 1966, Psychological review.

[11]  Carson C. Chow,et al.  Spontaneous action potentials due to channel fluctuations. , 1996, Biophysical journal.

[12]  Krishna V. Palem,et al.  Computational Proof as Experiment: Probabilistic Algorithms from a Thermodynamic Perspective , 2003, Verification: Theory and Practice.

[13]  Mark D. McDonnell,et al.  The benefits of noise in neural systems: bridging theory and experiment , 2011, Nature Reviews Neuroscience.

[14]  Jeremy E. Niven,et al.  Miniaturization of Nervous Systems and Neurons , 2012, Current Biology.

[15]  Tim Palmer.  Towards the probabilistic Earth‐system simulator: a vision for the future of climate and weather prediction , 2012 .

[16]  B. Baars,et al.  Consciousness, biology and quantum hypotheses. , 2012, Physics of life reviews.

[17]  Marian Stamp Dawkins,et al.  The Noisy Brain: Stochastic Dynamics as a Principle of Brain Function The Noisy Brain: Stochastic Dynamics as a Principle of Brain Function. By Edmund T. Rolls & Gustavo Deco. Oxford: Oxford University Press (2010). Pp. 310. Price £37.95 hardback. , 2010, Animal Behaviour.

[18]  T. Palmer.  Extended-range atmospheric prediction and the Lorenz model , 1993 .

[19]  J. White,et al.  Channel noise in neurons , 2000, Trends in Neurosciences.

[20]  A. M. Turing,et al.  Computing Machinery and Intelligence , 1950, The Philosophy of Artificial Intelligence.

[21]  Rob R. de Ruyter van Steveninck,et al.  The metabolic cost of neural information , 1998, Nature Neuroscience.

[22]  L. Chittka,et al.  Travel Optimization by Foraging Bumblebees through Readjustments of Traplines after Discovery of New Feeding Locations , 2010, The American Naturalist.

[23]  Kurt Wiesenfeld,et al.  Stochastic resonance and the benefits of noise: from ice ages to crayfish and SQUIDs , 1995, Nature.

[24]  S. Laughlin,et al.  Ion-Channel Noise Places Limits on the Miniaturization of the Brain’s Wiring , 2005, Current Biology.

[25]  R. Penrose,et al.  Shadows of the Mind , 1994 .

[26]  Tim N. Palmer,et al.  Modelling: Build imprecise supercomputers , 2015, Nature.

[27]  A. M. Turing,et al.  Computing Machinery and Intelligence , 1950, The Philosophy of Artificial Intelligence.

[28]  Bart Selman,et al.  Boosting Combinatorial Search Through Randomization , 1998, AAAI/IAAI.