What Is Stochastic Resonance? Definitions, Misconceptions, Debates, and Its Relevance to Biology

Stochastic resonance is said to be observed when increases in levels of unpredictable fluctuations—e.g., random noise—cause an increase in a metric of the quality of signal transmission or detection performance, rather than a decrease. This counterintuitive effect relies on system nonlinearities and on some parameter ranges being “suboptimal”. Stochastic resonance has been observed, quantified, and described in a plethora of physical and biological systems, including neurons. Being a topic of widespread multidisciplinary interest, the definition of stochastic resonance has evolved significantly over the last decade or so, leading to a number of debates, misunderstandings, and controversies. Perhaps the most important debate is whether the brain has evolved to utilize random noise in vivo, as part of the “neural code”. Surprisingly, this debate has been for the most part ignored by neuroscientists, despite much indirect evidence of a positive role for noise in the brain. We explore some of the reasons for this and argue why it would be more surprising if the brain did not exploit randomness provided by noise—via stochastic resonance or otherwise—than if it did. We also challenge neuroscientists and biologists, both computational and experimental, to embrace a very broad definition of stochastic resonance in terms of signal-processing “noise benefits”, and to devise experiments aimed at verifying that random variability can play a functional role in the brain, nervous system, or other areas of biology

Critical and resonance phenomena in neural networks

Brain rhythms contribute to every aspect of brain function. Here, we study critical and resonance phenomena that precede the emergence of brain rhythms. Using an analytical approach and simulations of a cortical circuit model of neural networks with stochastic neurons in the presence of noise, we show that spontaneous appearance of network oscillations occurs as a dynamical (non-equilibrium) phase transition at a critical point determined by the noise level, network structure, the balance between excitatory and inhibitory neurons, and other parameters. We find that the relaxation time of neural activity to a steady state, response to periodic stimuli at the frequency of the oscillations, amplitude of damped oscillations, and stochastic fluctuations of neural activity are dramatically increased when approaching the critical point of the transition.Comment: 8 pages, Proceedings of 12th Granada Seminar, September 17-21, 201

Creativity and the Brain

Neurocognitive approach to higher cognitive functions that bridges the gap between psychological and neural level of description is introduced. Relevant facts about the brain, working memory and representation of symbols in the brain are summarized. Putative brain processes responsible for problem solving, intuition, skill learning and automatization are described. The role of non-dominant brain hemisphere in solving problems requiring insight is conjectured. Two factors seem to be essential for creativity: imagination constrained by experience, and filtering that selects most interesting solutions. Experiments with paired words association are analyzed in details and evidence for stochastic resonance effects is found. Brain activity in the process of invention of novel words is proposed as the simplest way to understand creativity using experimental and computational means. Perspectives on computational models of creativity are discussed

Noise-induced synchronization and anti-resonance in excitable systems; Implications for information processing in Parkinson's Disease and Deep Brain Stimulation

We study the statistical physics of a surprising phenomenon arising in large networks of excitable elements in response to noise: while at low noise, solutions remain in the vicinity of the resting state and large-noise solutions show asynchronous activity, the network displays orderly, perfectly synchronized periodic responses at intermediate level of noise. We show that this phenomenon is fundamentally stochastic and collective in nature. Indeed, for noise and coupling within specific ranges, an asymmetry in the transition rates between a resting and an excited regime progressively builds up, leading to an increase in the fraction of excited neurons eventually triggering a chain reaction associated with a macroscopic synchronized excursion and a collective return to rest where this process starts afresh, thus yielding the observed periodic synchronized oscillations. We further uncover a novel anti-resonance phenomenon: noise-induced synchronized oscillations disappear when the system is driven by periodic stimulation with frequency within a specific range. In that anti-resonance regime, the system is optimal for measures of information capacity. This observation provides a new hypothesis accounting for the efficiency of Deep Brain Stimulation therapies in Parkinson's disease, a neurodegenerative disease characterized by an increased synchronization of brain motor circuits. We further discuss the universality of these phenomena in the class of stochastic networks of excitable elements with confining coupling, and illustrate this universality by analyzing various classical models of neuronal networks. Altogether, these results uncover some universal mechanisms supporting a regularizing impact of noise in excitable systems, reveal a novel anti-resonance phenomenon in these systems, and propose a new hypothesis for the efficiency of high-frequency stimulation in Parkinson's disease

Noise-enhanced computation in a model of a cortical column

Varied sensory systems use noise in order to enhance detection of weak signals. It has been conjectured in the literature that this effect, known as stochastic resonance, may take place in central cognitive processes such as the memory retrieval of arithmetical multiplication. We show in a simplified model of cortical tissue, that complex arithmetical calculations can be carried out and are enhanced in the presence of a stochastic background. The performance is shown to be positively correlated to the susceptibility of the network, defined as its sensitivity to a variation of the mean of its inputs. For nontrivial arithmetic tasks such as multiplication, stochastic resonance is an emergent property of the microcircuitry of the model network

Transient Information Flow in a Network of Excitatory and Inhibitory Model Neurons: Role of Noise and Signal Autocorrelation

We investigate the performance of sparsely-connected networks of integrate-and-fire neurons for ultra-short term information processing. We exploit the fact that the population activity of networks with balanced excitation and inhibition can switch from an oscillatory firing regime to a state of asynchronous irregular firing or quiescence depending on the rate of external background spikes. We find that in terms of information buffering the network performs best for a moderate, non-zero, amount of noise. Analogous to the phenomenon of stochastic resonance the performance decreases for higher and lower noise levels. The optimal amount of noise corresponds to the transition zone between a quiescent state and a regime of stochastic dynamics. This provides a potential explanation on the role of non-oscillatory population activity in a simplified model of cortical micro-circuits.Comment: 27 pages, 7 figures, to appear in J. Physiology (Paris) Vol. 9

Self-organized criticality and stochastic resonance in the human brain

The human brain spontaneously generates neuronal network oscillations at around 10 and 20 Hz with a large variability in amplitude, duration, and recurrence. Despite more than 70 years of research, the complex dynamics and functional significance of these oscillations have remained poorly understood. This Thesis concerns the dynamic character and functional significance of noninvasively recorded 10- and 20-Hz oscillations in the human brain. The hypotheses, experimental paradigms, data analyses, and interpretations of the results are inspired by recent insights from physics - most notable the theory of self-organized criticality and the phenomenon of stochastic resonance whose applicability to large-scale neuronal networks is explained. We show that amplitude fluctuations of 10- and 20-Hz oscillations during wakeful rest are correlated over thousands of oscillation cycles and that the decay of temporal correlations exhibits power-law scaling behavior. However, when these ongoing oscillations are perturbed with sensory stimuli, the amplitude attenuates quickly, reliably, and transiently, and the long-range temporal dynamics is affected as evidenced by changes in scaling exponents compared to rest. In addition to the rich temporal dynamics in local areas of the cortex, ongoing oscillations tend to synchronize their phases and exhibit correlated amplitude fluctuations across the two hemispheres, as shown for oscillations in homologous areas of the sensorimotor cortices. Finally, it is revealed that intermediate amplitude levels of ongoing oscillations provide the optimal oscillatory state of the sensorimotor cortex for reliable and quick conscious detection of weak somatosensory stimuli. We propose that the long-range temporal correlations, the power-law scaling behavior, the high susceptibility to stimulus perturbations, and the large amplitude variability of ongoing oscillations may find a unifying explanation within the theory of self-organized criticality. This theory offers a general mechanism for the ubiquitous emergence of complex dynamics with power-law decay of spatiotemporal correlations in non-linear self-organizing stochastic systems consisting of many units. The optimal ability to detect consciously and respond behaviorally to weak somatosensory stimuli at intermediate levels of ongoing sensorimotor oscillations is attributed to stochastic resonance - the intuitively paradoxical phenomenon that the signal-to-noise ratio of detecting or transmitting a signal in a non-linear system can be enhanced by noise. Based on the above results, we conjecture that a mechanism of intrinsic stochastic resonance between self-organized critical and stimulus-induced activities may be a general organizing principle of great importance for central nervous system function and account for some of the variability in the way we perceive and react to the outside world.reviewe