Biocomplexity: adaptive behavior in complex stochastic dynamical systems

Freeman, Walter J.; Kozma, Róbert; Werbos, Paul J.

doi:10.1016/s0303-2647(00)00146-5

Cited by 133 publications

(77 citation statements)

References 59 publications

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“…Usually, the interactions among meso-sources are modelled with some form of dynamic causal model Horwitz et al, 1999). Moreover, we may want to consider random fluctuations in the (hidden) meso-dynamics (of coupled meso-sources) (Freeman et al, 2001). In this situation, we have to infer the mesostates, given our observations and our prior beliefs about their dynamical behaviour.…”

Section: Mesostate Dynamicsmentioning

confidence: 99%

A mesostate-space model for EEG and MEG

Daunizeau

Friston

2007

NeuroImage

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We present a multi-scale generative model for EEG, that entails a minimum number of assumptions about evoked brain responses, namely: (1) bioelectric activity is generated by a set of distributed sources, (2) the dynamics of these sources can be modelled as random fluctuations about a small number of mesostates, (3) mesostates evolve in a temporal structured way and are functionally connected (i.e. influence each other), and (4) the number of mesostates engaged by a cognitive task is small (e.g. between one and a few).A Variational Bayesian learning scheme is described that furnishes the posterior density on the models parameters and its evidence. Since the number of meso-sources specifies the model, the model evidence can be used to compare models and find the optimum number of meso-sources.In addition to estimating the dynamics at each cortical dipole, the mesostate-space model and its inversion provide a description of brain activity at the level of the mesostates (i.e. in terms of the dynamics of meso-sources that are distributed over dipoles). The inclusion of a mesostate level allows one to compute posterior probability maps of each dipole being active (i.e. belonging to an active mesostate). Critically, this model accommodates constraints on the number of meso-sources, while retaining the flexibility of distributed source models in explaining data. In short, it bridges the gap between standard distributed and equivalent current dipole models. Furthermore, because it is explicitly spatiotemporal, the model can embed any stochastic dynamical causal model (e.g. a neural mass model) as a Markov process prior on the mesostate dynamics.The approach is evaluated and compared to standard inverse EEG techniques, using synthetic data and real data. The results demonstrate the added-value of the mesostate-space model and its variational inversion.

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Section: Mesostate Dynamicsmentioning

confidence: 99%

A mesostate-space model for EEG and MEG

Daunizeau

Friston

2007

NeuroImage

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“…A vibrational sum of all contributing environmental influences transmitted into the consciousness (including quantum data) may be a possible mode of operation , this would process multiple sources of input which would then be integrated together [215] and co-ordinated in photon pulses "spin energies can accommodate the interactions between protons, electrons, and photons and the action potentials associated with intention, consciousness, and entanglement" [216]. A type of cellular vibration/solitonic effect was believed to be induced during the interaction of magnetic fields with cellular structures, [217]- [219]. It may be possible but not known if this is involved in emotional contagion , such data transfers could combine optical, biomagnetic, soliton/vibrational and quantum channels to form a stream of consciousness for decision making.…”

Section: Other Factorsmentioning

confidence: 99%

“…While previously cited experimental work can be interpreted as circumstantially supporting a hypothesis of emotion affecting bio energetic emissions [219], understanding is needed of what a mechanism would need to do to function so that all or most people in the vicinity interpret emissions in a similar fashion. Subjects of experiments should possess sufficient similarities in both their physiologies and unconscious minds responses in order to provide unambiguous results when exposed to similar external stimuli, although this will pose difficulties in evaluating similarities in both.…”

Section: Experimental Problems With Crowd Behaviourmentioning

confidence: 99%

The Sixth Sense-Emotional Contagion; Review of Biophysical Mechanisms Influencing Information Transfer in Groups

McDonnell¹

2014

JBBS

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Rioting historically has been known to cause changes in individual cognition, with heightened emotionality, increased excitement and reduced reflective thought. A cross-disciplinary literature review hypothesises emotional states generate corresponding metabolic bio magnetic fields that radiate in patterns reflecting these emotional states. The oscillatory phase of these waves is advanced as being more significant than the power of signal, permitting stochastic effects to magnify signals by appropriating ambient noise. Possible transmission and detection structures in the body are discussed, induced paramagnetic sensitivity in crowd participants in a phase transition process is hypothesised, the effect may be proportional to crowd numbers. It is suspected that positive effects seen in Transcranial Magnetic Therapy used to treat depression may be operating on this mechanism, acting on natural receptors in the limbic system which also capture light and are implicated in mood transitions. A number of paramagnetic neurotransmitters may be implicated.

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“…13B), so that impulse driving tends transiently to increase the gain. However, cortex maintains itself at or near a state of criticality (Bressler & Kelso, 2001;Freeman, 2008;Freeman et al, 2001;Kozma & Freeman, 2008;Kozma et al, 2005) by the saturation modeled with the point attractor that is symbolized by the pole at the origin of the complex plane (∆ in Fig. 11).…”

Section: Role Of Inhibition In Generating Oscillationsmentioning

confidence: 99%

“…The activity is seen in the continuous irregular firing of action potentials at the microscopic level, in fluctuating dendritic potentials at the mesoscopic level, and in spatiotemporal fluctuations in blood flow at the macroscopic level, which indirectly reflect the insatiable demands of brains for energy. It is commonly regarded as chaos, but it is not deterministic chaos (Rapp, 1993), because those systems are stationary, autonomous and noise-free, whereas brains are noisy, time-varying, open systems generating stochastic chaos (Freeman, 2000;Freeman, Kozma, & Werbos, 2001;Kozma, Puljic, Balister, Bollabás, & Freeman, 2005). It is also regarded as noise and removed by time-locked averaging to extract event-related potentials, on the assumption that the stimulus-or response-related activity is superimposed on the background activity.…”

Section: Introductionmentioning

confidence: 99%

Vortices in brain activity: Their mechanism and significance for perception

Freeman

2009

Neural Networks

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Brains interface with the world through perception. The process extracts information from microscopic sensory inputs and incorporates it into the mesoscopic memory store for retrieval in recognition. The process requires creation of spatiotemporal patterns of neural activity. The construction is done through phase transitions in cortical populations that condense the background activity through spontaneous symmetry breaking. Large-scale interactions create fields of synaptically driven activity that is observed by measuring brain waves (electrocorticogram, ECoG) and evaluated by constructing a mesoscopic vectorial order parameter as follows. The negative feedback among excitatory and inhibitory neurons creates spatially and spectrally distributed gamma oscillations (20-80 Hz) in the background activity. Band pass filtering reveals beats in ECoG log analytic power. In some beats that recur at theta rates (3-7 Hz), the order parameter transiently approaches zero, giving a null spike in which the microscopic activity is uniformly disordered (symmetric). A phase transition that is manifested in an analytic phase discontinuity breaks the symmetry. As the null spike terminates, the resurgent order parameter imposes mesoscopic order seen in spatial patterns of ECoG amplitude modulation (AM) that actualize and update the memory of a stimulus. Read-out is through a divergent/convergent projection that performs on cortical output an irreversible spatiotemporal integral transformation. The ECoG reveals a conic phase gradient that accompanies an AM pattern. The phase cone manifests a vortex, which is initiated by the null spike, and which is inferred to help stabilize and prolong its accompanying AM pattern that might otherwise be rapidly degraded by the turbulent neural noise from which it emerges.

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Biocomplexity: adaptive behavior in complex stochastic dynamical systems

Cited by 133 publications

References 59 publications

A mesostate-space model for EEG and MEG

A mesostate-space model for EEG and MEG

The Sixth Sense-Emotional Contagion; Review of Biophysical Mechanisms Influencing Information Transfer in Groups

Vortices in brain activity: Their mechanism and significance for perception

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