Self-organized criticality as a fundamental property of neural systems. The neural criticality hypothesis states that the brain may be poised in a critical state at a boundary between different types of dynamics. Evidence for criticality has been found in cell cultures, brain slices, and anesthetized animals.‎Introduction · ‎Experimental Evidence · ‎Models of Self-Organized · ‎Discussion. Criticality in Neural Systems (Annual Reviews of Nonlinear Dynamics and Complexity (VCH)) [Dietmar Plenz, Ernst Niebur, Heinz Georg Schuster] on. Condensed Matter > Disordered Systems and Neural Networks theoretical models for criticality and avalanche dynamics in neural networks.


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For example, the observation of resonance in electrophysiological recordings can predict oscillations on the network level. By contrast, top-down approaches start by considering the properties of the brain on the level of brain areas or the whole brain, and infer downwards to the properties of its constituents.

Hypotheses on the microscopic level are formed based of the macroscopic criticality in neural systems. For example, correlated activity in Criticality in neural systems recordings predicts a connection between the underlying brain areas.

A central concept connecting the microscopic and macroscopic levels is criticality. In the investigation of neural criticality, the word critical criticality in neural systems used in the sense of statistical physics, which is distinct from other meanings, including the colloquial use.

Self-organized criticality as a fundamental property of neural systems

In statistical physics, criticality is defined as a specific type of behavior observed when a system undergoes a phase transition. Physics characterizes the behavior of systems into qualitatively different phases.


This classification scheme has its origin in criticality in neural systems phases of classical matter, i. The different macroscopic properties of, say, ice, liquid water, and steam can be explained by the microscopic forces between single water molecules.

As a substantial change in the synaptic conductances is only observed after several spikes, the plasticity acts on a slower time-scale than the neuronal activity.

Self-organized criticality as a fundamental property of neural systems

This provides the time-scale separation required for a robust tuning of the system to critical states. A change in the synaptic conductances could directly influence the excitability of the synapse basically the parameter p in criticality in neural systems simplified modelwhich is sufficient to tune the system to criticality.

A change of individual synaptic weights, which translates into an overall change in excitability, is only the simplest possible criticality in neural systems.

The excitability can also be changed directly by mechanisms of homeostatic plasticity Stewart and Plenz, ; Droste et al.

[] Self-organized criticality in neural network models

Other possible targets criticality in neural systems sophisticated self-organizing plasticity mechanisms are changes in the level of micro-scale modularity, or of the heterogeneity in the system. These factors, which we have ignored so far, affect the location of critical points and can thus be used to tune the system to criticality.

The simple picture, in which exactly one global control parameter is tuned, is thus misleading. In reality, the microscopic changes in the system are likely to affect tens or hundreds of network level quantities at criticality in neural systems same time, which all act as possible control parameters for phase transitions.

Another open question is to which critical state the network organizes.

While we have so far focused on the phase transition at the onset of activity, some evidence suggests the onset of criticality in neural systems as a more likely candidate. Some insights into this question can be gained based on the relation between the nature of the transition at which criticality in neural systems system resides and efficient coding of information.

For the activity transition considered so far, the optimal computational properties are likely to be realized if the information is presented in a rate code, where the activity of a node represents directly an input. To achieve optimal information representation for a synchronization code, where an input is represented by synchronous activity, the system needs to be tuned to the phase transition at the onset of synchronization.

In a system with many parameters, the term critical point is misleading.

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