The distribution of collective firing of neurons , known as a neural avalanche, obeys a power law. Three proposed explanations of this emergent scale-free behavior are criticality, neutral theory, and URLanized criticality. We show that the neutral theory of neural avalanches can be unified with criticality, which requires fine tuning of control parameters, and rule out URLanized criticality .  We study a model of the brain for which the dynamics are governed by neutral theory. We identify the tuning parameters , which are consistent with experiments , and show that scale-free neural avalanches occur only at the critical point . The scaling hypothesis provides a unified explanation of the power laws which characterize the critical point. The critical exponents characterizing the avalanche distributions and divergence of the response functions are shown to be consistent with the predictions of the scaling hypothesis. We use an universal scaling function for the avalanche profile to find that the firing rate for avalanches of different sizes shows data collapse after appropriate rescaling.  Critical slowing-down and algebraic relaxation of avalanches demonstrate that the dynamics are also consistent with the system being at a critical point. We discuss how our results can motivate future empirical studies of criticality in the brain.
Neural avalanches are collective firings of neurons that exhibit emergent scale-free behavior . Understanding the nature and distribution of these avalanches is an important element in understanding how the brain functions. We study a model of the brain for which the dynamics are governed by neutral theory. The neural avalanches are defined using causal connections between the firing neurons. We analyze the scaling of causal neural avalanches as the critical point is approached from the absorbing phase. By using cluster analysis tools from percolation theory, we characterize the critical properties of the neural avalanches. We identify the tuning parameters consistent with experiments . The scaling hypothesis provides a unified explanation of the power laws which characterize the critical point. The critical exponents characterizing the avalanche distributions and divergence of the response functions are consistent with the predictions of the scaling hypothesis. We use an universal scaling function for the avalanche profile to find that the firing rate for avalanches of different sizes shows data collapse after appropriate rescaling. We also find data collapse for the avalanche distribution functions, which is a stronger evidence of criticality than just the existence of power laws. Critical slowing-down and power law relaxation of avalanches is observed as the system is tuned to its critical point. We discuss how our results motivate future empirical studies of criticality in the brain.
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The distribution of collective firing of neurons , known as a neural avalanche, obeys a power law. Three proposed explanations of this
Neural avalanches are collective firings of neurons that exhibit
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are criticality, neutral theory, and URLanized criticality. We show that the neutral theory of neural avalanches can be unified with criticality, which requires fine tuning of control parameters, and rule out URLanized criticality
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Understanding the nature and distribution of these avalanches is an important element in understanding how the brain functions.
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The neural avalanches are defined using causal connections between the firing neurons. We analyze the scaling of causal neural avalanches as the critical point is approached from the absorbing phase. By using cluster analysis tools from percolation theory, we characterize the critical properties of the neural avalanches. We
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, and show that scale-free neural avalanches occur only at the critical point
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We also find data collapse for the avalanche distribution functions, which is a stronger evidence of criticality than just the existence of power laws.
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