Recurrent Infomax Generates Cell Assemblies, Neuronal Avalanches, and Simple Cell-Like Selectivity

Tanaka, Takuma; Kaneko, Takeshi; Aoyagi, Toshio

doi:10.1162/neco.2008.03-08-727

Cited by 55 publications

(75 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Analytical and numerical studies predict that critical systems have maximized dynamic range (Kinouchi and Copelli, 2006), information transmission (Beggs and Plenz, 2003; Tanaka et al, 2009), and entropy of events and states (Haldeman and Beggs, 2005; Ramo et al, 2007). By experimentally manipulating the balance of excitation and inhibition, neuronal avalanche dynamics has been shown to maximize the dynamic range (Shew et al, 2009), pattern variability (Stewart and Plenz, 2006), and information capacity and input-output mutual information (Shew et al, 2011), and to represent the most diverse state of intermittent phase locking between distant cortical sites (Yang et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

Neuronal Avalanches in the Resting MEG of the Human Brain

Shriki¹,

Alstott

Carver³

et al. 2013

J. Neurosci.

325

394

View full text Add to dashboard Cite

What constitutes normal cortical dynamics in healthy human subjects is a major question in systems neuroscience. Numerous in vitro and in vivo animal studies have shown that ongoing or resting cortical dynamics are characterized by cascades of activity across many spatial scales, termed neuronal avalanches. In experiment and theory, avalanche dynamics are identified by two measures: (1) a power law in the size distribution of activity cascades with an exponent of −3/2 and (2) a branching parameter of the critical value of 1, reflecting balanced propagation of activity at the border of premature termination and potential blowup. Here we analyzed resting-state brain activity recorded using noninvasive magnetoencephalography (MEG) from 124 healthy human subjects and two different MEG facilities using different sensor technologies. We identified large deflections at single MEG sensors and combined them into spatiotemporal cascades on the sensor array using multiple timescales. Cascade size distributions obeyed power laws. For the timescale at which the branching parameter was close to 1, the power law exponent was −3/2. This relationship was robust to scaling and coarse graining of the sensor array. It was absent in phase-shuffled controls with the same power spectrum or empty scanner data. Our results demonstrate that normal cortical activity in healthy human subjects at rest organizes as neuronal avalanches and is well described by a critical branching process. Theory and experiment have shown that such critical, scale-free dynamics optimize information processing. Therefore, our findings imply that the human brain attains an optimal dynamical regime for information processing.

show abstract

Section: Discussionmentioning

confidence: 99%

Neuronal Avalanches in the Resting MEG of the Human Brain

Shriki¹,

Alstott

Carver³

et al. 2013

J. Neurosci.

325

394

View full text Add to dashboard Cite

show abstract

“…Importantly, avalanche size, s, distributes according to a power law, P(s) ϳ s ␣ with exponent ␣ close to Ϫ1.5, a hallmark of critical state dynamics (Plenz and Thiagarajan, 2007;Klaus et al, 2011). Both theoretical (Kinouchi and Copelli, 2006;Rämö et al, 2007;Tanaka et al, 2009) and empirical studies (Shew et al, 2009(Shew et al, , 2011 suggest that avalanches optimize various aspects of information processing in cortical networks.…”

Section: Higher-order Interactions Are Essential For Neuronal Avalancmentioning

confidence: 99%

Higher-Order Interactions Characterized in Cortical Activity

Yu¹,

Yang²,

Nakahara³

et al. 2011

J. Neurosci.

201

267

View full text Add to dashboard Cite

In the cortex, the interactions among neurons give rise to transient coherent activity patterns that underlie perception, cognition, and action. Recently, it was actively debated whether the most basic interactions, i.e., the pairwise correlations between neurons or groups of neurons, suffice to explain those observed activity patterns. So far, the evidence reported is controversial. Importantly, the overall organization of neuronal interactions and the mechanisms underlying their generation, especially those of high-order interactions, have remained elusive. Here we show that higher-order interactions are required to properly account for cortical dynamics such as ongoing neuronal avalanches in the alert monkey and evoked visual responses in the anesthetized cat. A Gaussian interaction model that utilizes the observed pairwise correlations and event rates and that applies intrinsic thresholding identifies those higher-order interactions correctly, both in cortical local field potentials and spiking activities. This allows for accurate prediction of large neuronal population activities as required, e.g., in brain-machine interface paradigms. Our results demonstrate that higher-order interactions are inherent properties of cortical dynamics and suggest a simple solution to overcome the apparent formidable complexity previously thought to be intrinsic to those interactions.

show abstract

“…Scale-free dynamics and other features predicted at criticality were previously shown to emerge during adaptation, but were not present during the intense transient response following stimulus onset [16]. Importantly, cortical slice experiments [20] and theory [21–23] suggest that when a network operates near criticality, it is optimal for stimulus discrimination, although these studies did not address adaptation. Similarly, Fisher information is predicted to peak at criticality [24].…”

Section: Introductionmentioning

confidence: 99%

Adaptation towards scale-free dynamics improves cortical stimulus discrimination at the cost of reduced detection

Clawson

Weßel

et al. 2017

PLoS Comput Biol

View full text Add to dashboard Cite

Fundamental to the function of nervous systems is the ability to reorganize to cope with changing sensory input. Although well-studied in single neurons, how such adaptive versatility manifests in the collective population dynamics and function of cerebral cortex remains unknown. Here we measured population neural activity with microelectrode arrays in turtle visual cortex while visually stimulating the retina. First, we found that, following the onset of stimulation, adaptation tunes the collective population dynamics towards a special regime with scale-free spatiotemporal activity, after an initial large-scale transient response. Concurrently, we observed an adaptive tradeoff between two important aspects of population coding–sensory detection and discrimination. As adaptation tuned the cortex toward scale-free dynamics, stimulus discrimination was enhanced, while stimulus detection was reduced. Finally, we used a network-level computational model to show that short-term synaptic depression was sufficient to mechanistically explain our experimental results. In the model, scale-free dynamics emerge only when the model operates near a special regime called criticality. Together our model and experimental results suggest unanticipated functional benefits and costs of adaptation near criticality in visual cortex.

show abstract

Recurrent Infomax Generates Cell Assemblies, Neuronal Avalanches, and Simple Cell-Like Selectivity

Cited by 55 publications

References 33 publications

Neuronal Avalanches in the Resting MEG of the Human Brain

Neuronal Avalanches in the Resting MEG of the Human Brain

Higher-Order Interactions Characterized in Cortical Activity

Adaptation towards scale-free dynamics improves cortical stimulus discrimination at the cost of reduced detection

Contact Info

Product

Resources

About