Random dispersion in excitatory synapse response

Ventriglia, Francesco

doi:10.1007/s11571-014-9285-1

Cited by 7 publications

(4 citation statements)

References 41 publications

(43 reference statements)

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“…Key findings of the INT are: (I) their topographical organization during both resting and task states along uni- and transmodal regions/networks; (II) their topographical carry-over and partial change during the transition from rest to task 22 ; and (III) their relation to the temporal structure of external inputs during task states 1 , 26 , 49 , 50 , 70 , 71 . Together, these findings suggest their involvement specifically in the brain’s input processing.…”

Section: Part Ii: Input Processing Through Intrinsic Neural Timescalesmentioning

confidence: 99%

The brain and its time: intrinsic neural timescales are key for input processing

et al. 2021

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We process and integrate multiple timescales into one meaningful whole. Recent evidence suggests that the brain displays a complex multiscale temporal organization. Different regions exhibit different timescales as described by the concept of intrinsic neural timescales (INT); however, their function and neural mechanisms remains unclear. We review recent literature on INT and propose that they are key for input processing. Specifically, they are shared across different species, i.e., input sharing. This suggests a role of INT in encoding inputs through matching the inputs’ stochastics with the ongoing temporal statistics of the brain’s neural activity, i.e., input encoding. Following simulation and empirical data, we point out input integration versus segregation and input sampling as key temporal mechanisms of input processing. This deeply grounds the brain within its environmental and evolutionary context. It carries major implications in understanding mental features and psychiatric disorders, as well as going beyond the brain in integrating timescales into artificial intelligence.

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Section: Part Ii: Input Processing Through Intrinsic Neural Timescalesmentioning

confidence: 99%

The brain and its time: intrinsic neural timescales are key for input processing

et al. 2021

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“…In line with this observation, it has been suggested that conscious awareness necessarily demands mental content being held ''fixed'', ''frozen'' within a discrete but continuous progressive present moment that stands for a phenomenal unity (Fingelkurts and Fingelkurts 2014). This occurs because spatial relationships, encoded in sensory stimulus, are translated into temporal rhythms, an orthogonal organization, by the place cells of the hippocampus (Ventriglia 2014). The brain's gradually improving responses to stimuli molded brain organization into the structural, organizational mirror of physical systems.…”

Section: Introductionmentioning

confidence: 97%

Relationships between short and fast brain timescales

2017

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Brain electric activity exhibits two important features: oscillations with different timescales, characterized by diverse functional and psychological outcomes, and a temporal power law distribution. In order to further investigate the relationships between low- and high- frequency spikes in the brain, we used a variant of the Borsuk-Ulam theorem which states that, when we assess the nervous activity as embedded in a sphere equipped with a fractal dimension, we achieve two antipodal points with similar features (the slow and fast, scale-free oscillations). We demonstrate that slow and fast nervous oscillations mirror each other over time via a sinusoid relationship and provide, through the Bloch theorem from solid-state physics, the possible equation which links the two timescale activities. We show that, based on topological findings, nervous activities occurring in micro-levels are projected to single activities at meso- and macro-levels. This means that brain functions assessed at the higher scale of the whole brain necessarily display a counterpart in the lower ones, and vice versa. Our topological approach makes it possible to assess brain functions both based on entropy, and in the general terms of particle trajectories taking place on donut-like manifolds. Condensed brain activities might give rise to ideas and concepts by combination of different functional and anatomical levels. Furthermore, cognitive phenomena, as well as social activity can be described by the laws of quantum mechanics; memories and decisions exhibit holographic organization. In physics, the term duality refers to a case where two seemingly different systems turn out to be equivalent. This topological duality holds for all the types of spatio-temporal brain activities, independent of their inter- and intra-level relationships, strength, magnitude and boundaries, allowing us to connect the physiological manifestations of consciousness to the electric activities of the brain.

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“…It has recently been shown that near orthogonality of large dimensional random vectors can produce holographic projection [97] . Sensory perception translates spatial relationships into temporal rhythms, an orthogonal organization, by the hippocampus's place cells [169] . The holographic projection integrates temporally distant identities in perception and decision-making to form and orient the self [34] , [35] , [37] .…”

Section: Discussionmentioning

confidence: 99%

The thermodynamics of cognition: A mathematical treatment

Deli¹,

Peters

Kisvárday

2021

Computational and Structural Biotechnology Journal

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Graphical abstract The thermodynamics consequences of intelligent computation. The neural system uses substantial resources to maintain the resting state; therefore, the Carnot cycle can model the evoked cycle. The synaptic complexity during neural computation changes in parallel with the ability to respond to future challenges. Positive emotions (top) trigger the reversed Carnot cycle, which accumulates energy and entropy (the entropy at the end of the cycle is A′ where S (A′) > S (A)). Negative emotions initiate an exothermic Carnot cycle, which accumulates information and wastes energy (entropy at the end of the cycle is A′ and S (A′) < S (A)).

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Random dispersion in excitatory synapse response

Cited by 7 publications

References 41 publications

The brain and its time: intrinsic neural timescales are key for input processing

The brain and its time: intrinsic neural timescales are key for input processing

Relationships between short and fast brain timescales

The thermodynamics of cognition: A mathematical treatment

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