Distinct ground state and activated state modes of firing in forebrain neurons

Levenstein, Daniel; Girardeau, Gabrielle; Jonathan, Gornet,; Grosmark, Andres; Huszár, Roman; Peyrache, Adrien; Senzai, Yuta; Watson, Brendon O.; Rinzel, John; Buzsáki, György

doi:10.1101/2021.09.20.461152

Cited by 6 publications

(10 citation statements)

References 69 publications

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“…Provided that we can determine the characteristic Inter-Spike Interval (ISI)s for those different states, one can attempt to interpret that the message comprises the information in which state of the available ones the sender neuron is. Given that in [ 53 ] only 4… 5 stationary states could be identified, it looks like that 3 bits (in agreement with [ 4 ], ch. 6) can describe the transmitted information (in this sense, only those stationary states).…”

Section: Information and Entropysupporting

confidence: 79%

“…That is, the same time difference value (which collects, of course, some noise) is available at the receiver. The relevance of using the time between spikes (ISI) in the experimental works [ 30 , 53 ] strongly supports the relevance of our hypothesis about the role of the temporal description. Whether to measure the time between received spikes (i.e., measuring the spike rate), the receiver neuron uses the ‘bit time’ derived from the previously received time or uses some absolute time that has remained open and is to be researched.…”

Section: Information and Entropysupporting

confidence: 77%

“…The recent investigation [ 53 ] seems to experimentally underpin our claim. In that work, stationary states are called GS and AS, and time (ISI) is used to describe spikes’ distribution.…”

Section: Evidence Underpinning the Suggested Approachmentioning

confidence: 56%

“…Assuming that the transferred information is about different states in the sender neuron enables us to explain the presence of several bits per spike. There is evidence [ 53 ] that the different spiking neurons can stably exist only in a couple of discrete states, including a ground state (GS) and a small number of active state (AS). Provided that we can determine the characteristic Inter-Spike Interval (ISI)s for those different states, one can attempt to interpret that the message comprises the information in which state of the available ones the sender neuron is.…”

Section: Information and Entropymentioning

confidence: 99%

“…In this way, we go back to Hartley’s original definition [ 29 ] and create an N element ISI histogram. Some stationary states may share their contribution between neighboring cells, and N is probably an overestimation (some histogram cells can be empty) of the number of transferred bits

If we estimate (from Figure 2 in [ 53 ])

s, we arrive at

, i.e., we can describe the transferred information by 5 (or maybe 6) bits. As discussed in [ 30 ], transferring signals with

repetition rate requires less ATP, so we can presume that, for metabolic efficiency, transferring the (GS) signal needs below-average neural bandwidth and transmission power.…”

Section: Information and Entropymentioning

confidence: 99%

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Towards Generalizing the Information Theory for Neural Communication

Végh¹,

Berki

2022

Entropy

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Neuroscience extensively uses the information theory to describe neural communication, among others, to calculate the amount of information transferred in neural communication and to attempt the cracking of its coding. There are fierce debates on how information is represented in the brain and during transmission inside the brain. The neural information theory attempts to use the assumptions of electronic communication; despite the experimental evidence that the neural spikes carry information on non-discrete states, they have shallow communication speed, and the spikes’ timing precision matters. Furthermore, in biology, the communication channel is active, which enforces an additional power bandwidth limitation to the neural information transfer. The paper revises the notions needed to describe information transfer in technical and biological communication systems. It argues that biology uses Shannon’s idea outside of its range of validity and introduces an adequate interpretation of information. In addition, the presented time-aware approach to the information theory reveals pieces of evidence for the role of processes (as opposed to states) in neural operations. The generalized information theory describes both kinds of communication, and the classic theory is the particular case of the generalized theory.

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Section: Information and Entropysupporting

confidence: 79%

Section: Information and Entropysupporting

confidence: 77%

Section: Evidence Underpinning the Suggested Approachmentioning

confidence: 56%

Section: Information and Entropymentioning

confidence: 99%

If we estimate (from Figure 2 in [ 53 ])

s, we arrive at

, i.e., we can describe the transferred information by 5 (or maybe 6) bits. As discussed in [ 30 ], transferring signals with

repetition rate requires less ATP, so we can presume that, for metabolic efficiency, transferring the (GS) signal needs below-average neural bandwidth and transmission power.…”

Section: Information and Entropymentioning

confidence: 99%

See 3 more Smart Citations

Towards Generalizing the Information Theory for Neural Communication

Végh¹,

Berki

2022

Entropy

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Metabolically regulated spiking could serve neuronal energy homeostasis and protect from reactive oxygen species

Chintaluri,

Vogels

2023

Proc. Natl. Acad. Sci. U.S.A.

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So-called spontaneous activity is a central hallmark of most nervous systems. Such non-causal firing is contrary to the tenet of spikes as a means of communication, and its purpose remains unclear. We propose that self-initiated firing can serve as a release valve to protect neurons from the toxic conditions arising in mitochondria from lower-than-baseline energy consumption. To demonstrate the viability of our hypothesis, we built a set of models that incorporate recent experimental results indicating homeostatic control of metabolic products—Adenosine triphosphate (ATP), adenosine diphosphate (ADP), and reactive oxygen species (ROS)—by changes in firing. We explore the relationship of metabolic cost of spiking with its effect on the temporal patterning of spikes and reproduce experimentally observed changes in intrinsic firing in the fruitfly dorsal fan-shaped body neuron in a model with ROS-modulated potassium channels. We also show that metabolic spiking homeostasis can produce indefinitely sustained avalanche dynamics in cortical circuits. Our theory can account for key features of neuronal activity observed in many studies ranging from ion channel function all the way to resting state dynamics. We finish with a set of experimental predictions that would confirm an integrated, crucial role for metabolically regulated spiking and firmly link metabolic homeostasis and neuronal function.

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Hyperpolarization-Activated Currents Drive Neuronal Activation Sequences in Sleep

Mehrotra,

Levenstein,

Duszkiewicz

et al. 2023

Preprint

Self Cite

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Sequential neuronal patterns are believed to support information processing in the cortex, yet their origin is still a matter of debate. We report that neuronal activity in the mouse head-direction cortex (HDC, i.e., the post-subiculum) was sequentially activated along the dorso-ventral axis during sleep at the transition from hyperpolarized “DOWN” to activated “UP” states, while representing a stable direction. Computational modelling suggested that these dynamics could be attributed to a spatial gradient of hyperpolarization-activated current (Ih), which we confirmed inex vivoslice experiments and corroborated in other cortical structures. These findings open up the possibility that varying amounts of Ihacross cortical neurons could result in sequential neuronal patterns, and that travelling activity upstream of the entorhinal-hippocampal circuit organises large-scale neuronal activity supporting learning and memory during sleep.HighlightsNeuronal Activation Sequence in HDC: neuronal activity was sequentially reinstated along the dorsoventral axis of the HDC at UP state but not DOWN state onset.Role of Ihin Sequence Generation: Incorporating the hyperpolarization-activated current (Ih) into computational models, we identified its pivotal role in UP/DOWN dynamics and neuronal activity sequences.Ex VivoVerification: slice physiology revealed a dorsoventral gradient of Ih in the HDC.Implications Beyond HDC: the gradient of Ihcould account for the sequential organization of neuronal activity across various cortical areas.

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Distinct ground state and activated state modes of firing in forebrain neurons

Cited by 6 publications

References 69 publications

Towards Generalizing the Information Theory for Neural Communication

Towards Generalizing the Information Theory for Neural Communication

Metabolically regulated spiking could serve neuronal energy homeostasis and protect from reactive oxygen species

Hyperpolarization-Activated Currents Drive Neuronal Activation Sequences in Sleep

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