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Pregnancy Timeline by SemestersDevelopmental TimelineFertilizationFirst TrimesterSecond TrimesterThird TrimesterFirst Thin Layer of Skin AppearsEnd of Embryonic PeriodEnd of Embryonic PeriodFemale Reproductive SystemBeginning Cerebral HemispheresA Four Chambered HeartFirst Detectable Brain WavesThe Appearance of SomitesBasic Brain Structure in PlaceHeartbeat can be detectedHeartbeat can be detectedFinger and toe prints appearFinger and toe prints appearFetal sexual organs visibleBrown fat surrounds lymphatic systemBone marrow starts making blood cellsBone marrow starts making blood cellsInner Ear Bones HardenSensory brain waves begin to activateSensory brain waves begin to activateFetal liver is producing blood cellsBrain convolutions beginBrain convolutions beginImmune system beginningWhite fat begins to be madeHead may position into pelvisWhite fat begins to be madePeriod of rapid brain growthFull TermHead may position into pelvisImmune system beginningLungs begin to produce surfactant
CLICK ON weeks 0 - 40 and follow along every 2 weeks of fetal development




 

How cells divide tasks and conquer work

Despite advances in neuroscience, the brain is still very much a black box. No one even knows how many different types of neurons exist! Now, a Salk Institute scientist uses a mathematical viewpoint to better understand how cell types divide up the work of the brain.


The theory, which is described in the journal Neuron on June 7, 2017, could help reveal how cell types achieve greater efficiency and reliability — or how disease results when the division of labor is not effective.

"Understanding how different cell types work together is a big unknown in biology," says Tatyana Sharpee, an associate professor in Salk's Computational Neurobiology Laboratory and holder of the Helen McLoraine Developmental Chair. "For example, in the brain we do not yet know the number of different cell types — with ongoing debates on what even constitutes a cell type. Having a theoretical framework such as this one can focus experimental efforts in understanding biological complexity."

In the 1950s, information theory was developed to study how to send messages in the most cost-effective manner while minimizing errors. This theory is also relevant for how neurons in the brain communicate with each other.

Sharpee, who uses information theory to figure out fundamental laws governing biological complexity, believes it can help predict how many different cell types in a system can work together.
"Differentiation and pluripotency are well-studied processes. This paper actually links the two. Before, we didn't know these pathways were actively talking to each other. It was pretty surprising."

Tatyana Sharpee PhD, Associate Professor and holder of the Helen McLoraine Developmental Chair, Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, California, USA.

Sharpee and colleagues published their initial idea in 2015 in Proceedings of the National Academy of Sciences, to explain why neurons in the salamander retina, sensitive to dimming lights, split into two sub-types. Whereas, comparable neurons sensitive to increases in light — do not.
It turns out that neurons sensitive to light dimming are more stable than neurons sensitive to light's increasing.

The increased stability of dark-sensitive neurons means they are able to represent signals of different strengths individually. But, neurons fluctuating as light increases, must work together, in effect averaging their response.

In the new paper, Sharpee further describes how these arguments can be generalized to help us understand how different proteins — such as ion channels that help produce signals in the brain in the first place — divide their input range to achieve greater efficiency. Based on information theory, such arguments can be applied to systems outside of neuroscience equally as well.
"The theory that we tested in the retina can be relevant for understanding complexity in many systems. If you have noisy input-output elements, it's better to average the output. If the elements are slightly more stable, the design can be more specific."

Tatyana Sharpee PhD

Sharpee is working with a number of groups to test and broaden the range of applications, such as inflammation, mood disorders, metabolism and cancers.

Abstract: Neuron 2017
Discretization in neural circuits occurs on many levels, from the generation of action potentials and dendritic integration, to neuropeptide signaling and processing of signals from multiple neurons, to behavioral decisions. It is clear that discretization, when implemented properly, can convey many benefits. However, the optimal solutions depend on both the level of noise and how it impacts a particular computation. This Perspective discusses how current physiological data could potentially be integrated into one theoretical framework based on maximizing information. Key experiments for testing that framework are discussed.

Keywords: information theory, dendrites, neuromodulation, neural cell types, neuropeptide, ion channels, ionic currents, power law, scale-free dynamics, phase transitions

Abstract: PNAS 2015
Significance
It is unknown what functional properties influence the number of cell types in the brain. Here we show how one can use a powerful framework from physics that describes the transitions between different phases of matter, such as between liquid and gas, to specify under what conditions it becomes optimal to split neural populations into new subtypes to maximize information transmission. These results outline a conceptual framework that spans both physical and biological systems and can be used to explain the emergence of different functional classes of neuronal types.

Abstract
Computation in the brain involves multiple types of neurons, yet the organizing principles for how these neurons work together remain unclear. Information theory has offered explanations for how different types of neurons can maximize the transmitted information by encoding different stimulus features. However, recent experiments indicate that separate neuronal types exist that encode the same filtered version of the stimulus, but then the different cell types signal the presence of that stimulus feature with different thresholds. Here we show that the emergence of these neuronal types can be quantitatively described by the theory of transitions between different phases of matter. The two key parameters that control the separation of neurons into subclasses are the mean and standard deviation (SD) of noise affecting neural responses. The average noise across the neural population plays the role of temperature in the classic theory of phase transitions, whereas the SD is equivalent to pressure or magnetic field, in the case of liquid–gas and magnetic transitions, respectively. Our results account for properties of two recently discovered types of salamander Off retinal ganglion cells, as well as the absence of multiple types of On cells. We further show that, across visual stimulus contrasts, retinal circuits continued to operate near the critical point whose quantitative characteristics matched those expected near a liquid–gas critical point and described by the nearest-neighbor Ising model in three dimensions. By operating near a critical point, neural circuits can maximize information transmission in a given environment while retaining the ability to quickly adapt to a new environment. .


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Jun 9, 2017   Fetal Timeline   Maternal Timeline   News   News Archive


How to calculate an immune response

Dartmouth College uses computational analysis to define immune system response.
Image Credit: Frederick S. Varn



Phospholid by Wikipedia >
History

Bibliography

Pregnancy Timeline

Prescription Drug Effects on Pregnancy

Pregnancy Calculator

Female Reproductive System

News
Disclaimer: The Visible Embryo web site is provided for your general information only. The information contained on this site should not be treated as a substitute for medical, legal or other professional advice. Neither is The Visible Embryo responsible or liable for the contents of any websites of third parties which are listed on this site.


Content protected under a Creative Commons License.
No dirivative works may be made or used for commercial purposes.

 

Pregnancy Timeline by SemestersDevelopmental TimelineFertilizationFirst TrimesterSecond TrimesterThird TrimesterFirst Thin Layer of Skin AppearsEnd of Embryonic PeriodEnd of Embryonic PeriodFemale Reproductive SystemBeginning Cerebral HemispheresA Four Chambered HeartFirst Detectable Brain WavesThe Appearance of SomitesBasic Brain Structure in PlaceHeartbeat can be detectedHeartbeat can be detectedFinger and toe prints appearFinger and toe prints appearFetal sexual organs visibleBrown fat surrounds lymphatic systemBone marrow starts making blood cellsBone marrow starts making blood cellsInner Ear Bones HardenSensory brain waves begin to activateSensory brain waves begin to activateFetal liver is producing blood cellsBrain convolutions beginBrain convolutions beginImmune system beginningWhite fat begins to be madeHead may position into pelvisWhite fat begins to be madePeriod of rapid brain growthFull TermHead may position into pelvisImmune system beginningLungs begin to produce surfactant
CLICK ON weeks 0 - 40 and follow along every 2 weeks of fetal development




 

How cells divide tasks and conquer work

Despite advances in neuroscience, the brain is still very much a black box. No one even knows how many different types of neurons exist! Now, a Salk Institute scientist uses a mathematical viewpoint to better understand how cell types divide up the work of the brain.


The theory, which is described in the journal Neuron on June 7, 2017, could help reveal how cell types achieve greater efficiency and reliability — or how disease results when the division of labor is not effective.

"Understanding how different cell types work together is a big unknown in biology," says Tatyana Sharpee, an associate professor in Salk's Computational Neurobiology Laboratory and holder of the Helen McLoraine Developmental Chair. "For example, in the brain we do not yet know the number of different cell types — with ongoing debates on what even constitutes a cell type. Having a theoretical framework such as this one can focus experimental efforts in understanding biological complexity."

In the 1950s, information theory was developed to study how to send messages in the most cost-effective manner while minimizing errors. This theory is also relevant for how neurons in the brain communicate with each other.

Sharpee, who uses information theory to figure out fundamental laws governing biological complexity, believes it can help predict how many different cell types in a system can work together.
"Differentiation and pluripotency are well-studied processes. This paper actually links the two. Before, we didn't know these pathways were actively talking to each other. It was pretty surprising."

Tatyana Sharpee PhD, Associate Professor and holder of the Helen McLoraine Developmental Chair, Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, California, USA.

Sharpee and colleagues published their initial idea in 2015 in Proceedings of the National Academy of Sciences, to explain why neurons in the salamander retina, sensitive to dimming lights, split into two sub-types. Whereas, comparable neurons sensitive to increases in light — do not.
It turns out that neurons sensitive to light dimming are more stable than neurons sensitive to light's increasing.

The increased stability of dark-sensitive neurons means they are able to represent signals of different strengths individually. But, neurons fluctuating as light increases, must work together, in effect averaging their response.

In the new paper, Sharpee further describes how these arguments can be generalized to help us understand how different proteins — such as ion channels that help produce signals in the brain in the first place — divide their input range to achieve greater efficiency. Based on information theory, such arguments can be applied to systems outside of neuroscience equally as well.
"The theory that we tested in the retina can be relevant for understanding complexity in many systems. If you have noisy input-output elements, it's better to average the output. If the elements are slightly more stable, the design can be more specific."

Tatyana Sharpee PhD

Sharpee is working with a number of groups to test and broaden the range of applications, such as inflammation, mood disorders, metabolism and cancers.

Abstract: Neuron 2017
Discretization in neural circuits occurs on many levels, from the generation of action potentials and dendritic integration, to neuropeptide signaling and processing of signals from multiple neurons, to behavioral decisions. It is clear that discretization, when implemented properly, can convey many benefits. However, the optimal solutions depend on both the level of noise and how it impacts a particular computation. This Perspective discusses how current physiological data could potentially be integrated into one theoretical framework based on maximizing information. Key experiments for testing that framework are discussed.

Keywords: information theory, dendrites, neuromodulation, neural cell types, neuropeptide, ion channels, ionic currents, power law, scale-free dynamics, phase transitions

Abstract: PNAS 2015
Significance
It is unknown what functional properties influence the number of cell types in the brain. Here we show how one can use a powerful framework from physics that describes the transitions between different phases of matter, such as between liquid and gas, to specify under what conditions it becomes optimal to split neural populations into new subtypes to maximize information transmission. These results outline a conceptual framework that spans both physical and biological systems and can be used to explain the emergence of different functional classes of neuronal types.

Abstract
Computation in the brain involves multiple types of neurons, yet the organizing principles for how these neurons work together remain unclear. Information theory has offered explanations for how different types of neurons can maximize the transmitted information by encoding different stimulus features. However, recent experiments indicate that separate neuronal types exist that encode the same filtered version of the stimulus, but then the different cell types signal the presence of that stimulus feature with different thresholds. Here we show that the emergence of these neuronal types can be quantitatively described by the theory of transitions between different phases of matter. The two key parameters that control the separation of neurons into subclasses are the mean and standard deviation (SD) of noise affecting neural responses. The average noise across the neural population plays the role of temperature in the classic theory of phase transitions, whereas the SD is equivalent to pressure or magnetic field, in the case of liquid–gas and magnetic transitions, respectively. Our results account for properties of two recently discovered types of salamander Off retinal ganglion cells, as well as the absence of multiple types of On cells. We further show that, across visual stimulus contrasts, retinal circuits continued to operate near the critical point whose quantitative characteristics matched those expected near a liquid–gas critical point and described by the nearest-neighbor Ising model in three dimensions. By operating near a critical point, neural circuits can maximize information transmission in a given environment while retaining the ability to quickly adapt to a new environment. .


Return to top of page

Jun 9, 2017   Fetal Timeline   Maternal Timeline   News   News Archive


How to calculate an immune response

Dartmouth College uses computational analysis to define immune system response.
Image Credit: Frederick S. Varn



Phospholid by Wikipedia