scholarly journals BDNF signaling in Hebbian and Stentian structural plasticity in the developing visual system

2021 ◽  
Author(s):  
Elena Kutsarova ◽  
Anne Schohl ◽  
Martin Munz ◽  
Alex Wang ◽  
Yuan Yuan Zhang ◽  
...  
Keyword(s):  
Neural Activity ◽  
Visual Stimulation ◽  
Retinal Ganglion ◽  
Topographic Map ◽  
Multiphoton Imaging ◽  
Hebbian Plasticity ◽  
Bdnf Signaling ◽  
Axon Branch ◽  
Contralateral Eye ◽  
Activity Dependent

During development, patterned neural activity in input neurons innervating their target, instructs topographic map refinement. Axons from adjacent neurons, firing with similar patterns of neural activity, converge onto target neurons and stabilize their synapses with these postsynaptic partners (Hebbian plasticity). On the other hand, non-correlated firing of inputs promotes synaptic weakening and exploratory axonal growth (Stentian plasticity). We used visual stimulation to control the visually-evoked correlation structure of neural activity in ectopic ipsilaterally projecting (ipsi) retinal ganglion cell axons with respect to their neighboring contralateral eye inputs in the optic tectum of albino Xenopus laevis tadpoles. Multiphoton imaging of the ipsi axons in the live tadpole, combined with manipulation of brain-derived neurotrophic factor (BDNF) signaling, revealed that presynaptic p75NTR and TrkB both promoted axonal branch addition during Stentian plasticity, whereas predominantly postsynaptic BDNF signaling mediated activity-dependent Hebbian suppression of axon branch addition. Additionally, we found that BDNF signaling is required for local suppression of branch loss induced by correlated firing.

Expression of the BDNF gene in the developing visual system of the chick

Development ◽  
1994 ◽  
Vol 120 (6) ◽  
pp. 1643-1649 ◽  
Author(s):  
K.H. Herzog ◽  
K. Bailey ◽  
Y.A. Barde
Keyword(s):  
Visual System ◽  
Ganglion Cells ◽  
Mrna Levels ◽  
Retinal Ganglion ◽  
Bdnf Gene ◽  
Bdnf Mrna ◽  
Optic Stalk ◽  
Copy Numbers ◽  
Activity Dependent

Using a sensitive and quantitative method, the mRNA levels of brain-derived neurotrophic factor (BDNF) were determined during the development of the chick visual system. Low copy numbers were detected, and BDNF was found to be expressed in the optic tectum already 2 days before the arrival of the first retinal ganglion cell axons, suggesting an early role of BDNF in tectal development. After the beginning of tectal innervation, BDNF mRNA levels markedly increased, and optic stalk transection at day 4 (which prevents subsequent tectal innervation) was found to reduce the contralateral tectal levels of BDNF mRNA. Comparable reductions were obtained after injection of tetrodotoxin into one eye, indicating that, already during the earliest stages of target encounter in the CNS, the degree of BDNF gene expression is influenced by activity-dependent mechanisms. BDNF mRNA was also detected in the retina itself and at levels comparable to those found in the tectum. Together with previous findings indicating that BDNF prevents the death of cultured chick retinal ganglion cells, these results support the idea that the tightly controlled expression of the BDNF gene might be important in the co-ordinated development of the visual system.

scholarly journals Hebbian activity-dependent plasticity in white matter

2022 ◽  
Author(s):  
Alberto Lazari ◽  
Piergiorgio Salvan ◽  
Michiel Cottaar ◽  
Daniel Papp ◽  
Matthew FS Rushworth ◽  
...  
Keyword(s):  
Synaptic Plasticity ◽  
White Matter ◽  
Associative Learning ◽  
Fiber Bundle ◽  
Cortical Excitability ◽  
Brain Plasticity ◽  
Cortical Areas ◽  
Hebbian Plasticity ◽  
Synaptic Changes ◽  
Activity Dependent

Synaptic plasticity is required for learning and follows Hebb's Rule, the computational principle underpinning associative learning. In recent years, a complementary type of brain plasticity has been identified in myelinated axons, which make up the majority of brain's white matter. Like synaptic plasticity, myelin plasticity is required for learning, but it is unclear whether it is Hebbian or whether it follows different rules. Here, we provide evidence that white matter plasticity operates following Hebb's Rule in humans. Across two experiments, we find that co-stimulating cortical areas to induce Hebbian plasticity leads to relative increases in cortical excitability and associated increases in a myelin marker within the stimulated fiber bundle. We conclude that Hebbian plasticity extends beyond synaptic changes, and can be observed in human white matter fibers.

scholarly journals Coupling an aVLSI Neuromorphic Vision Chip to a Neurotrophic Model of Synaptic Plasticity: The Development of Topography

2002 ◽  
Vol 14 (10) ◽  
pp. 2353-2370 ◽  
Author(s):  
Terry Elliott ◽  
Jörg Kramer
Keyword(s):  
Synaptic Plasticity ◽  
Neuronal Development ◽  
Ganglion Cells ◽  
Activity Patterns ◽  
Retinal Ganglion ◽  
Biologically Inspired ◽  
Silicon Neurons ◽  
Retina Chip ◽  
Developing System ◽  
Activity Dependent

We couple a previously studied, biologically inspired neurotrophic model of activity-dependent competitive synaptic plasticity and neuronal development to a neuromorphic retina chip. Using this system, we examine the development and refinement of a topographic mapping between an array of afferent neurons (the retinal ganglion cells) and an array of target neurons. We find that the plasticity model can indeed drive topographic refinement in the presence of afferent activity patterns generated by a real-world device. We examine the resilience of the developing system to the presence of high levels of noise by adjusting the spontaneous firing rate of the silicon neurons.

Decoding the sense of smell: understanding the structural organization within the brain

2020 ◽  
Vol 2 (7) ◽  
pp. 4-9
Author(s):  
Shripriya Singh
Keyword(s):  
Neural Activity ◽  
Structural Organization ◽  
Piriform Cortex ◽  
Molecular Structures ◽  
The Other ◽  
Multiphoton Imaging ◽  
Sensory Inputs ◽  
Correlated Patterns ◽  
Olfactory Sense ◽  
The Brain

The olfactory sense is a potent sensory tool which helps us perceive our environment much better. However, smells despite being similar have different impacts on individuals. What makes one odor categorically different from the other and why do people have a unique and personalized experience with smell is an answer that needs to be addressed. In the present article we have discussed the research in which neuroscientists have decoded and described how the relationships between different odors are encoded in the brain. How the brain transforms information about odor chemistry into the perception of smell is a major highlight of this publication. Carefully selected odors with defined molecular structures were delivered in mice and the neural activity was analyzed. It was observed that neuronal representations of smell in the cortex reflected chemical similarities between odors, thus allowing the brain to categorize scents. The study has employed chemo informatics and multiphoton imaging in the mouse to demonstrate both the piriform cortex and its sensory inputs from the olfactory bulb represent chemical odor relationships through correlated patterns of activity. The research has given us cues in the direction of how the brain translates odor chemistry into neurochemistry and eventually perception of smell.

scholarly journals Stimulus Feature-Specific Information Flow Along the Columnar Cortical Microcircuit Revealed by Multivariate Laminar Spiking Analysis

2020 ◽  
Vol 14 ◽  
Author(s):  
David A. Tovar ◽  
Jacob A. Westerberg ◽  
Michele A. Cox ◽  
Kacie Dougherty ◽  
Thomas A. Carlson ◽  
...  
Keyword(s):  
Neural Activity ◽  
Visual Stimulation ◽  
Brain Activity ◽  
Contextual Information ◽  
Univariate Analysis ◽  
Stimulus Presentation ◽  
Population Level ◽  
Granular Cell ◽  
Stimulus Orientation ◽  
Specific Information

Most of the mammalian neocortex is comprised of a highly similar anatomical structure, consisting of a granular cell layer between superficial and deep layers. Even so, different cortical areas process different information. Taken together, this suggests that cortex features a canonical functional microcircuit that supports region-specific information processing. For example, the primate primary visual cortex (V1) combines the two eyes' signals, extracts stimulus orientation, and integrates contextual information such as visual stimulation history. These processes co-occur during the same laminar stimulation sequence that is triggered by the onset of visual stimuli. Yet, we still know little regarding the laminar processing differences that are specific to each of these types of stimulus information. Univariate analysis techniques have provided great insight by examining one electrode at a time or by studying average responses across multiple electrodes. Here we focus on multivariate statistics to examine response patterns across electrodes instead. Specifically, we applied multivariate pattern analysis (MVPA) to linear multielectrode array recordings of laminar spiking responses to decode information regarding the eye-of-origin, stimulus orientation, and stimulus repetition. MVPA differs from conventional univariate approaches in that it examines patterns of neural activity across simultaneously recorded electrode sites. We were curious whether this added dimensionality could reveal neural processes on the population level that are challenging to detect when measuring brain activity without the context of neighboring recording sites. We found that eye-of-origin information was decodable for the entire duration of stimulus presentation, but diminished in the deepest layers of V1. Conversely, orientation information was transient and equally pronounced along all layers. More importantly, using time-resolved MVPA, we were able to evaluate laminar response properties beyond those yielded by univariate analyses. Specifically, we performed a time generalization analysis by training a classifier at one point of the neural response and testing its performance throughout the remaining period of stimulation. Using this technique, we demonstrate repeating (reverberating) patterns of neural activity that have not previously been observed using standard univariate approaches.

scholarly journals Regulation and Modulation of pH in the Brain

2003 ◽  
Vol 83 (4) ◽  
pp. 1183-1221 ◽  
Author(s):  
MITCHELL CHESLER
Keyword(s):  
Intracellular Ph ◽  
Neural Activity ◽  
Extracellular Fluid ◽  
Cell Types ◽  
Brain Regions ◽  
Metabolic Products ◽  
Homeostatic Function ◽  
Acid Extrusion ◽  
Activity Dependent ◽  
The Brain

Chesler, Mitchell. Regulation and Modulation of pH in the Brain. Physiol Rev 83: 1183-1221, 2003; 10.1152/physrev.00010.2003.—The regulation of pH is a vital homeostatic function shared by all tissues. Mechanisms that govern H+ in the intracellular and extracellular fluid are especially important in the brain, because electrical activity can elicit rapid pH changes in both compartments. These acid-base transients may in turn influence neural activity by affecting a variety of ion channels. The mechanisms responsible for the regulation of intracellular pH in brain are similar to those of other tissues and are comprised principally of forms of Na+/H+ exchange, Na+-driven Cl-/HCO3- exchange, Na+-HCO3- cotransport, and passive Cl-/HCO3- exchange. Differences in the expression or efficacy of these mechanisms have been noted among the functionally and morphologically diverse neurons and glial cells that have been studied. Molecular identification of transporter isoforms has revealed heterogeneity among brain regions and cell types. Neural activity gives rise to an assortment of extracellular and intracellular pH shifts that originate from a variety of mechanisms. Intracellular pH shifts in neurons and glia have been linked to Ca2+ transport, activation of acid extrusion systems, and the accumulation of metabolic products. Extracellular pH shifts can occur within milliseconds of neural activity, arise from an assortment of mechanisms, and are governed by the activity of extracellular carbonic anhydrase. The functional significance of these compartmental, activity-dependent pH shifts is discussed.

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