scholarly journals The temporal paradox of Hebbian learning and homeostatic plasticity

2017 ◽  
Author(s):  
Friedemann Zenke ◽  
Wulfram Gerstner ◽  
Surya Ganguli
Keyword(s):  
Learning And Memory ◽  
Neuronal Activity ◽  
Neural Circuits ◽  
Hebbian Learning ◽  
Homeostatic Plasticity ◽  
Hebbian Plasticity ◽  
Slow Evolution ◽  
The Brain

AbstractHebbian plasticity, a synaptic mechanism which detects and amplifies co-activity between neurons, is considered a key ingredient underlying learning and memory in the brain. However, Hebbian plasticity alone is unstable, leading to runaway neuronal activity, and therefore requires stabilization by additional compensatory processes. Traditionally, a diversity of homeostatic plasticity phenomena found in neural circuits are thought to play this role. However, recent modelling work suggests that the slow evolution of homeostatic plasticity, as observed in experiments, is insufficient to prevent instabilities originating from Hebbian plasticity. To remedy this situation, we suggest that homeostatic plasticity is complemented by additional rapid compensatory processes, which rapidly stabilize neuronal activity on short timescales.

scholarly journals Homeostatic plasticity mechanisms in mouse V1

2017 ◽  
Vol 372 (1715) ◽  
pp. 20160504 ◽  
Author(s):  
Megumi Kaneko ◽  
Michael P. Stryker
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Neuronal Activity ◽  
Dynamic Range ◽  
Ocular Dominance ◽  
Homeostatic Plasticity ◽  
Ocular Dominance Plasticity ◽  
The Brain

Mechanisms thought of as homeostatic must exist to maintain neuronal activity in the brain within the dynamic range in which neurons can signal. Several distinct mechanisms have been demonstrated experimentally. Three mechanisms that act to restore levels of activity in the primary visual cortex of mice after occlusion and restoration of vision in one eye, which give rise to the phenomenon of ocular dominance plasticity, are discussed. The existence of different mechanisms raises the issue of how these mechanisms operate together to converge on the same set points of activity. This article is part of the themed issue ‘Integrating Hebbian and homeostatic plasticity’.

Difficulties with synaptic theory of learning and memory and possible remedies

2000 ◽  
Vol 23 (4) ◽  
pp. 550-551
Author(s):  
Mikhail N. Zhadin
Keyword(s):  
Cerebral Cortex ◽  
Associative Learning ◽  
Learning And Memory ◽  
Hebbian Learning ◽  
Behavioral Responses ◽  
Learning Rules ◽  
The Brain

The absence of a clear influence of an animal's behavioral responses to Hebbian associative learning in the cerebral cortex requires some changes in the Hebbian learning rules. The participation of the brain monoaminergic systems in Hebbian associative learning is considered.

scholarly journals The dialectic of Hebb and homeostasis

2017 ◽  
Vol 372 (1715) ◽  
pp. 20160258 ◽  
Author(s):  
Gina G. Turrigiano
Keyword(s):  
Recent Progress ◽  
Neural Circuits ◽  
Neural Circuit ◽  
Homeostatic Plasticity ◽  
Homeostatic Control ◽  
Spatial And Temporal Scales ◽  
Network Function ◽  
Hebbian Plasticity ◽  
Circuit Function ◽  
Hebbian Mechanisms

It has become widely accepted that homeostatic and Hebbian plasticity mechanisms work hand in glove to refine neural circuit function. Nonetheless, our understanding of how these fundamentally distinct forms of plasticity compliment (and under some circumstances interfere with) each other remains rudimentary. Here, I describe some of the recent progress of the field, as well as some of the deep puzzles that remain. These include unravelling the spatial and temporal scales of different homeostatic and Hebbian mechanisms, determining which aspects of network function are under homeostatic control, and understanding when and how homeostatic and Hebbian mechanisms must be segregated within neural circuits to prevent interference. This article is part of the themed issue ‘Integrating Hebbian and homeostatic plasticity’.

scholarly journals Motor Learning Drives Dynamic Patterns of Intermittent Myelination on Behaviorally-Activated Axons

2021 ◽  
Author(s):  
Clara M. Bacmeister ◽  
Rongchen Huang ◽  
Michael A. Thornton ◽  
Lauren Conant ◽  
Anthony R. Chavez ◽  
...  
Keyword(s):  
Motor Learning ◽  
Computational Modeling ◽  
Learning And Memory ◽  
Neuronal Activity ◽  
Neural Circuits ◽  
Cortical Layers ◽  
Nodes Of Ranvier ◽  
Two Photon ◽  
Myelin Sheaths

Myelin plasticity occurs when newly-formed and pre-existing oligodendrocytes remodel existing myelination. Recent studies show these processes occur in response to changes in neuronal activity and are required for learning and memory. However, the link between behaviorally-relevant neuronal activity and circuit-specific changes in myelination remains unknown. Using longitudinal, in vivo two-photon imaging and targeted labeling of behaviorally-activated neurons, we explore how the pattern of intermittent myelination is altered on individual cortical axons during learning of a dexterous reach task. We show that learning-induced plasticity is targeted to behaviorally-activated axons and occurs in a staged response across cortical layers. During learning, myelin sheaths retract, lengthening nodes of Ranvier. Following learning, addition of new sheaths increases the number of continuous stretches of myelination. Computational modeling suggests these changes initially slow and subsequently increase conduction speed. Thus, behaviorally-activated, circuit-specific changes to myelination may fundamentally alter how information is transferred in neural circuits during learning.

scholarly journals Integrating Hebbian and homeostatic plasticity: introduction

2017 ◽  
Vol 372 (1715) ◽  
pp. 20160413 ◽  
Author(s):  
Kevin Fox ◽  
Michael Stryker
Keyword(s):  
Royal Society ◽  
Research Agenda ◽  
Homeostatic Plasticity ◽  
Sensory Adaptation ◽  
Original State ◽  
Critical Questions ◽  
Theory Of Plasticity ◽  
Hebbian Plasticity ◽  
The Brain

Hebbian plasticity is widely considered to be the mechanism by which information can be coded and retained in neurons in the brain. Homeostatic plasticity moves the neuron back towards its original state following a perturbation, including perturbations produced by Hebbian plasticity. How then does homeostatic plasticity avoid erasing the Hebbian coded information? To understand how plasticity works in the brain, and therefore to understand learning, memory, sensory adaptation, development and recovery from injury, requires development of a theory of plasticity that integrates both forms of plasticity into a whole. In April 2016, a group of computational and experimental neuroscientists met in London at a discussion meeting hosted by the Royal Society to identify the critical questions in the field and to frame the research agenda for the next steps. Here, we provide a brief introduction to the papers arising from the meeting and highlight some of the themes to have emerged from the discussions. This article is part of the themed issue ‘Integrating Hebbian and homeostatic plasticity’.

Learning, Neurogenesis, and Effects of Flavonoids on Learning

2021 ◽  
Vol 21 ◽  
Author(s):  
Asan Yalmaz Hasan Almulla ◽  
Rasim Mogulkoc ◽  
Abdulkerim Kasim Baltaci ◽  
Dervis Dasdelen
Keyword(s):  
Learning And Memory ◽  
Neural Circuits ◽  
Brain Regions ◽  
Hippocampal Neurogenesis ◽  
Adult Brain ◽  
Factors Affecting ◽  
Different Types ◽  
Mature Neurons ◽  
Dependent Learning ◽  
The Brain

: Learning and memory are two of our mind's most magical abilities. Different brain regions have roles in processing and storing different types of memories. The hippocampus is the part of the brain responsible for receiving information and storing it in the neocortex. One of the most impressive characteristics of the hippocampus is its capacity for neurogenesis, which is a process in which new neurons are produced and then transformed into mature neurons and finally integrated into neural circuits. The neurogenesis process in the hippocampus, an example of neuroplasticity in the adult brain, is believed to aid hippocampal-dependent learning and memory. New neurons are constantly produced in the hippocampus and integrated into the pre-existing neuronal network; this allows old memories already stored in the neocortex to be removed from the hippocampus and replaced with new ones. Factors affecting neurogenesis in the hippocampus may also affect hippocampal-dependent learning and memory. The flavonoids can particularly exert powerful actions in mammalian cognition and improve hippocampal-dependent learning and memory by positively affecting hippocampal neurogenesis.

scholarly journals Achieving stable dynamics in neural circuits

2020 ◽  
Author(s):  
Leo Kozachkov ◽  
Mikael Lundqvist ◽  
Jean-Jacques Slotine ◽  
Earl K. Miller
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Neural Circuits ◽  
Time Varying ◽  
Biological Complexity ◽  
Multiple Sources ◽  
Hebbian Plasticity ◽  
Autonomous Activity ◽  
The Brain ◽  
Shed Light

1AbstractThe brain consists of many interconnected networks with time-varying, partially autonomous activity. There are multiple sources of noise and variation yet activity has to eventually converge to a stable, reproducible state (or sequence of states) for its computations to make sense. We approached this problem from a control-theory perspective by applying contraction analysis to recurrent neural networks. This allowed us to find mechanisms for achieving stability in multiple connected networks with biologically realistic dynamics, including synaptic plasticity and time-varying inputs. These mechanisms included inhibitory Hebbian plasticity, excitatory anti-Hebbian plasticity, synaptic sparsity and excitatory-inhibitory balance. Our findings shed light on how stable computations might be achieved despite biological complexity.

scholarly journals Hebbian plasticity requires compensatory processes on multiple timescales

2017 ◽  
Vol 372 (1715) ◽  
pp. 20160259 ◽  
Author(s):  
Friedemann Zenke ◽  
Wulfram Gerstner
Keyword(s):  
Synaptic Plasticity ◽  
Experimental Research ◽  
Neural Circuits ◽  
Homeostatic Plasticity ◽  
Fine Tuning ◽  
Control Mechanisms ◽  
Network Stability ◽  
Multiple Timescales ◽  
Hebbian Plasticity ◽  
Compensatory Process

We review a body of theoretical and experimental research on Hebbian and homeostatic plasticity, starting from a puzzling observation: while homeostasis of synapses found in experiments is a slow compensatory process, most mathematical models of synaptic plasticity use rapid compensatory processes (RCPs). Even worse, with the slow homeostatic plasticity reported in experiments, simulations of existing plasticity models cannot maintain network stability unless further control mechanisms are implemented. To solve this paradox, we suggest that in addition to slow forms of homeostatic plasticity there are RCPs which stabilize synaptic plasticity on short timescales. These rapid processes may include heterosynaptic depression triggered by episodes of high postsynaptic firing rate. While slower forms of homeostatic plasticity are not sufficient to stabilize Hebbian plasticity, they are important for fine-tuning neural circuits. Taken together we suggest that learning and memory rely on an intricate interplay of diverse plasticity mechanisms on different timescales which jointly ensure stability and plasticity of neural circuits. This article is part of the themed issue ‘Integrating Hebbian and homeostatic plasticity’.

scholarly journals Indexing sensory plasticity: Evidence for distinct Predictive Coding and Hebbian Learning mechanisms in the cerebral cortex

2017 ◽  
Author(s):  
Meg J Spriggs ◽  
Rachael L Sumner ◽  
Rebecca L McMillan ◽  
Rosalyn J Moran ◽  
Ian J Kirk ◽  
...  
Keyword(s):  
Hebbian Learning ◽  
Predictive Coding ◽  
Task Demands ◽  
Learning Mechanisms ◽  
Sensory Plasticity ◽  
Hebbian Plasticity ◽  
Underlying Network ◽  
Generative Mechanisms ◽  
The Brain ◽  
Hebbian Mechanisms

The Roving Mismatch Negativity (MMN), and Visual LTP paradigms are widely used as independent measures of sensory plasticity. However, the paradigms are built upon fundamentally different (and seemingly opposing) models of perceptual learning; namely, Predictive Coding (MMN) and Hebbian plasticity (LTP). The aims of the current study were to 1) compare the generative mechanisms of the MMN and visual LTP, therefore assessing whether Predictive Coding and Hebbian mechanisms co-occur in the brain, and 2) assess whether the paradigms identify similar group differences in plasticity. Forty participants were split into two groups based on the BDNF Val66Met polymorphism and were presented with both paradigms. Consistent with Predictive Coding and Hebbian predictions, Dynamic Causal Modelling revealed that the generation of the MMN modulates forward and backward connections in the underlying network, while visual LTP only modulates forward connections. Genetic differences were identified in the ERPs for both paradigms, but were only apparent in backward connections of the MMN network. These results suggest that both Predictive Coding and Hebbian mechanisms are utilized by the brain under different task demands. Additionally, both tasks provide unique insight into plasticity mechanisms, which has important implications for future studies of aberrant plasticity in clinical populations.

Observation of neuronal activity using real-time voltage-sensitive dye imaging

1992 ◽  
Vol 50 (1) ◽  
pp. 476-477
Author(s):  
John S. Kauer ◽  
Angel Cinelli ◽  
David Wellis ◽  
Joel White
Keyword(s):  
Neuronal Activity ◽  
Neural Circuits ◽  
Brain Activity ◽  
Single Cells ◽  
Pyramidal Cells ◽  
Optical Recording ◽  
Sensory Cells ◽  
Large Numbers ◽  
The Brain ◽  
Prepyriform Cortex

Sensory systems are confronted with the problem of taking “information” in the outside world and encoding and manipulating it in forms that can be used in the neuronal world. A major challenge is to document how the transition between these worlds takes place (transduction) and, once it has taken place, how the data are manipulated by neural circuits (integration). Since the brain is an intrinsically parallel device, carrying out many functions simultaneously, it would appear as important to record brain activity in a similarly parallel manner as to record events in single cells and membranes. Optical recording of neuronal events offers a first step toward thing to observe events that are distributed among the cells and processes of a neuronal network.In the sense of smell odors appear to be encoded by activity distributed across many neurons at each level of the system studied so far, from the sensory cells in the nose to the pyramidal cells in prepyriform cortex (for review see). Thus, to elucidate how the molecular properties of odorants are represented by neurons it is probably necessary to observe the patterns of distributed activation. The distribution of activity across many neuronal elements, in contrast to representing odor molecules by dedicated “labelled lines”, confers redundancy and fault tolerance on a system that is crucial for complex behaviors that underly survival for many animals species, as well as providing flexibility for being sensitive to large numbers of compounds.

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