Editorial

Ooyen, Arjen van; Butz‐Ostendorf, Markus

doi:10.1016/b978-0-12-803784-3.00033-0

Cited by 6 publications

(12 citation statements)

References 10 publications

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“…First, the fact that the synapse is a structure involving neuronal connections is not a burden to a synaptic theory of memory but an asset. Associative memories are formed through a rewiring of brain circuits (Fuster, 1997 ; Chau et al, 2014 ; Van Ooyen and Butz-Ostendorf, 2017 ) such that the collection of neurons (cell assemblies) representing stimulus “A” and those representing stimulus “B” are either connected by de novo synaptogenesis (Le Bé and Markram, 2006 ; Kwon and Sabatini, 2011 ); have existing synapses between these neurons potentiated; or a combination of both (Choi et al, 2018 ). Subsequent activity in “A” encoding neurons can then, through spreading activation, elicit activity in the neurons encoding “B” thus providing a neural link between the cell assemblies (a phase sequence) representing each of these two originally discrete stimuli (Anderson, 1983 ).…”

Section: Resolution Of Recent Critiques Using Modern Neurophysiologicmentioning

confidence: 99%

The Synaptic Theory of Memory: A Historical Survey and Reconciliation of Recent Opposition

Langille

Brown

2018

Front. Syst. Neurosci.

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Trettenbrein (2016) has argued that the concept of the synapse as the locus of memory is outdated and has made six critiques of this concept. In this article, we examine these six critiques and suggest that the current theories of the neurobiology of memory and the empirical data indicate that synaptic activation is the first step in a chain of cellular and biochemical events that lead to memories formed in cell assemblies and neural networks that rely on synaptic modification for their formation. These neural networks and their modified synaptic connections can account for the cognitive basis of learning and memory and for memory deterioration in neurological disorders. We first discuss Hebb’s (1949) theory that synaptic change and the formation of cell assemblies and phase sequences can link neurophysiology to cognitive processes. We then examine each of Trettenbrein’s (2016) critiques of the synaptic theory in light of Hebb’s theories and recent empirical data. We examine the biochemical basis of memory formation and the necessity of synaptic modification to form the neural networks underlying learning and memory. We then examine the use of Hebb’s theories of synaptic change and cell assemblies for integrating neurophysiological and cognitive conceptions of learning and memory. We conclude with an examination of the applications of the Hebb synapse and cell assembly theories to the study of the neuroscience of learning and memory, the development of computational models of memory and the construction of “intelligent” robots. We conclude that the synaptic theory of memory has not met its demise, but is essential to our understanding of the neural basis of memory, which has two components: synaptic plasticity and intrinsic plasticity.

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Section: Resolution Of Recent Critiques Using Modern Neurophysiologicmentioning

confidence: 99%

The Synaptic Theory of Memory: A Historical Survey and Reconciliation of Recent Opposition

Langille

Brown

2018

Front. Syst. Neurosci.

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“…In neuronal networks, the adaptive (plastic) nature of the synaptic contacts is linked to normal brain function, learning of new skills and retention of long-term memories 15 – 17 , and formation of non-random and clustered assemblies of neurons 15 , 18 , 19 . Notwithstanding, network plasticity may also engender pathological neuronal synchronization observed in several brain disorders, such as epilepsy and Parkinson’s disease (PD) 20 – 22 .…”

Section: Introductionmentioning

confidence: 99%

“…Another form of plasticity called structural plasticity (SP), operating on a longer time scale compared to STDP, involves the addition and elimination (pruning) of the synaptic contacts, which could be activity-dependent 15 , 38 . For instance, the homeostatic SP maintains a background (homeostatic) level of activity of the neurons 38 , 39 and is essential for stabilizing the activity of the neuronal networks 19 , 39 by both scaling the synaptic weights 40 and adding and removing contacts 15 , 41 . On the other hand, SP could lead to the stabilization of pathological conditions such as chronic pain, neuropathic pain, and nociceptive hypersensitivity 42 .…”

Section: Introductionmentioning

confidence: 99%

Dynamics of phase oscillator networks with synaptic weight and structural plasticity

Chauhan

Khaledi-Nasab

Neiman

et al. 2022

Sci Rep

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We study the dynamics of Kuramoto oscillator networks with two distinct adaptation processes, one varying the coupling strengths and the other altering the network structure. Such systems model certain networks of oscillatory neurons where the neuronal dynamics, synaptic weights, and network structure interact with and shape each other. We model synaptic weight adaptation with spike-timing-dependent plasticity (STDP) that runs on a longer time scale than neuronal spiking. Structural changes that include addition and elimination of contacts occur at yet a longer time scale than the weight adaptations. First, we study the steady-state dynamics of Kuramoto networks that are bistable and can settle in synchronized or desynchronized states. To compare the impact of adding structural plasticity, we contrast the network with only STDP to one with a combination of STDP and structural plasticity. We show that the inclusion of structural plasticity optimizes the synchronized state of a network by allowing for synchronization with fewer links than a network with STDP alone. With non-identical units in the network, the addition of structural plasticity leads to the emergence of correlations between the oscillators’ natural frequencies and node degrees. In the desynchronized regime, the structural plasticity decreases the number of contacts, leading to a sparse network. In this way, adding structural plasticity strengthens both synchronized and desynchronized states of a network. Second, we use desynchronizing coordinated reset stimulation and synchronizing periodic stimulation to induce desynchronized and synchronized states, respectively. Our findings indicate that a network with a combination of STDP and structural plasticity may require stronger and longer stimulation to switch between the states than a network with STDP only.

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“…In addition, a computational analysis revealed that the observed changes of structural weights are compliant with a hypothesized Hebbian-like mechanism ( van Hartevelt et al, 2015 ). For a more extensive discussion on the role of structural plasticity in the brain please see the corresponding section of the Supplementary Material and van Ooyen and Butz-Ostendorf (2017) .…”

Section: Introductionmentioning

confidence: 99%

Long-Term Desynchronization by Coordinated Reset Stimulation in a Neural Network Model With Synaptic and Structural Plasticity

2021

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Several brain disorders are characterized by abnormal neuronal synchronization. To specifically counteract abnormal neuronal synchrony and, hence, related symptoms, coordinated reset (CR) stimulation was computationally developed. In principle, successive epochs of synchronizing and desynchronizing stimulation may reversibly move neural networks with plastic synapses back and forth between stable regimes with synchronized and desynchronized firing. Computationally derived predictions have been verified in pre-clinical and clinical studies, paving the way for novel therapies. However, as yet, computational models were not able to reproduce the clinically observed increase of desynchronizing effects of regularly administered CR stimulation intermingled by long stimulation-free epochs. We show that this clinically important phenomenon can be computationally reproduced by taking into account structural plasticity (SP), a mechanism that deletes or generates synapses in order to homeostatically adapt the firing rates of neurons to a set point-like target firing rate in the course of days to months. If we assume that CR stimulation favorably reduces the target firing rate of SP, the desynchronizing effects of CR stimulation increase after long stimulation-free epochs, in accordance with clinically observed phenomena. Our study highlights the pivotal role of stimulation- and dosing-induced modulation of homeostatic set points in therapeutic processes.

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Editorial

Cited by 6 publications

References 10 publications

The Synaptic Theory of Memory: A Historical Survey and Reconciliation of Recent Opposition

The Synaptic Theory of Memory: A Historical Survey and Reconciliation of Recent Opposition

Dynamics of phase oscillator networks with synaptic weight and structural plasticity

Long-Term Desynchronization by Coordinated Reset Stimulation in a Neural Network Model With Synaptic and Structural Plasticity

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