Networks of Formal Neurons and Memory Palimpsests

Nadal, Jean-Pierre; Toulouse, G.; Changeux, J P; Dehaene, Stanislas

doi:10.1209/0295-5075/1/10/008

Cited by 206 publications

(121 citation statements)

References 7 publications

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“…The slow plasticity dynamics of synapses are driven by competitive and cooperative interactions consequent on the fast dynamics of firing neurons. The model is analysed within a mean-field approximation, in common with many physics-based approaches to neuroscience, ranging from earlier work [11][12][13][14][15][16][17][18][19][20] to more recent developments [55]. Such a mean-field framework is of course appropriate given the lack of knowledge of microscopic details at the neural or synaptic level.…”

Section: Discussionmentioning

confidence: 99%

“…An essential property of these models as well as real neural networks is that their capacity is finite. Forgetting is therefore an important aspect of continued learning [3,[15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

“…This mechanism is an important one in the context of finite storage capacities, when space is created via the spontaneous decay of old memories. This is sometimes referred to as the palimpsest effect [15,16].…”

Section: Introductionmentioning

confidence: 99%

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Slow synaptic dynamics in a network: From exponential to power-law forgetting

Luck

Mehta

2014

Phys. Rev. E

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We investigate a mean-field model of interacting synapses on a directed neural network. Our interest lies in the slow adaptive dynamics of synapses, which are driven by the fast dynamics of the neurons they connect. Cooperation is modelled from the usual Hebbian perspective, while competition is modelled by an original polarity-driven rule. The emergence of a critical manifold culminating in a tricritical point is crucially dependent on the presence of synaptic competition. This leads to a universal 1/t power-law relaxation of the mean synaptic strength along the critical manifold and an equally universal 1/ √ t relaxation at the tricritical point, to be contrasted with the exponential relaxation that is otherwise generic. In turn, this leads to the natural emergence of long-and short-term memory from different parts of parameter space in a synaptic network, which is the most novel and important result of our present investigations.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Slow synaptic dynamics in a network: From exponential to power-law forgetting

Luck

Mehta

2014

Phys. Rev. E

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“…The main feature of the models [14,15,21] generalizing the Hebb rule for learning process is that the memory has a finite capacity and that the learning of new patterns generally implies forgetting of old ones. In this way, one avoids to reach the state of total confusion.…”

Section: A Stochastic Model For Memory Loadingmentioning

confidence: 99%

“…In the last years, after the seminal work of Hopfield [6], there was great interest in associative memory networks exhibiting statistical behavior similar to the socalled spin glass model [ 1,10,[13][14][15][16]. Probability laws were first introduced by Little [10], who interpreted the sigmo~dal kind of output of a cell to a given sequence of inputs in terms of random noise.…”

Section: Introductionmentioning

confidence: 99%

Stochastic dynamical aspects of neuronal activity

et al. 1993

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A scientific theory of Ars Memoriae: Spatial view cells in a continuous attractor network with linked items

Rolls

2017

Hippocampus

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The art of memory (ars memoriae) used since classical times includes using a well-known scene to associate each view or part of the scene with a different item in a speech. This memory technique is also known as the 'method of loci'. The new theory is proposed that this type of memory is implemented in the CA3 region of the hippocampus where there are spatial view cells in primates that allow a particular view to be associated with a particular object in an event or episodic memory. Given that the CA3 cells with their extensive recurrent collateral system connecting different CA3 cells, and associative synaptic modifiability, form an autoassociation or attractor network, the spatial view cells with their approximately Gaussian view fields become linked in a continuous attractor network. As the view space is traversed continuously (for example by self-motion or imagined self-motion across the scene), the views are therefore successively recalled in the correct order, with no view missing, and with low interference between the items to be recalled. Given that each spatial view has been associated with a different discrete item, the items are recalled in the correct order, with none missing. This is the first neuroscience theory of ars memoriae. The theory provides a foundation for understanding how a key feature of ars memoriae, the ability to use a spatial scene to encode a sequence of items to be remembered, is implemented.3

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Networks of Formal Neurons and Memory Palimpsests

Abstract: PACS. 87.30. -Biophysics of neurophysiological processes. PACS. 75.10H. -Ising and other classical spin models. PACS. 64.60. -General studies of phase transitions.

Cited by 206 publications

References 7 publications

Slow synaptic dynamics in a network: From exponential to power-law forgetting

Slow synaptic dynamics in a network: From exponential to power-law forgetting

Stochastic dynamical aspects of neuronal activity

A scientific theory of Ars Memoriae: Spatial view cells in a continuous attractor network with linked items

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