Synergies Between Intrinsic and Synaptic Plasticity Mechanisms

Triesch, Jochen

doi:10.1162/neco.2007.19.4.885

Cited by 139 publications

(177 citation statements)

References 42 publications

(38 reference statements)

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“…Intrinsic plasticity (Triesch 2007;Mozzachiodi and Byrne 2009;Gründemann and Häusser 2010) describes a mechanism that changes, based on the neuron's activity history, a neuron's input-output activity relation, thereby leading to distinct neuronal responses for different "memorized" activities. In Table 1-2 we state the original description by Triesch (2007). The physiological basis of the update rules for slope a and offset b is, however, unclear.…”

Section: Physiological Mechanismmentioning

confidence: 99%

Time scales of memory, learning, and plasticity

et al. 2012

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If we stored every bit of input, the storage capacity of our nervous system would be reached after only about 10 days. The nervous system relies on at least two mechanisms that counteract this capacity limit: compression and forgetting. But the latter mechanism needs to know how long an entity should be stored: some memories are relevant only for the next few minutes, some are important even after the passage of several years. Psychology and physiology have found and described many different memory mechanisms, and these mechanisms indeed use different time scales. In this prospect we review these mechanisms with respect to their time scale and propose relations between mechanisms in learning and memory and their underlying physiological basis.

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Section: Physiological Mechanismmentioning

confidence: 99%

Time scales of memory, learning, and plasticity

et al. 2012

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“…4, the neurons' intrinsic plasticity modeling [17], which is applied in the hidden layers, is based on the biological finding that a neuron can adjust its intrinsic electrical properties following its neuronal or synaptic activity. Mathematically, it adjusts its function parameters, slope and threshold, so as to fit its output rate to a sparse exponential regime.…”

Section: Training Algorithmmentioning

confidence: 99%

Learning Features and Predictive Transformation Encoding Based on a Horizontal Product Model

Zhong

Weber

Wermter

2012

Artificial Neural Networks and Machine Learning – ICANN 2012

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Abstract. The visual system processes the features and movement of an object in separate pathways, called the ventral and dorsal streams. To integrate this principle in a functional model, a recurrent predictive network with a horizontal product is introduced. Learned in an unsupervised manner, two sets of hidden units represent cells in the ventral and dorsal pathways, respectively. Experiments show that the activity in the ventral-like units persists, given that the same feature appears in the receptive field, whilst the activity in the dorsal-like units shows a fluctuating pattern with different directions of object movements. Moreover, we show that the position information predicts the input's future position taking into account its moving direction due to the direction-selective responses of the dorsal-like units.

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“…Despite many implementations of Hebbian learning mechanisms that comprise forms of stabilization [3] and synaptic competition [4], [5] have been proposed, very few attempts have been made to implement explicit global homeostatic plasticity mechanisms, in parallel with classical local learning ones [6]. However, it has been recently argued that the interplay between local Hebbian and global homeostatic processes can have complementary roles [5], or even important synergistic effects [6], allowing for complex behaviors, such as independent component analysis, which would not be possible with either mechanisms alone.…”

Section: Introductionmentioning

confidence: 99%

Implementing homeostatic plasticity in VLSI networks of spiking neurons

Bartolozzi

Nikolayeva

Indiveri

2008

2008 15th IEEE International Conference on Electronics, Circuits and Systems

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Homeostatic plasticity acts to stabilize firing activity in neural systems, ensuring a homogeneous computational substrate despite the inherent differences among neurons and their continuous change. These types of mechanisms are extremely relevant for any physical implementation of neural systems. They can be used in VLSI pulse-based neural networks to automatically adapt to chronic input changes, device mismatch, as well as slow systematic changes in the circuitpsilas functionality (e.g. due to temperature drifts). In this paper we propose analog circuits for implementing homeostatic plasticity mechanisms in VLSI spiking neural networks, compatible with local spike-based learning mechanisms. We show experimental results where a homeostatic control is implemented as a hybrid SoftWare/HardWare (SW/HW) solution, and present analog circuits for a complete on-chip stand-alone solution, validated by circuit simulations. Abstract-Homeostatic plasticity acts to stabilize firing activity in neural systems, ensuring a homogeneous computational substrate despite the inherent differences among neurons and their continuous change. These types of mechanisms are extremely relevant for any physical implementation of neural systems. They can be used in VLSI pulse-based neural networks to automatically adapt to chronic input changes, device mismatch, as well as slow systematic changes in the circuit's functionality (e.g. due to temperature drifts). In this paper we propose analog circuits for implementing homeostatic plasticity mechanisms in VLSI spiking neural networks, compatible with local spike-based learning mechanisms. We show experimental results where a homeostatic control is implemented as a hybrid SoftWare/HardWare (SW/HW) solution, and present analog circuits for a complete on-chip stand-alone solution, validated by circuit simulations.

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Synergies Between Intrinsic and Synaptic Plasticity Mechanisms

Cited by 139 publications

References 42 publications

Time scales of memory, learning, and plasticity

Time scales of memory, learning, and plasticity

Learning Features and Predictive Transformation Encoding Based on a Horizontal Product Model

Implementing homeostatic plasticity in VLSI networks of spiking neurons

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