Equilibrium Properties of Temporally Asymmetric Hebbian Plasticity

Rubin, Jonathan E.; Lee, Daniel D.; Sompolinsky, Haim

doi:10.1103/physrevlett.86.364

Cited by 271 publications

(314 citation statements)

References 7 publications

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“…The rule induces no competition between synapses and consequently no activity regulation (Kistler and van Hemmen 2000;van Rossum et al 2000;Rubin et al 2001;Câteau and Fukai 2003). The results obtained (data not shown) were quite similar to those shown in Figure 4C; as expected, synchronous firing was not predictive in the absence of the activity regulation.…”

Section: Dual Roles Of Stdp In the Predictive Synchronysupporting

confidence: 77%

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Temporal Characteristics of the Predictive Synchronous Firing Modeled by Spike-Timing-Dependent Plasticity

Kitano

Fukai²

2004

Learn. Mem.

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When a sensory cue was repeatedly followed by a behavioral event with fixed delays, pairs of premotor and primary motor neurons showed significant increases of coincident spikes at times a monkey was expecting the event. These results provided evidence that neuronal firing synchrony has predictive power. To elucidate the underlying mechanism, here we argue some nontrivial characteristics of the predictive synchronous firing developed by spike-timing-dependent plasticity in a paradigm similar to classical conditioning. We find that the computationally developed synchrony shows the modulations of temporal precision, which are quite similar to those observed experimentally. Thus, our model suggests that the important characteristics of predictive synchronous firing, which were previously attributed to an animal's higher cognitive function, can emerge from a synaptic-level mechanism.It is clearly advantageous for animals to detect and remember the causal relationships between events, to predict their occurrence and to prepare for a response (Barto et al. 1983;Desmond and Moore 1988: Tanji andShima 1994;Schultz et al. 1997;Schultz 1998). Recent studies show that rate changes and synchronous firing of cortical neurons differentially engage in these cognitive functions. Riehle et al. (1997) trained monkeys on a delayed response task in which a GO signal to instruct a motor response repeatedly followed a sensory cue at several possible, fixed delays. Spikes from pairs of premotor and primary motor (PM/MI) neurons coincided more frequently than expected by chance, particularly at those times when the monkeys were expecting the GO signal. If the GO signal was actually presented at one of those times, the synchronous firing was followed by rate modulations.

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Section: Dual Roles Of Stdp In the Predictive Synchronysupporting

confidence: 77%

“…The standard multiplicative STDP rule (Kistler and van Hemmen 2000;van Rossum et al 2000;Rubin et al 2001) was incapable of organizing predictive synchrony. The results, however, do not exclude the possible emergence of predictive synchrony under multiplicative-type rules.…”

Section: Discussionmentioning

confidence: 99%

Temporal Characteristics of the Predictive Synchronous Firing Modeled by Spike-Timing-Dependent Plasticity

Kitano

Fukai²

2004

Learn. Mem.

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“…Note that, due to the multiplicative [36] application of STDP temporal windows (Section 3.1), the weights are never strictly equal to w min or w max . Experiments show that they do not saturate toward extremal values.…”

Section: Article In Pressmentioning

confidence: 99%

“…Following [36], in order to avoid a saturation of the weights to the extremal values w min ¼ 0 and w max ¼ 1 (excitatory) or w max ¼ À1 (inhibitory), we apply a multiplicative learning rule, as stated in Eq. (4), where a is a positive learning rate.…”

Section: Synaptic Plasticitymentioning

confidence: 99%

Delay learning and polychronization for reservoir computing

Paugam-Moisy¹,

Martinez²,

Bengio³

2008

Neurocomputing

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We propose a multi-timescale learning rule for spiking neuron networks, in the line of the recently emerging field of reservoir computing. The reservoir is a network model of spiking neurons, with random topology and driven by STDP (spike-time-dependent plasticity), a temporal Hebbian unsupervised learning mode, biologically observed. The model is further driven by a supervised learning algorithm, based on a margin criterion, that affects the synaptic delays linking the network to the readout neurons, with classification as a goal task. The network processing and the resulting performance can be explained by the concept of polychronization, proposed by Izhikevich [Polychronization: computation with spikes, Neural Comput. 18(2) (2006) 245-282], on physiological grounds. The model emphasizes that polychronization can be used as a tool for exploiting the computational power of synaptic delays and for monitoring the topology and activity of a spiking neuron network. r

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“…Moreover, because of the strong competition, patterns in the synaptic distribution can emerge that do not reflect patterns of correlated activity in the input. On the other hand, the multiplicative model (Kistler and van Hemmen, 2000;van Rossum et al, 2000;Rubin et al, 2001) assumes linear attenuation of potentiating and depressing synaptic changes as the corresponding upper or lower boundary is approached. This model results in stable synaptic dynamics.…”

Section: Introductionmentioning

confidence: 99%

Learning Input Correlations through Nonlinear Temporally Asymmetric Hebbian Plasticity

Aharonov²,

et al. 2003

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Triggered by recent experimental results, temporally asymmetric Hebbian (TAH) plasticity is considered as a candidate model for the biological implementation of competitive synaptic learning, a key concept for the experience-based development of cortical circuitry. However, because of the well known positive feedback instability of correlation-based plasticity, the stability of the resulting learning process has remained a central problem. Plagued by either a runaway of the synaptic efficacies or a greatly reduced sensitivity to input correlations, the learning performance of current models is limited. Here we introduce a novel generalized nonlinear TAH learning rule that allows a balance between stability and sensitivity of learning. Using this rule, we study the capacity of the system to learn patterns of correlations between afferent spike trains. Specifically, we address the question of under which conditions learning induces spontaneous symmetry breaking and leads to inhomogeneous synaptic distributions that capture the structure of the input correlations. To study the efficiency of learning temporal relationships between afferent spike trains through TAH plasticity, we introduce a novel sensitivity measure that quantifies the amount of information about the correlation structure in the input, a learning rule capable of storing in the synaptic weights. We demonstrate that by adjusting the weight dependence of the synaptic changes in TAH plasticity, it is possible to enhance the synaptic representation of temporal input correlations while maintaining the system in a stable learning regime. Indeed, for a given distribution of inputs, the learning efficiency can be optimized.

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Equilibrium Properties of Temporally Asymmetric Hebbian Plasticity

Cited by 271 publications

References 7 publications

Temporal Characteristics of the Predictive Synchronous Firing Modeled by Spike-Timing-Dependent Plasticity

Temporal Characteristics of the Predictive Synchronous Firing Modeled by Spike-Timing-Dependent Plasticity

Delay learning and polychronization for reservoir computing

Learning Input Correlations through Nonlinear Temporally Asymmetric Hebbian Plasticity

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