Spiking neurons, dopamine, and plasticity: Timing is everything, but concentration also matters

Thivierge, Jean-Philippe; Rivest, François; Monchi, Oury

doi:10.1002/syn.20378

Cited by 21 publications

(20 citation statements)

References 76 publications

(142 reference statements)

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“…. , n, is a time constant, g i is a leakage conductance, E i is a reversal potential, I tonic is a tonic current, I i is an external current (set to zero when purely spontaneous activity is considered), and w ij is a connection efficacy from cell i to cell j. K j is the excitatory potential of incoming spikes (Gütig and Sompolinsky, 2006;Thivierge et al, 2007) described by the following:…”

Section: Methodsmentioning

confidence: 99%

Nonperiodic Synchronization in Heterogeneous Networks of Spiking Neurons

Thivierge¹,

Cisek²

2008

J. Neurosci.

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Neural synchronization is of wide interest in neuroscience and has been argued to form the substrate for conscious attention to stimuli, movement preparation, and the maintenance of task-relevant representations in active memory. Despite a wealth of possible functions, the mechanisms underlying synchrony are still poorly understood. In particular, in vitro preparations have demonstrated synchronization with no apparent periodicity, which cannot be explained by simple oscillatory mechanisms. Here, we investigate the possible origins of nonperiodic synchronization through biophysical simulations. We show that such aperiodic synchronization arises naturally under a simple set of plausible assumptions, depending crucially on heterogeneous cell properties. In addition, nonperiodicity occurs even in the absence of stochastic fluctuation in membrane potential, suggesting that it may represent an intrinsic property of interconnected networks. Simulations capture some of the key aspects of population-level synchronization in spontaneous network spikes (NSs) and suggest that the intrinsic nonperiodicity of NSs observed in reduced cell preparations is a phenomenon that is highly robust and can be reproduced in simulations that involve a minimal set of realistic assumptions. In addition, a model with spike timing-dependent plasticity can overcome a natural tendency to exhibit nonperiodic behavior. After rhythmic stimulation, the model does not automatically fall back to a state of nonperiodic behavior, but keeps replaying the pattern of evoked NSs for a few cycles. A cluster analysis of synaptic strengths highlights the importance of population-wide interactions in generating this result and describes a possible route for encoding temporal patterns in networks of neurons.

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Section: Methodsmentioning

confidence: 99%

Nonperiodic Synchronization in Heterogeneous Networks of Spiking Neurons

Thivierge¹,

Cisek²

2008

J. Neurosci.

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“…D1 receptor activation in the striatum leads to activation of the Go cells of the direct pathway and the consequent initiation of movements, whereas D2 receptor activation inhibits the NoGo cells of the striatum leading to the suppression of movements. The GPi and the substantia nigra pars reticulata (SNr) target the thalamic nuclei projection to the frontal cortex Adapted from Frank et al 2005 Various reward-modulated plasticity models have been proposed (Florian 2007;Izhikevich 2007;Roberts et al 2008;Thivierge et al 2007). Most of these models use a form of synaptic modification known as spike timing dependent plasticity (STDP), which is based on the temporal relationship between pre-and postsynaptic activation (see Fig.…”

Section: Models Of the Effects Of Dopamine Releasementioning

confidence: 99%

“…In most reward-modulated STDP models, an RL component updates the synaptic weights based on a reward signal, eligibility trace and learning curve (Florian 2007;Izhikevich 2007;Roberts et al 2008;Thivierge et al 2007). A decaying eligibility trace determines which synapses are potentiated by DA and by how much depending on the size of the time interval between the stimulus and the reward (in this case, the time interval between the preand postsynaptic activations).…”

Section: Models Of the Effects Of Dopamine Releasementioning

confidence: 99%

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Computational models of reinforcement learning: the role of dopamine as a reward signal

2010

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Reinforcement learning is ubiquitous. Unlike other forms of learning, it involves the processing of fast yet content-poor feedback information to correct assumptions about the nature of a task or of a set of stimuli. This feedback information is often delivered as generic rewards or punishments, and has little to do with the stimulus features to be learned. How can such low-content feedback lead to such an efficient learning paradigm? Through a review of existing neuro-computational models of reinforcement learning, we suggest that the efficiency of this type of learning resides in the dynamic and synergistic cooperation of brain systems that use different levels of computations. The implementation of reward signals at the synaptic, cellular, network and system levels give the organism the necessary robustness, adaptability and processing speed required for evolutionary and behavioral success.

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“…In cortex, neural heterogeneity in found in the morphology, functional properties, intrinsic biophysical properties, and connectivity of neurons (particularly interneurons, Buzsaki et al 2004). As a consequence of heterogeneity, neural circuits produce intricate forms of neural activity, giving patterns of spikes an intrinsically "noisy" appearance (Thivierge et al 2007;. Here, we suggest that the intricate patterns of activity produced in heterogeneous circuits play a central role in generating transient yet highly precise responses to temporal patterns.…”

Section: Introductionmentioning

confidence: 96%

Spiking neurons that keep the rhythm

Thivierge

Cisek

2010

J Comput Neurosci

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Detecting the temporal relationship among events in the environment is a fundamental goal of the brain. Following pulses of rhythmic stimuli, neurons of the retina and cortex produce activity that closely approximates the timing of an omitted pulse. This omitted stimulus response (OSR) is generally interpreted as a transient response to rhythmic input and is thought to form a basis of short-term perceptual memories. Despite its ubiquity across species and experimental protocols, the mechanisms underlying OSRs remain poorly understood. In particular, the highly transient nature of OSRs, typically limited to a single cycle after stimulation, cannot be explained by a simple mechanism that would remain locked to the frequency of stimulation. Here, we describe a set of realistic simulations that capture OSRs over a range of stimulation frequencies matching experimental work. The model does not require an explicit mechanism for learning temporal sequences. Instead, it relies on spike timing-dependent plasticity (STDP), a form of synaptic modification that is sensitive to the timing of pre- and post-synaptic action potentials. In the model, the transient nature of OSRs is attributed to the heterogeneous nature of neural properties and connections, creating intricate forms of activity that are continuously changing over time. Combined with STDP, neural heterogeneity enabled OSRs to complex rhythmic patterns as well as OSRs following a delay period. These results link the response of neurons to rhythmic patterns with the capacity of heterogeneous circuits to produce transient and highly flexible forms of neural activity.

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Spiking neurons, dopamine, and plasticity: Timing is everything, but concentration also matters

Cited by 21 publications

References 76 publications

Nonperiodic Synchronization in Heterogeneous Networks of Spiking Neurons

Nonperiodic Synchronization in Heterogeneous Networks of Spiking Neurons

Computational models of reinforcement learning: the role of dopamine as a reward signal

Spiking neurons that keep the rhythm

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