Dopamine-enabled anti-Hebbian timing-dependent plasticity in prefrontal circuitry

Ruan, Hongyu; Saur, Taixiang; Yao, Wei‐Dong

doi:10.3389/fncir.2014.00038

Cited by 39 publications

(36 citation statements)

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“…Dopamine is an important neuromodulator and plays a critical role in learning and memory processes [46]. Here, inspired by the DA-STDP found in different brain areas, such as the hippocampus and prefrontal cortex [30,31,32] (Figure 1), we hypothesized that DA can modulate changes in synaptic weights according to the DA-STDP rule during the SL process. The weights increment ∆W under the phenomenological model of DA-STDP can be expressed as [30,32]:…”

Section: Network Architecture and Neuronal Dynamicsmentioning

confidence: 99%

“…We introduced the dopamine-modulated STDP (DA-STDP) rule, a new type of symmetric STDP (sym-STDP), for pattern recognition. The DA-STDP rule has been observed in several different experiments in the hippocampus and prefrontal cortex [30,31,32], where the modification of synaptic weight is always incremental if the interval between the pre-and post-synaptic spike activities is within a narrow time-window when dopamine (DA) is present. The differences between the sym-or DA-STDP and classic STDP rules are shown in Figure 1 [4,30,32].…”

Section: Introductionmentioning

confidence: 99%

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A biologically plausible supervised learning method for spiking neural networks using the symmetric STDP rule

et al. 2020

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Spiking neural networks (SNNs) possess energy-efficient potential due to eventbased computation. However, supervised training of SNNs remains a challenge as spike activities are non-differentiable. Previous SNNs training methods can be generally categorized into two basic classes, i.e., backpropagation-like training methods and plasticity-based learning methods. The former methods are dependent on energy-inefficient real-valued computation and non-local transmission, as also required in artificial neural networks (ANNs), whereas the latter are either considered to be biologically implausible or exhibit poor performance.Hence, biologically plausible (bio-plausible) high-performance supervised learning (SL) methods for SNNs remain deficient. In this paper, we proposed a novel bio-plausible SNN model for SL based on the symmetric spike-timing dependent plasticity (sym-STDP) rule found in neuroscience. By combining the sym-STDP rule with bio-plausible synaptic scaling and intrinsic plasticity of the dynamic threshold, our SNN model implemented SL well and achieved good performance in the benchmark recognition task (MNIST dataset). To reveal the underlying mechanism of our SL model, we visualized both layer-based * These two authors contributed equally to this work.activities and synaptic weights using the t-distributed stochastic neighbor embedding (t-SNE) method after training and found that they were well clustered, thereby demonstrating excellent classification ability. Furthermore, to verify the robustness of our model, we trained it on another more realistic dataset (Fashion-MNIST), which also showed good performance. As the learning rules were bio-plausible and based purely on local spike events, our model could be easily applied to neuromorphic hardware for online training and may be helpful for understanding SL information processing at the synaptic level in biological neural systems.

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Section: Network Architecture and Neuronal Dynamicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A biologically plausible supervised learning method for spiking neural networks using the symmetric STDP rule

et al. 2020

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“…It is known to give rise to excitatory-inhibitory balance and has been implicated as the mechanism behind many experimental findings such as sparse firing rates in cortex 28 . Similarly, anti-Hebbian plasticity exists across many brain areas and species, such as salamander and rabbit retina 29 , rat hippocampus 48,49 electric fish electrosensory lobe 50 and mouse prefrontal cortex 51 . Anti-hebbian dynamics can give rise to sparse neural codes which decrease correlations between neural activity and increase overall stimulus representation in the network 52 .…”

Section: Discussionmentioning

confidence: 99%

Achieving stable dynamics in neural circuits

Kozachkov

Lundqvist

Slotine

et al. 2020

Preprint

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10The brain consists of many interconnected networks with time-varying, partially 11 autonomous activity. There are multiple sources of noise and variation yet activity has to 12 eventually converge to a stable, reproducible state (or sequence of states) for its 13 computations to make sense. We approached this problem from a control-theory 14 perspective by applying contraction analysis to recurrent neural networks. This allowed 15 us to find mechanisms for achieving stability in multiple connected networks with 16 biologically realistic dynamics, including synaptic plasticity and time-varying inputs. These 17 mechanisms included inhibitory Hebbian plasticity, excitatory anti-Hebbian plasticity, 18 synaptic sparsity and excitatory-inhibitory balance. Our findings shed light on how stable 19 computations might be achieved despite biological complexity.20 21 2 25often vary between identical trials 1,2 . This can be due to a variety of factors including 26 variability in membrane potentials, inputs, plastic changes due to recent experience and 27 so on. Yet, in spite of these fluctuations, brain networks must achieve computational 28 stability: despite being "knocked around" by plasticity and noise, the behavioral output of 29 the brain on two experimentally identical trials needs to be similar. How is this stability 30 achieved? 31Stability has played a central role in computational neuroscience since the 1980's, 32 with the advent of models of associative memory that stored neural activation patterns as 33 stable point attractors 3-7 , although researchers were thinking about the brain's stability 34 since as early as the 1950's 8 . The vast majority of this work is concerned with the stability 35 of activity around points, lines, or planes in neural state space 9,10 . However, recent 36 neurophysiological studies have revealed that in many cases, single-trial neural activity 37 is highly dynamic, and therefore potentially inconsistent with a static attractor viewpoint 38 1,11 . Consequently, there has been a number of recent studies-both computational and 39 experimental-which focus more broadly on the stability of neural trajectories 12,13 . 40 While these studies provide important empirical results and intuitions, they do not 41 offer analytical insight into mechanisms for achieving stable trajectories in recurrent 42 neural networks. Nor do they offer insights into achieving such stability in plastic (or multi-43 modal) networks. Here we focus on finding conditions that guarantee stable trajectories 44 in recurrent neural networks and thus shed light onto how stable trajectories might be 45 achieved in vivo. 48 the population activity of a contracting network will converge towards the same trajectory, 49 thus achieving stable dynamics (Figure 1). One way to understand contraction is to 50 represent the state of a network at a given time as a point in the network's 'state-space', 51 for instance the space spanned by the possible firing rates of all the networks' neurons. 52 This state-space has the same num...

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“…Inhibition of the sAHP facilitates the expression of LTP by enhancing NMDA conductance and reducing the threshold for LTP expression (Cohen et al, 1999; Kramár et al, 2004; Sah and Bekkers, 1996). Furthermore, inhibition of the sAHP gates the induction of spike-timing dependent plasticity (STDP) (Zaitsev and Anwyl, 2012), facilitating LTP by extending the temporal window for its induction (Fuenzalida et al, 2007; Ruan et al, 2014; Xu and Yao, 2010; Zhang et al, 2009), ultimately promoting the consolidation of memories (Cohen-Matsliah et al, 2010; Thompson et al, 1996). …”

Section: Plasticity In Intrinsic Excitability As a Cellular Mechanmentioning

confidence: 99%

Chronic cocaine disrupts mesocortical learning mechanisms

Buchta

Riegel

2015

Brain Research

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The addictive power of drugs of abuse such as cocaine comes from their ability to hijack natural reward and plasticity mechanisms mediated by dopamine signaling in the brain. Reward learning involves burst firing of midbrain dopamine neurons in response to rewards and cues predictive of reward. The resulting release of dopamine in terminal regions is thought to act as a teaching signaling to areas such as the prefrontal cortex and striatum. In this review, we posit that a pool of extrasynaptic dopaminergic D1-like receptors activated in response to dopamine neuron burst firing serve to enable synaptic plasticity in the prefrontal cortex in response to rewards and their cues. We propose that disruptions in these mechanisms following chronic cocaine use contribute to addiction pathology, in part due to the unique architecture of the mesocortical pathway. By blocking dopamine reuptake in the cortex, cocaine elevates dopamine signaling at these extra-synaptic receptors, prolonging D1-receptor activation and the subsequent activation of intracellular signaling cascades, and thus inducing long-lasting maladaptive plasticity. These cellular adaptations may account for many of the changes in cortical function observed in drug addicts, including an enduring vulnerability to relapse. Therefore, understanding and targeting these neuroadaptations may provide cognitive benefits and help prevent relapse in human drug addicts.

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Dopamine-enabled anti-Hebbian timing-dependent plasticity in prefrontal circuitry

Cited by 39 publications

References 65 publications

A biologically plausible supervised learning method for spiking neural networks using the symmetric STDP rule

A biologically plausible supervised learning method for spiking neural networks using the symmetric STDP rule

Achieving stable dynamics in neural circuits

Chronic cocaine disrupts mesocortical learning mechanisms

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