Experimental and computational aspects of signaling mechanisms of spike-timing-dependent plasticity

The critical balance between dopamine D2 receptor and RGS for the sensitive detection of a transient decay in dopamine signal

In behavioral learning, reward-related events are encoded into phasic dopamine (DA) signals in the brain. In particular, unexpected reward omission leads to a phasic decrease in DA (DA dip) in the striatum, which triggers long-term potentiation (LTP) in DA D2 receptor (D2R)-expressing spiny-projection neurons (D2 SPNs). While this LTP is required for reward discrimination, it is unclear how such a short DA-dip signal (0.5-2 s) is transferred through intracellular signaling to the coincidence detector, adenylate cyclase (AC). In the present study, we built a computational model of D2 signaling to determine conditions for the DA-dip detection. The DA dip can be detected only if the basal DA signal sufficiently inhibits AC, and the DA-dip signal sufficiently disinhibits AC. We found that those two requirements were simultaneously satisfied only if two key molecules, D2R and regulators of G protein signaling (RGS) were balanced within a certain range; this balance has indeed been observed in experimental studies. We also found that high level of RGS was required for the detection of a 0.5-s short DA dip, and the analytical solutions for these requirements confirmed their universality. The imbalance between D2R and RGS is associated with schizophrenia and DYT1 dystonia, both of which are accompanied by abnormal striatal LTP. Our simulations suggest that D2 SPNs in patients with schizophrenia and DYT1 dystonia cannot detect short DA dips. We finally discussed that such psychiatric and movement disorders can be understood in terms of the imbalance between D2R and RGS.

50 Years Since the Marr, Ito, and Albus Models of the Cerebellum

Fifty years have passed since David Marr, Masao Ito, and James Albus proposed seminal models of cerebellar functions. These models share the essential concept that parallel-fiber-Purkinje-cell synapses undergo plastic changes, guided by climbing-fiber activities during sensorimotor learning. However, they differ in several important respects, including holistic versus complementary roles of the cerebellum, pattern recognition versus control as computational objectives, potentiation versus depression of synaptic plasticity, teaching signals versus error signals transmitted by climbing-fibers, sparse expansion coding by granule cells, and cerebellar internal models. In this review, we evaluate different features of the three models based on recent computational and experimental studies. While acknowledging that the three models have greatly advanced our understanding of cerebellar control mechanisms in eye movements and classical conditioning, we propose a new direction for computational frameworks of the cerebellum, that is, hierarchical reinforcement learning with multiple internal models.

In Vivo Decoding Mechanisms of the Temporal Patterns of Blood Insulin by the Insulin-AKT Pathway in the Liver

Cells respond to various extracellular stimuli through a limited number of signaling pathways. One strategy to process such stimuli is to code the information into the temporal patterns of molecules. Although we showed that insulin selectively regulated molecules depending on its temporal patterns using Fao cells, the in vivo mechanism remains unknown. Here, we show how the insulin-AKT pathway processes the information encoded into the temporal patterns of blood insulin. We performed hyperinsulinemic-euglycemic clamp experiments and found that, in the liver, all temporal patterns of insulin are encoded into the insulin receptor, and downstream molecules selectively decode them through AKT. S6K selectively decodes the additional secretion information. G6Pase interprets the basal secretion information through FoxO1, while GSK3β decodes all secretion pattern information. Mathematical modeling revealed the mechanism via differences in network structures and from sensitivity and time constants. Given that almost all hormones exhibit distinct temporal patterns, temporal coding may be a general principle of system homeostasis by hormones.

Challenges in Reproducibility, Replicability, and Comparability of Computational Models and Tools for Neuronal and Glial Networks, Cells, and Subcellular Structures

The possibility to replicate and reproduce published research results is one of the biggest challenges in all areas of science. In computational neuroscience, there are thousands of models available. However, it is rarely possible to reimplement the models based on the information in the original publication, let alone rerun the models just because the model implementations have not been made publicly available. We evaluate and discuss the comparability of a versatile choice of simulation tools: tools for biochemical reactions and spiking neuronal networks, and relatively new tools for growth in cell cultures. The replicability and reproducibility issues are considered for computational models that are equally diverse, including the models for intracellular signal transduction of neurons and glial cells, in addition to single glial cells, neuron-glia interactions, and selected examples of spiking neuronal networks. We also address the comparability of the simulation results with one another to comprehend if the studied models can be used to answer similar research questions. In addition to presenting the challenges in reproducibility and replicability of published results in computational neuroscience, we highlight the need for developing recommendations and good practices for publishing simulation tools and computational models. Model validation and flexible model description must be an integral part of the tool used to simulate and develop computational models. Constant improvement on experimental techniques and recording protocols leads to increasing knowledge about the biophysical mechanisms in neural systems. This poses new challenges for computational neuroscience: extended or completely new computational methods and models may be required. Careful evaluation and categorization of the existing models and tools provide a foundation for these future needs, for constructing multiscale models or extending the models to incorporate additional or more detailed biophysical mechanisms. Improving the quality of publications in computational neuroscience, enabling progressive building of advanced computational models and tools, can be achieved only through adopting publishing standards which underline replicability and reproducibility of research results.

A common framework of signal processing in the induction of cerebellar LTD and cortical STDP

Cerebellar long-term depression (LTD) and cortical spike-timing-dependent synaptic plasticity (STDP) are two well-known and well-characterized types of synaptic plasticity. Induction of both types of synaptic plasticity depends on the spike timing, pairing frequency, and pairing numbers of two different sources of spiking. This implies that the induction of synaptic plasticity may share common frameworks in terms of signal processing regardless of the different signaling pathways involved in the two types of synaptic plasticity. Here we propose that both types share common frameworks of signal processing for spike-timing, pairing-frequency, and pairing-numbers detection. We developed system models of both types of synaptic plasticity and analyzed signal processing in the induction of synaptic plasticity. We found that both systems have upstream subsystems for spike-timing detection and downstream subsystems for pairing-frequency and pairing-numbers detection. The upstream systems used multiplication of signals from the feedback filters and nonlinear functions for spike-timing detection. The downstream subsystems used temporal filters with longer time constants for pairing-frequency detection and nonlinear switch-like functions for pairing-numbers detection, indicating that the downstream subsystems serve as a leaky integrate-and-fire system. Thus, our findings suggest that a common conceptual framework for the induction of synaptic plasticity exists despite the differences in molecular species and pathways.

An evaluation of neuroplasticity and behavior after deep brain stimulation of the nucleus accumbens in an animal model of depression

BACKGROUND

Recent interest has demonstrated the nucleus accumbens (NAcc) as a potential target for the treatment of depression with deep brain stimulation (DBS).

OBJECTIVE

To demonstrate that DBS of the NAcc is an effective treatment modality for depression and that chemical and structural changes associated with these behavioral changes are markers of neuroplasticity.

METHODS

A deep brain stimulator was placed in the NAcc of male Wistar-Kyoto rats. Groups were divided into sham (no stimulation), intermittent (3 h/d for 2 weeks), or continuous (constant stimulation for 2 weeks). Exploratory and anxietylike behaviors were evaluated with the open-field test before and after stimulation. Tissue samples of the prefrontal cortex (PFC) were processed with Western blot analysis of markers of noradrenergic activity that included the noradrenergic synthesizing enzyme tyrosine hydroxylase. Analysis of tissue levels for catecholamines was achieved with high-performance liquid chromatography. Morphological properties of cortical pyramidal neurons were assessed with Golgi-Cox staining.

RESULTS

Subjects undergoing intermittent and continuous stimulation of the NAcc exhibited an increase in exploratory behavior and reduced anxietylike behaviors. Tyrosine hydroxylase expression levels were decreased in the PFC after intermittent and continuous DBS, and dopamine and norepinephrine levels were decreased after continuous stimulation. Golgi-Cox staining indicated that DBS increased the length of apical and basilar dendrites in pyramidal neurons of the PFC.

CONCLUSION

Deep brain stimulation induces behavioral improvement in and neurochemical and morphological alterations of the PFC that demonstrate changes within the circuitry of the brain different from the target area of stimulation. This observed dendritic plasticity may underlie the therapeutic efficacy of this treatment.

Cerebellar supervised learning revisited: biophysical modeling and degrees-of-freedom control

The biophysical models of spike-timing-dependent plasticity have explored dynamics with molecular basis for such computational concepts as coincidence detection, synaptic eligibility trace, and Hebbian learning. They overall support different learning algorithms in different brain areas, especially supervised learning in the cerebellum. Because a single spine is physically very small, chemical reactions at it are essentially stochastic, and thus sensitivity-longevity dilemma exists in the synaptic memory. Here, the cascade of excitable and bistable dynamics is proposed to overcome this difficulty. All kinds of learning algorithms in different brain regions confront with difficult generalization problems. For resolution of this issue, the control of the degrees-of-freedom can be realized by changing synchronicity of neural firing. Especially, for cerebellar supervised learning, the triangle closed-loop circuit consisting of Purkinje cells, the inferior olive nucleus, and the cerebellar nucleus is proposed as a circuit to optimally control synchronous firing and degrees-of-freedom in learning.

Analysis of development of direction selectivity in retinotectum by a neural circuit model with spike timing-dependent plasticity

The development of direction selectivity in the visual system depends on visual experience. In the developing Xenopus retinotectal system, tectal neurons (TNs) become direction selective through spike timing-dependent plasticity (STDP) after repetitive retinal exposure to a moving bar in a specific direction. We investigated the mechanism responsible for the development of direction selectivity in the Xenopus retinotectal system using a neural circuit model with STDP. In this retinotectal circuit model, a moving bar stimulated the retinal ganglion cells (RGCs), which provided feedforward excitation to the TNs and interneurons (INs). The INs provided delayed feedforward inhibition to the TNs. The TNs also received feedback excitation from neighboring TNs. As a synaptic learning rule, a molecular STDP model was used for synapses between the RGCs and TNs. The retinotectal circuit model reproduced experimentally observed features of the development of direction selectivity, such as increase in input to the TN. The peak of feedforward excitation from RGCs to TNs shifted earlier as a result of STDP. Together with the delayed feedforward inhibition, a stronger earlier transient feedforward signal was generated, which exceeded the threshold of the feedback excitation from the neighboring TNs and resulted in amplification of input to the TN. The suppression of the delayed feedforward inhibition resulted in the development of orientation selectivity rather than direction selectivity, indicating the pivotal role of the delayed feedforward inhibition in direction selectivity. We propose a mechanism for the development of direction selectivity involving a delayed feedforward inhibition with STDP and the amplification of feedback excitation.

Postsynaptic signal transduction models for long-term potentiation and depression

More than a hundred biochemical species, activated by neurotransmitters binding to transmembrane receptors, are important in long-term potentiation (LTP) and long-term depression (LTD). To investigate which species and interactions are critical for synaptic plasticity, many computational postsynaptic signal transduction models have been developed. The models range from simple models with a single reversible reaction to detailed models with several hundred kinetic reactions. In this study, more than a hundred models are reviewed, and their features are compared and contrasted so that similarities and differences are more readily apparent. The models are classified according to the type of synaptic plasticity that is modeled (LTP or LTD) and whether they include diffusion or electrophysiological phenomena. Other characteristics that discriminate the models include the phase of synaptic plasticity modeled (induction, expression, or maintenance) and the simulation method used (deterministic or stochastic). We find that models are becoming increasingly sophisticated, by including stochastic properties, integrating with electrophysiological properties of entire neurons, or incorporating diffusion of signaling molecules. Simpler models continue to be developed because they are computationally efficient and allow theoretical analysis. The more complex models permit investigation of mechanisms underlying specific properties and experimental verification of model predictions. Nonetheless, it is difficult to fully comprehend the evolution of these models because (1) several models are not described in detail in the publications, (2) only a few models are provided in existing model databases, and (3) comparison to previous models is lacking. We conclude that the value of these models for understanding molecular mechanisms of synaptic plasticity is increasing and will be enhanced further with more complete descriptions and sharing of the published models.