相关论文: A neuroeconomic theory of bidirectional synaptic p…

Because obesity is a risk factor for many serious illnesses such as diabetes, better understandings of obesity and eating disorders have been attracting attention in neurobiology, psychiatry, and neuroeconomics. This paper presents future…

Effective treatments that prevent or reduce drug relapse vulnerability should be developed to relieve the high burden of drug addiction to society. This will only be possible by enhancing the understanding of the molecular mechanisms…

Suicidal behavior is a leading cause of injury and death worldwide. Suicide has been associated with psychiatric illnesses such as depression and schizophrenia, as well as economic uncertainty, and social/cultural factors. This study…

Neuroeconomic conditions for "rational addiction" (Becker and Murphy, 1988) have been unknown. This paper derived the conditions for "rational addiction" by utilizing a nonlinear time-perception theory of "hyperbolic" discounting, which is…

Intertemporal choice has drawn attention in behavioral economics, econophysics, and neuroeconomics. Recent studies in mainstream economics have mainly focused on inconsistency in intertemporal choice (dynamic inconsistency); while…

We present an effective model for timing-dependent synaptic plasticity (STDP) in terms of two interacting traces, corresponding to the fraction of activated NMDA receptors and the Ca2+ concentration in the dendritic spine of the…

Modifications of signaling pathways and synapses owing to changing behaviors, environments, numerous neural modulation as well as brain-tissue injuries is defined as neuroplasticity in developmental neurology. The central purpose of the…

Substances of abuse are known to activate and disrupt neuronal circuits in the brain reward system. We propose a simple and easily interpretable dynamical systems model to describe the neurobiology of drug addiction that incorporates the…

In neuroscience, learning and memory are usually associated to long-term changes of neuronal connectivity. In this context, synaptic plasticity refers to the set of mechanisms driving the dynamics of neuronal connections, called {\em…

Neuroplasticity, the ability of the nervous system to adapt throughout an organism's lifespan, offers potential as both a biomarker and treatment target for neuropsychiatric conditions. Psychedelics, a burgeoning category of drugs, are…

Experiments show that spike-triggered stimulation performed with Bidirectional Brain-Computer-Interfaces (BBCI) can artificially strengthen connections between separate neural sites in motor cortex (MC). What are the neuronal mechanisms…

In neuroscience, synaptic plasticity refers to the set of mechanisms driving the dynamics of neuronal connections, called synapses and represented by a scalar value, the synaptic weight. A Spike-Timing Dependent Plasticity (STDP) rule is a…

Thought to be responsible for memory, synaptic plasticity has been widely studied in the past few decades. One example of plasticity models is the popular Spike Timing Dependent Plasticity (STDP). The huge litterature of STDP models are…

We investigate spike-timing dependent plasticity (STPD) in the case of a synapse connecting two neural cells. We develop a theoretical analysis of several STDP rules using Markovian theory. In this context there are two different…

Many mathematical models of synaptic plasticity have been proposed to explain the diversity of plasticity phenomena observed in biological organisms. These models range from simple interpretations of Hebb's postulate, which suggests that…

Dynamical criticality has been shown to enhance information processing in dynamical systems, and there is evidence for self-organized criticality in neural networks. A plausible mechanism for such self-organization is activity dependent…

General results from statistical learning theory suggest to understand not only brain computations, but also brain plasticity as probabilistic inference. But a model for that has been missing. We propose that inherently stochastic features…

The synaptic connectivity of cortical networks features an overrepresentation of certain wiring motifs compared to simple random-network models. This structure is shaped, in part, by synaptic plasticity that promotes or suppresses…

To learn useful dynamics on long time scales, neurons must use plasticity rules that account for long-term, circuit-wide effects of synaptic changes. In other words, neural circuits must solve a credit assignment problem to appropriately…

A mathematical model of a spiking neuron network accompanied by astrocytes is considered. The network is composed of excitatory and inhibitory neurons with synaptic connections supplied by a memristor-based model of plasticity. Another…