Spike timing-dependent plasticity (STDP) as a Hebbian synaptic learning rule has been demonstrated in various neural circuits over a wide spectrum of species, from insects to humans. The dependence of synaptic modification on the order of pre- and postsynaptic spiking within a critical window of tens of milliseconds has profound functional implications. Over the past decade, significant progress has been made in understanding the cellular mechanisms of STDP at both excitatory and inhibitory synapses and of the associated changes in neuronal excitability and synaptic integration. Beyond the basic asymmetric window, recent studies have also revealed several layers of complexity in STDP, including its dependence on dendritic location, the nonlinear integration of synaptic modification induced by complex spike trains, and the modulation of STDP by inhibitory and neuromodulatory inputs. Finally, the functional consequences of STDP have been examined directly in an increasing number of neural circuits in vivo.

Spike timing-dependent plasticity: a Hebbian learning rule

https://itb.biologie.hu-berlin.de/~kempter/berlin_autumn_school/neuron/023.pdf

Spike Timing-Dependent Plasticity of Neural Circuits

Synaptic plasticity is a key component of the learning machinery in the brain. It is vital that such plasticity be tightly regulated so that it occurs to the proper extent at the proper time. Activity-dependent mechanisms that have been collectively termed metaplasticity have evolved to help implement these essential computational constraints. Various intercellular signalling molecules can trigger lasting changes in the ability of synapses to express plasticity; their mechanisms of action are reviewed here, along with a consideration of how metaplasticity might affect learning and clinical conditions.

Metaplasticity : tuning synapses and networks for plasticity

http://www2.neuroscience.umn.edu/eanwebsite/PDF%20GJClub/Neuron%2043%20729%202004.pdf

Neuronal Synchrony Mediated by Astrocytic Glutamate through Activation of Extrasynaptic NMDA Receptors

Most synaptic inputs are made onto the dendritic tree. Recent work has shown that dendrites play an active role in transforming synaptic input into neuronal output and in defining the relationships between active synapses. In this review, we discuss how these dendritic properties influence the rules governing the induction of synaptic plasticity. We argue that the location of synapses in the dendritic tree, and the type of dendritic excitability associated with each synapse, play decisive roles in determining the plastic properties of that synapse. Furthermore, since the electrical properties of the dendritic tree are not static, but can be altered by neuromodulators and by synaptic activity itself, we discuss how learning rules may be dynamically shaped by tuning dendritic function. We conclude by describing how this reciprocal relationship between plasticity of dendritic excitability and synaptic plasticity has changed our view of information processing and memory storage in neuronal networks.

Dendritic Excitability and Synaptic Plasticity

https://www.cell.com/neuron/pdf/S0896-6273(02)01189-3.pdf

Bidirectional Changes in Spatial Dendritic Integration Accompanying Long-Term Synaptic Modifications

Excitatory Crossmodal Input to a Widespread Population of Primary Sensory Cortical Neurons

Abstract Background The quality of learning and post-learning performances is critical for daily life. The behavioral flexibility is equally important for adapting the changing circumstances. The learning process requires repeated practices, which enhances prompt and proper behavioral responses, in turn, which promotes habits formation as well. Despite the well-documented sex differences in learning and performances, contradictory results were reported. A possible cause might be a systematic analysis due to specific research interests, regardless of the continuity of natural acquisition process. Here, we investigate the potential sex differences in learning, performances and adjustments of habited behaviors with regular and reversal Go/NoGo tasks. Methods Both male and female Sprague-Dawley rats were used in this study. All rats were trained for a regular rodent Go/NoGo task and a subset of rats were trained for a reversal rodent Go/NoGo task, both with strict elimination criteria. The behavioral performance data were stored in PC for off-line analysis. Multiple behavioral indices were analyzed for both passed and retired rats. Results The ability of learning the regular the reversal Go/NoGo tasks was similar for both male and female rats, however, the female rats took longer time to master the task principles in later stages for both tasks. In the regular Go/NoGo task, the female rats spent more time on completing the trial in performance optimization phases, which implied female rats were more cautious than male rats. Along with the progression of training, both male and female rats developed Go-preference strategies to perform the regular Go/NoGo task, which induced failure to meet the setting success criteria. The retired male rats exhibited shorter RTs and MTs than the retired female rats after developing Go-preference. Moreover, the time needed to complete the Go trials was significantly prolonged for male rats in the reversal Go/NoGo task. Conclusions Overall, we conclude that distinctive strategies were employed in performing Go/NoGo tasks for both male and female rats. Male rats required less time to stabilize the performance in behavioral optimization phase. In addition, male rats were more accurate in estimating time elapsing. In contrast, female rats took more cautious considerations in performing the task, through which minimal influences were manifested in the reversal version of task. 

/pdf/sex-differences-in-learning-and-performing-the-go-nogo-tasks-3eghzzgc.pdf

Zhiru Wang

Papers

Bidirectional Changes in Spatial Dendritic Integration Accompanying Long-Term Synaptic Modifications

Excitatory Crossmodal Input to a Widespread Population of Primary Sensory Cortical Neurons

Sex differences in learning and performing the Go/NoGo tasks