Pre- and postsynaptically expressed spike-timing-dependent plasticity contribute differentially to neuronal learning

A plethora of experimental studies have shown that long-term synaptic plasticity can be expressed pre- or postsynaptically depending on a range of factors such as developmental stage, synapse type, and activity patterns. The functional consequences of this diversity are not clear, although it is understood that whereas postsynaptic expression of plasticity predominantly affects synaptic response amplitude, presynaptic expression alters both synaptic response amplitude and short-term dynamics. In most models of neuronal learning, long-term synaptic plasticity is implemented as changes in connective weights. The consideration of long-term plasticity as a fixed change in amplitude corresponds more closely to post- than to presynaptic expression, which means theoretical outcomes based on this choice of implementation may have a postsynaptic bias. To explore the functional implications of the diversity of expression of long-term synaptic plasticity, we adapted a model of long-term plasticity, more specifically spike-timing-dependent plasticity (STDP), such that it was expressed either independently pre- or postsynaptically, or in a mixture of both ways. We compared pair-based standard STDP models and a biologically tuned triplet STDP model, and investigated the outcomes in a minimal setting, using two different learning schemes: in the first, inputs were triggered at different latencies, and in the second a subset of inputs were temporally correlated. We found that presynaptic changes adjusted the speed of learning, while postsynaptic expression was more efficient at regulating spike timing and frequency. When combining both expression loci, postsynaptic changes amplified the response range, while presynaptic plasticity allowed control over postsynaptic firing rates, potentially providing a form of activity homeostasis. Our findings highlight how the seemingly innocuous choice of implementing synaptic plasticity by single weight modification may unwittingly introduce a postsynaptic bias in modelling outcomes. We conclude that pre- and postsynaptically expressed plasticity are not interchangeable, but enable complimentary functions.

A synaptic novelty signal in the dentate gyrus supports switching hippocampal attractor networks from generalization to discrimination

Episodic memory formation and recall are complementary processes that put conflicting requirements on neuronal computations in the hippocampus. How this challenge is resolved in hippocampal circuits is unclear. To address this question, we obtained in vivo whole-cell patch-clamp recordings from dentate gyrus granule cells in head-fixed mice navigating in familiar and novel virtual environments. We find that granule cells consistently show a small transient depolarization of their membrane potential upon transition to a novel environment. This synaptic novelty signal is sensitive to local application of atropine, indicating that it depends on metabotropic acetylcholine receptors. A computational model suggests that the observed transient synaptic response to novel environments may lead to a bias in the granule cell population activity, which can in turn drive the downstream attractor networks to a new state, thereby favoring the switch from generalization to discrimination when faced with novelty. Such a novelty-driven cholinergic switch may enable flexible encoding of new memories while preserving stable retrieval of familiar ones.

Synaptic Dynamics of the Feed-forward Inhibitory Circuitry Gating Mechanical Allodynia in Mice

Background The authors’ previous studies have found that spinal protein kinase C γ expressing neurons are involved in the feed-forward inhibitory circuit gating mechanical allodynia in the superficial dorsal horn. The authors hypothesize that nerve injury enhances the excitability of spinal protein kinase C γ expressing interneurons due to disinhibition of the feed-forward inhibitory circuit, and enables Aβ primary inputs to activate spinal protein kinase C γ expressing interneurons. Methods Prkcg-P2A-tdTomato mice were constructed using the clustered regularly interspaced short palindromic repeats and clustered regularly interspaced short palindromic repeats-associated nuclease 9 technology, and were used to analyze the electrophysiologic properties of spinal protein kinase C γ expressing neurons in both normal conditions and pathologic conditions induced by chronic constriction injury of the sciatic nerve. Patch-clamp whole cell recordings were used to identify the nature of the dynamic synaptic drive to protein kinase C γ expressing neurons. Results Aβ fiber stimulation evoked a biphasic synaptic response in 42% (31 of 73) of protein kinase C γ expressing neurons. The inhibitory components of the biphasic synaptic response were blocked by both strychnine and bicuculline in 57% (16 of 28) of neurons. Toll-like receptor 5 immunoreactive fibers made close contact with protein kinase C γ expressing neurons. After nerve injury, the percentage of neurons double-labeled for c-fos and Prkcg-P2A-tdTomato in animals walking on a rotarod was significantly higher than that in the nerve injury animals (4.1% vs. 9.9%, 22 of 539 vs. 54 of 548,P < 0.001). Aβ fiber stimulation evoked burst action potentials in 25.8% (8 of 31) of protein kinase C γ expressing neurons in control animals, while the proportion increased to 51.1% (23 of 45) in nerve injury animals (P = 0.027). Conclusions The Prkcg-P2A-tdTomato mice the authors constructed provide a useful tool for further analysis on how the spinal allodynia gate works. The current study indicated that nerve injury enhanced the excitability of spinal protein kinase C γ expressing interneurons due to disinhibition of the feed-forward inhibitory circuit, and enabled Aβ primary inputs to activate spinal protein kinase C γ expressing interneurons. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

Controllable digital resistive switching for artificial synapses and pavlovian learning algorithm

Synaptic response has been enhanced by 340 times by geometrical modulation of a ZnO-based memristor. The device showed a variety of comprehensive synaptic functions, including the Pavlovian associative learning process in the human brain.