Cerebrocortical injuries, such as stroke, are a major source of disability. Maladaptive consequences can result from post-injury local reorganization of cortical circuits. For example, epilepsy is a common sequela of cortical stroke, yet mechanisms responsible for seizures following cortical injuries remain unknown. In addition to local reorganization, long-range, extra-cortical connections might be critical for seizure maintenance. Here we report in rats the first evidence that the thalamus – a structure remote from but connected to the injured cortex – is required to maintain cortical seizures. Thalamocortical neurons connected to the injured epileptic cortex undergo changes in HCN channel expression and become hyperexcitable. Targeting these neurons with a closed-loop optogenetic strategy demonstrates that reducing their activity in real-time is sufficient to immediately interrupt electrographic and behavioral seizures. This approach is of therapeutic interest for intractable epilepsy, since it spares cortical function between seizures, in contrast to existing treatments such as surgical lesioning or drugs.

Key pointsr Although learning can arise from few or even a single trial, synaptic plasticity is commonly assessed under prolonged activation. Here, we explored the existence of rapid responsiveness of synaptic plasticity at corticostriatal synapses in a major synaptic learning rule, spike-timing-dependent plasticity (STDP).r We found that spike-timing-dependent depression (tLTD) progressively disappears when the number of paired stimulations (below 50 pairings) is decreased whereas spike-timing-dependent potentiation (tLTP) displays a biphasic profile: tLTP is observed for 75-100 pairings, is absent for 25-50 pairings and re-emerges for 5-10 pairings. r Endocannabinoid-tLTP may represent a physiological mechanism operating during the rapid learning of new associative memories and behavioural rules characterizing the flexible behaviour of mammals or during the initial stages of habit learning.Abstract Synaptic plasticity, a main substrate for learning and memory, is commonly assessed with prolonged stimulations. Since learning can arise from few or even a single trial, synaptic strength is expected to adapt rapidly. However, whether synaptic plasticity occurs in response to limited event occurrences remains elusive. To answer this question, we investigated whether a low number of paired stimulations can induce plasticity in a major synaptic learning rule, spike-timing-dependent plasticity (STDP). It is known that 100 pairings induce bidirectional STDP, i.e. spike-timing-dependent potentiation (tLTP) and depression (tLTD) at most central synapses. In rodent striatum, we found that tLTD progressively disappears when the number of paired stimulations is decreased (below 50 pairings) whereas tLTP displays a biphasic profile: tLTP is observed for 75-100 pairings, absent for 25-50 pairings and re-emerges for 5-10 pairings. This tLTP, induced by very few pairings (ß5-10) depends on the endocannabinoid (eCB) system. This eCB-dependent tLTP (eCB-tLTP) involves postsynaptic endocannabinoid synthesis, requires paired activity (post-and presynaptic) and the activation of type-1 cannabinoid receptor (CB1R) and transient receptor potential vanilloid type-1 (TRPV1). eCB-tLTP occurs in both striatopallidal and striatonigral medium-sized spiny neurons (MSNs) and is dopamine dependent. Lastly, we show that eCB-LTP and eCB-LTD can be induced sequentially in the same neuron, depending on the cellular conditioning protocol. Thus, while endocannabinoids are usually thought simply to depress synaptic function, they also constitute a versatile system underlying bidirectional plasticity. Our results reveal a novel form of synaptic plasticity, eCB-tLTP, which may underlie rapid learning capabilities characterizing behavioural flexibility.

Synaptic plasticity is a cardinal cellular mechanism for learning and memory. The endocannabinoid (eCB) system has emerged as a pivotal pathway for synaptic plasticity because of its widely characterized ability to depress synaptic transmission on short- and long-term scales. Recent reports indicate that eCBs also mediate potentiation of the synapse. However, it is not known how eCB signaling may support bidirectionality. Here, we combined electrophysiology experiments with mathematical modeling to question the mechanisms of eCB bidirectionality in spike-timing dependent plasticity (STDP) at corticostriatal synapses. We demonstrate that STDP outcome is controlled by eCB levels and dynamics: prolonged and moderate levels of eCB lead to eCB-mediated long-term depression (eCB-tLTD) while short and large eCB transients produce eCB-mediated long-term potentiation (eCB-tLTP). Moreover, we show that eCB-tLTD requires active calcineurin whereas eCB-tLTP necessitates the activity of presynaptic PKA. Therefore, just like glutamate or GABA, eCB form a bidirectional system to encode learning and memory.DOI: http://dx.doi.org/10.7554/eLife.13185.001

Synaptic plasticity is classically considered as the neuronal substrate for learning and memory. However, activity-dependent changes in neuronal intrinsic excitability have been reported in several learning-related brain regions, suggesting that intrinsic plasticity could also participate to information storage. Compared to synaptic plasticity, there has been little exploration of the properties of induction and expression of intrinsic plasticity in an intact brain. Here, by the means of in vivo intracellular recordings in the rat we have examined how the intrinsic excitability of layer V motor cortex pyramidal neurones is altered following brief periods of repeated firing. Changes in membrane excitability were assessed by modifications in the discharge frequency versus injected current (F-I) curves. Most (∼64%) conditioned neurones exhibited a long-lasting intrinsic plasticity, which was expressed either by selective changes in the current threshold or in the slope of the F-I curve, or by concomitant changes in both parameters. These modifications in the neuronal input-output relationship led to a global increase or decrease in intrinsic excitability. Passive electrical membrane properties were unaffected by the intracellular conditioning, indicating that intrinsic plasticity resulted from modifications of voltage-gated ion channels. These results demonstrate that neocortical pyramidal neurones can express in vivo a bidirectional use-dependent intrinsic plasticity, modifying their sensitivity to weak inputs and/or the gain of their input-output function. These multiple forms of experience-dependent intrinsic changes, which expand the computational abilities of individual neurones, could shape new network dynamics and thus might participate in the formation of mnemonic motor engrams.

In vivo intracellular recordings were performed from striatal output neurones (SONs) ( n= 34) to test the role of their intrinsic membrane properties in the temporal integration of excitatory cortical synaptic inputs. In a first series of experiments, intracellular injection of a test depolarising current pulse was preceded by a 200 ms suprathreshold prepulse, the two pulses having the same intensity. An increase in intrinsic excitability was observed as a decrease (55 ± 21 ms, n= 13) (mean ± s.d.) in latency to the first action potential of the test response compared to the prepulse response. This value decayed exponentially as a function of the time interval between the current pulses (τ= 364 ± 37 ms, n= 5). The voltage response of SONs was not modified by a prepulse that induced a membrane depolarisation < −62 mV. The effect of the suprathreshold prepulse was tested on monosynaptic cortically evoked excitatory postsynaptic potentials (EPSPs). The ability to induce suprathreshold EPSPs was markedly increased by the prior depolarisation ( n= 11 cells). This facilitation decayed progressively as a function of the time intervals between prepulses and cortical stimuli. The potentiation was not observed on small EPSPs reaching a peak potential < −65 mV ( n= 3). We conclude that SONs can optimise cortical information transfer by modifying their intrinsic excitability as a function of their past activation. It is likely that this time‐dependent facilitation results, at least in part, from the kinetics of a striatal slowly inactivating potassium current available around −60 mV that recovers slowly from inactivation.

The aim of the present paper is to study the effects of Hebbian learning in random recurrent neural networks with biological connectivity, i.e. sparse connections and separate populations of excitatory and inhibitory neurons. We furthermore consider that the neuron dynamics may occur at a (shorter) time scale than synaptic plasticity and consider the possibility of learning rules with passive forgetting. We show that the application of such Hebbian learning leads to drastic changes in the network Preprint submitted to ElsevierApril 16, 2018 dynamics and structure. In particular, the learning rule contracts the norm of the weight matrix and yields a rapid decay of the dynamics complexity and entropy. In other words, the network is rewired by Hebbian learning into a new synaptic structure that emerges with learning on the basis of the correlations that progressively build up between neurons. We also observe that, within this emerging structure, the strongest synapses organize as a small-world network. The second effect of the decay of the weight matrix spectral radius consists in a rapid contraction of the spectral radius of the Jacobian matrix. This drives the system through the "edge of chaos" where sensitivity to the input pattern is maximal. Taken together, this scenario is remarkably predicted by theoretical arguments derived from dynamical systems and graph theory.

We present a mathematical analysis of the effects of Hebbian learning in random recurrent neural networks, with a generic Hebbian learning rule, including passive forgetting and different timescales, for neuronal activity and learning dynamics. Previous numerical work has reported that Hebbian learning drives the system from chaos to a steady state through a sequence of bifurcations. Here, we interpret these results mathematically and show that these effects, involving a complex coupling between neuronal dynamics and synaptic graph structure, can be analyzed using Jacobian matrices, which introduce both a structural and a dynamical point of view on neural network evolution. Furthermore, we show that sensitivity to a learned pattern is maximal when the largest Lyapunov exponent is close to 0. We discuss how neural networks may take advantage of this regime of high functional interest.

SUMMARY Loss of function in the Scn1a gene leads to a severe epileptic encephalopathy called Dravet syndrome (DS). Reduced excitability in cortical inhibitory neurons is thought to be the major cause of DS seizures. Here, in contrast, we show enhanced excitability in thalamic inhibitory neurons that promotes the non-convulsive seizures that are a prominent yet poorly understood feature of DS. In a mouse model of DS with a loss of function in Scn1a , reticular thalamic cells exhibited abnormally long bursts of firing caused by the downregulation of calcium-activated potassium SK channels. Our study supports a mechanism in which loss of SK activity causes the reticular thalamic neurons to become hyperexcitable and promote non-convulsive seizures in DS. We propose that reduced excitability of inhibitory neurons is not global in DS and that non-GABAergic mechanisms such as SK channels may be important targets for treatment.