Intracerebroventricularly delivered VEGF promotes contralesional corticorubral plasticity after focal cerebral ischemia via mechanisms involving anti-inflammatory actions

Vascular endothelial growth factor (VEGF) has potent angiogenic and neuroprotective effects in the ischemic brain. Its effect on axonal plasticity and neurological recovery in the post-acute stroke phase was unknown. Using behavioral tests combined with anterograde tract tracing studies and with immunohistochemical and molecular biological experiments, we examined effects of a delayed i.c.v. delivery of recombinant human VEGF(165), starting 3 days after stroke, on functional neurological recovery, corticorubral plasticity and inflammatory brain responses in mice submitted to 30 min of middle cerebral artery occlusion. We herein show that the slowly progressive functional improvements of motor grip strength and coordination, which are induced by VEGF, are accompanied by enhanced sprouting of contralesional corticorubral fibres that branched off the pyramidal tract in order to cross the midline and innervate the ipsilesional parvocellular red nucleus. Infiltrates of CD45+ leukocytes were noticed in the ischemic striatum of vehicle-treated mice that closely corresponded to areas exhibiting Iba-1+ activated microglia. VEGF attenuated the CD45+ leukocyte infiltrates at 14 but not 30 days post ischemia and diminished the microglial activation. Notably, the VEGF-induced anti-inflammatory effect of VEGF was associated with a downregulation of a broad set of inflammatory cytokines and chemokines in both brain hemispheres. These data suggest a link between VEGF's immunosuppressive and plasticity-promoting actions that may be important for successful brain remodeling. Accordingly, growth factors with anti-inflammatory action may be promising therapeutics in the post-acute stroke phase.

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior, and in particular in behavioral decision making. Such decision making is likely to involve the integration of many synaptic events in space and time. However, using a single reinforcement signal to modulate synaptic plasticity, as suggested in classical reinforcement learning algorithms, a twofold problem arises. Different synapses will have contributed differently to the behavioral decision, and even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike-time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward, but also by a population feedback signal. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference (TD) based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task, the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second task involves an action sequence which is itself extended in time and reward is only delivered at the last action, as it is the case in any type of board-game. The third task is the inspection game that has been studied in neuroeconomics, where an inspector tries to prevent a worker from shirking. Applying our algorithm to this game yields a learning behavior which is consistent with behavioral data from humans and monkeys, revealing themselves properties of a mixed Nash equilibrium. The examples show that our neuronal implementation of reward based learning copes with delayed and stochastic reward delivery, and also with the learning of mixed strategies in two-opponent games.

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior. But behavioral decision making is likely to involve the integration of many synaptic events in space and time. So in using a single reinforcement signal to modulate synaptic plasticity a twofold problem arises. Different synapses will have contributed differently to the behavioral decision and, even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward but by a population feedback signal as well. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second one involves an action sequence which is itself extended in time and reward is only delivered at the last action, as is the case in any type of board-game. The third is the inspection game that has been studied in neuroeconomics. It only has a mixed Nash equilibrium and exemplifies that the model also copes with stochastic reward delivery and the learning of mixed strategies.

Spatio-temporal credit assignment in population learning

We present a model for plasticity induction in reinforcement learning which is based on a cascade of synaptic memory traces. In the cascade of these so called eligibility traces presynaptic input is first corre lated with postsynaptic events, next with the behavioral decisions and finally with the external reinforcement. A population of leaky integrate and fire neurons endowed with this plasticity scheme is studied by simulation on different tasks. For operant co nditioning with delayed reinforcement, learning succeeds even when the delay is so large that the delivered reward reflects the appropriateness, not of the immediately preceeding response, but of a decision made earlier on in the stimulus - decision sequence . So the proposed model does not rely on the temporal contiguity between decision and pertinent reward and thus provides a viable means of addressing the temporal credit assignment problem. In the same task, learning speeds up with increasing population si ze, showing that the plasticity cascade simultaneously addresses the spatial problem of assigning credit to the different population neurons. Simulations on other task such as sequential decision making serve to highlight the robustness of the proposed sch eme and, further, contrast its performance to that of temporal difference based approaches to reinforcement learning.

Structural plasticity in the language system related to increased second language proficiency

While functional changes linked to second language learning have been subject to extensive investigation, the issue of learning-dependent structural plasticity in the fields of bilingualism and language comprehension has so far received less notice. In the present study we used voxel-based morphometry to monitor structural changes occurring within five months of second language learning. Native English-speaking exchange students learning German in Switzerland were examined once at the beginning of their stay and once about five months later, when their German language skills had significantly increased. We show that structural changes in the left inferior frontal gyrus are correlated with the increase in second language proficiency as measured by a paper-and-pencil language test. Contrary to the increase in proficiency and grey matter, the absolute values of grey matter density and second language proficiency did not correlate (neither on first nor on second measurement). This indicates that the individual amount of learning is reflected in brain structure changes, regardless of absolute proficiency.