Network Functions And Plasticity

Homeostatic and Retrograde Signaling Mechanisms Modulating Presynaptic Function and Plasticity

Activity within neural circuits shapes the synaptic properties of component neurons in a manner that maintains stable excitatory drive, a process referred to as homeostatic plasticity. These potent and adaptive mechanisms have been demonstrated to modulate activity at the level of an individual neuron, synapse, circuit, or entire network, and dysregulation at some or all of these levels may contribute to neuropsychiatric disorders, intellectual disability, and epilepsy. Greater mechanistic understanding of homeostatic plasticity will provide key insights into the etiology of these disorders, which may result from network instability and synaptic dysfunction. Over the past 15 years, the molecular mechanisms of this form of plasticity have been intensely studied in various model organisms, including invertebrates and vertebrates. Though once thought to have a predominantly postsynaptic basis, emerging evidence suggests that homeostatic mechanisms act on both sides of the synapse through mechanisms such as retrograde signaling, to orchestrate compensatory adaptations that maintain stable network function. These trans-synaptic signaling systems ultimately alter neurotransmitter release probability by a variety of mechanisms including changes in vesicle pool size and calcium influx. These adaptations are not expected to occur homogenously at all terminals of a pre-synaptic neuron, as they might synapse with neurons in non-overlapping circuits. However, the factors that govern the homeostatic control of synapse-specific plasticity are only beginning to be understood. In addition to our limited molecular understanding of pre-synaptic homeostatic plasticity, very little is known about its prevalence in vivo or its physiological and disease relevance. In this research topic, we aim to fill the aforementioned void by covering a broad range of topics that include: - Identification of signaling pathways and mechanisms that operate globally or locally to induce specific pre-synaptic adaptations - The nature of pre-synaptic ion channels relevant to this form of plasticity and their synapse-specific modulation and trafficking - Development and utilization of new tools or methods to study homeostatic plasticity in axons and pre-synaptic terminals - Novel mechanisms of homeostatic adaptations in pre-synaptic neurons - Postsynaptic sensors of activity and retrograde synaptic signaling systems - A comprehensive analysis of the kinds of pre-synaptic adaptations in diverse neural circuits and cell types - Identification of physiological or developmental conditions that promote pre-synaptic homeostatic adaptations - How activity-dependent (Hebbian) and homeostatic synaptic changes are integrated to both permit sufficient flexibility and maintain stable activity - Relevance of pre-synaptic homeostatic plasticity to the etiology of neuropsychiatric disorders - Computational modeling of pre-synaptic homeostatic plasticity and network stability.

Theory of Cortical Plasticity

This invaluable book presents a theory of cortical plasticity and shows how this theory leads to experiments that test both its assumptions and consequences. It elucidates, in a manner that is accessible to students as well as researchers, the role which the BCM theory has played in guiding research and suggesting experiments that have led to our present understanding of the mechanisms underlying cortical plasticity. Most of the connections between theory and experiment that are discussed require complex simulations. A unique feature of the book is the accompanying software package, Plasticity. This is provided complete with source code, and enables the reader to repeat any of the simulations quoted in the book as well as to vary either parameters or assumptions. Plasticity is thus a research and an educational tool. Readers can use it to obtain hands-on knowledge of the structure of BCM and various other learning algorithms. They can check and replicate our results as well as test algorithms andrefinements of their own.

From Structure to Function in Neuronal Networks: Effects of Adaptation, Time-Delays, and Noise