In neuroscience, homeostatic plasticity refers to the capacity of neurons to regulate their own excitability relative to network activity. The term homeostatic plasticity derives from two opposing concepts: 'homeostatic' (a product of the Greek words for 'same' and 'state' or 'condition') and plasticity (or 'change'), thus homeostatic plasticity means "staying the same through change".
Homeostatic synaptic plasticity is a means of maintaining the synaptic basis for learning, respiration, and locomotion, in contrast to the Hebbian plasticity associated with learning and memory. Although Hebbian forms of plasticity, such as long-term potentiation and long-term depression occur rapidly, homeostatic plasticity (which relies on protein synthesis) can take hours or days. TNF-α and microRNAs are important mediators of homeostatic synaptic plasticity.
Homeostatic plasticity is thought to balance Hebbian plasticity by modulating the activity of the synapse or the properties of ion channels. Homeostatic plasticity in neocortical circuits has been studied in depth by Gina G. Turrigiano and Sacha Nelson of Brandeis University, who first observed compensatory changes in excitatory postsynaptic currents (mEPSCs) after chronic activity manipulations.
Synaptic scaling has been proposed as a potential mechanism of homeostatic plasticity. Homeostatic plasticity can be used to describe a process that maintains the stability of neuronal functions through a coordinated plasticity among subcellular compartments, such as the synapses versus the neurons and the cell bodies versus the axons. Recently, it was proposed that homeostatic synaptic scaling may play a role in establishing the specificity of an associative memory.
Homeostatic plasticity also maintains neuronal excitability in a real-time manner through the coordinated plasticity of threshold and refractory period at voltage-gated sodium channels.
Homeostatic plasticity is also very important in the context of central pattern generators.
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In neuroscience, synaptic plasticity is the ability of synapses to strengthen or weaken over time, in response to increases or decreases in their activity. Since memories are postulated to be represented by vastly interconnected neural circuits in the brain, synaptic plasticity is one of the important neurochemical foundations of learning and memory (see Hebbian theory). Plastic change often results from the alteration of the number of neurotransmitter receptors located on a synapse.
In neuroscience, homeostatic plasticity refers to the capacity of neurons to regulate their own excitability relative to network activity. The term homeostatic plasticity derives from two opposing concepts: 'homeostatic' (a product of the Greek words for 'same' and 'state' or 'condition') and plasticity (or 'change'), thus homeostatic plasticity means "staying the same through change". Homeostatic synaptic plasticity is a means of maintaining the synaptic basis for learning, respiration, and locomotion, in contrast to the Hebbian plasticity associated with learning and memory.
Neuroplasticity, also known as neural plasticity, or brain plasticity, is the ability of neural networks in the brain to change through growth and reorganization. It is when the brain is rewired to function in some way that differs from how it previously functioned. These changes range from individual neuron pathways making new connections, to systematic adjustments like cortical remapping. Examples of neuroplasticity include circuit and network changes that result from learning a new ability, information acquisition, environmental influences, practice, and psychological stress.
The course introduces students to a synthesis of modern neuroscience and state-of-the-art data management, modelling and computing technologies with a focus on the biophysical level.
In this course we study mathematical models of neurons and neuronal networks in the context of biology and establish links to models of cognition. The focus is on brain dynamics approximated by determ
The goal of the course is to increase students' knowledge in the field of Neuroscience, with a particular emphasis on neuronal circuits responsible for sensation, motor control and sensorimotor intera
Homeostatic plasticity of intrinsic excitability goes hand in hand with homeostatic plasticity of synaptic transmission. However, the mechanisms linking the two forms of homeostatic regulation have no
The activity of neurons in the brain and the code used by these neurons is described by mathematical neuron models at different levels of detail.
The activity of neurons in the brain and the code used by these neurons is described by mathematical neuron models at different levels of detail.
This course explains the mathematical and computational models that are used in the field of theoretical neuroscience to analyze the collective dynamics of thousands of interacting neurons.