Topic: synaptic scaling

Synaptic scaling is a homeostatic normalization mechanism that preserves relative synaptic strengths by adjusting them with a common factor. This multiplicative change is believed to be critical, since synaptic strengths are involved in learning and memory retention. Further, this homeostatic process is thought to be crucial for neuronal stability, playing a stabilizing role in otherwise runaway Hebbian plasticity [1-3]. Synaptic scaling requires a mechanism to sense total neuron activity and globally adjust synapses to achieve some activity set-point [4]. This process is relatively slow, whic

Plasticity shapes the brain during development, and mechanisms of plasticity continue into adulthood to enable learning and memory. Nearly all brain functions are influenced by past events, reinforcing the view that the confluence of plasticity and computation in the same circuit elements is a core component of biological intelligence. My laboratory studies plasticity in the cerebral cortex during development, and plasticity during behaviour that is manifest as cortical dynamics. I will describe how cortical plasticity is implemented by learning rules that involve not only Hebbian changes and

Synaptic changes driven by Hebbian plasticity are unstable and require compensatory mechanisms to homeostatically control neuronal activity. Synaptic scaling regulates the synaptic efficacies to bring individual neurons to a set firing regime [1]. However, the timescales of synaptic scaling are too slow to counterbalance the rapid growth due to Hebbian mechanisms [2]. Heterosynaptic plasticity that operates on timescales similar to Hebbian processes can prevent runaway potentiation [3] but its ability to homeostatically control activity is uncertain. Here, we introduce aggregate scaling, a si

Perception relies on spatio-temporal activity patterns. Also, within the brain computations plau- sibly generate and exploit temporal structures. It is, however, unknown how neuronal systems develop selectivity for spatio-temporal patterns. Furthermore, learning should preserve already acquired contents while becoming selective to new contents. It is also not known by what mech- anisms synapses could stabilize previously stored memories of spatio-temporal patterns while they remain plastic and contribute to further learning. Here initially, the mechanisms for neurons to learn these patterns ar