Topic: activity-dependent plasticity

In this webinar on spatial navigation circuits, three researchers—Ann Hermundstad, Ila Fiete, and Barbara Webb—discussed how diverse species solve navigation problems using specialized yet evolutionarily conserved brain structures. Hermundstad illustrated the fruit fly’s central complex, focusing on how hardwired circuit motifs (e.g., sinusoidal steering curves) enable rapid, flexible learning of goal-directed navigation. This framework combines internal heading representations with modifiable goal signals, leveraging activity-dependent plasticity to adapt to new environments. Fiete explored t

During development, the complex neuronal circuitry of the brain arises from limited information contained in the genome. After the genetic code instructs the birth of neurons, the emergence of brain regions, and the formation of axon tracts, it is believed that neuronal activity plays a critical role in shaping circuits for behavior. Current AI technologies are modeled after the same principle: connections in an initial weight matrix are pruned and strengthened by activity-dependent signals until the network can sufficiently generalize a set of inputs into outputs. Here, we challenge these lea

A widely-accepted tenet of neuroscience is that learning relies on synaptic plasticity. Because plasticity is thought to be governed by local, activity-dependent rules, it is a longstanding mystery how synapses change in concert so that behavioral output improves. This problem is aggravated by the recurrent and multilayered architecture of the cortex, which makes it hard to determine the effects of a given synaptic modification. Here we present a novel principle for learning rooted in optimal control theory, which yields local plasticity rules for any neural dynamics which reaches an equilibri