Finding neuroheterogeneity’s sweet spot
Once thought to be detrimental to memory processing, neuronal diversity—at the right ‘Goldilocks’ dose—is a new boon to theoretical neuroscience.
Neuronal heterogeneity, once considered detrimental, can actually enhance memory processing and network flexibility, according to recent studies. Traditionally, computational models of the brain have treated neurons as identical, but researchers are now recognizing the importance of neuronal diversity. Scott Rich, an assistant professor at the University of Connecticut, explains that incorporating heterogeneity into models makes them more complex, but it is necessary for understanding the brain's true computations.
Heterogeneity has been shown to improve decision-making by increasing a neural network's flexibility. It can also make networks more robust, as too much diversity can destabilize networks, leading to pathological states. However, the right amount of heterogeneity can help a network switch flexibly between distinct memories or representations, much like how the Goldilocks principle balances the desirable qualities of something.
One benefit of neuronal heterogeneity is its ability to flatten the computational landscape of networks, making them easier to decode. Networks with varied firing properties have shallower valleys, representing stored memories, and can move between them more flexibly. This flattening helps improve performance on decoding tasks, as neurons can snap into new states with less input.
Neuromodulators like norepinephrine may also play a role in tuning neuronal heterogeneity. Arousal levels, linked to norepinephrine, can affect how heterogeneous neurons' excitability is, potentially influencing auditory task performance. Additionally, the connectivity degree of cortical neurons, or how many other neurons connect to them, varies greatly and can also shape network dynamics.
Understanding neuronal heterogeneity is crucial for developing improved models of the brain. With the recent explosion of sequencing and tracing technologies, it is clear that even within a single cell type, no two neurons are the same. Incorporating this heterogeneity into future models is essential for unraveling the brain's unique computations.
Written by urgent.news from The Transmitter's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
