Neurochemistry seminars

Dopamine-based reinforcement is well studied in artificial tasks, but its role in learning natural behaviour needs direct evidence. This talk tracks juvenile zebra-finch song alongside dopamine signals in a song-related basal-ganglia region. Signals distinguish renditions approaching the eventual adult song from poorer versions, predict later behavioural change and depend on recent performance history. These observations support a prediction-error account of reinforcement during developmental vocal learning.

Neural circuits are continuously under the influence of multiple chemical neuromodulators. The prevalent view is that neuromodulation increases the flexibility of circuit output. However, different modulators can have overlapping cellular and subcellular targets, and thus convergence and occlusion may limit the repertoire of possible circuit states. We propose a complementary view that convergent comodulation may result in a more consistent circuit activity that, with increasing numbers of modulators, becomes less dependent on the specific identity of the modulators involved. We examine this hypothesis in the crustacean stomatogastric ganglion, where multiple excitatory neuropeptides activate the same ionic current and have similar effects on synapses. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2026-01-28. Recording duration: 00:40:30.

Photoreceptor loss is the primary cause behind vision impairment and blindness in diseases such as retinitis pigmentosa and age-related macular degeneration. However, the death of rods and cones allows retinoids to permeate the inner retina, causing retinal ganglion cells to become spontaneously hyperactive, severely reducing the signal-to-noise ratio, and creating interference in the communication between the surviving retina and the brain. Treatments aimed at blocking or reducing hyperactivity improve vision initiated from surviving photoreceptors and could enhance the signal fidelity generated by vision restoration methodologies.

Functional connectivity has been the focus of many research groups aiming to study the interaction between cells and brain regions. A standard method for analyzing connectivity is to statistically compare pairwise interactions between cells or brain regions across behavioral states or conditions. This methodology ignores the intrinsic properties of functional connectivity as a multivariate and dynamic signal, expressing the correlational configuration of the network. In this talk, I will present a geometric approach, combining Graph Theory and Riemannian Geometry to build "a graph of graphs" and extract the latent dynamics of the overall correlational structure. Using this approach, we formulate the statistical relations between network dynamics and spontaneous behavior as a second-order Taylor’s expansion. Our analysis shows that fast fluctuations in functional connectivity of large-scale cortical networks are closely linked to variations in behavioral metrics related to the arousal state. We further expand this methodology to longer time scales to study the effect of dopamine on network dynamics in the primary motor cortex (M1) during learning. We developed a series of analysis methods indicating that as animals learn to perform a motor task, the network of pyramidal neurons in layer 2-3 gradually and monotonically reorganizes toward an "expert" configuration. Our results highlight the critical role of dopamine in driving synaptic plasticity: Blocking dopaminergic neurotransmission locally in M1 prevented motor learning at the behavioral level and concomitantly halted plasticity changes in network activity and in functional connectivity. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2025-01-29. Recording duration: 00:27:08.