From neuron to network
connectivity of microcircuits and dynamical consequences
Time: Mon 2025-08-25 10.00
Location: F3 (Flodis), Lindstedtsvägen 26 & 28, Stockholm
Language: English
Doctoral student: Lihao Guo , Beräkningsvetenskap och beräkningsteknik (CST), Arvind Kumar
Opponent: Srikanth Ramaswamy, 22 Featherstone Grove, NE3 5RJ, Newcastle Upon Tyne, United Kingdom.
Supervisor: Arvind Kumar, Beräkningsvetenskap och beräkningsteknik (CST); Professor Jens Hjerling-Leffler, Department of Medical Biochemistry and Biophysics, Karolinska Institute; Jeanette Hellgren Kotaleski, Beräkningsvetenskap och beräkningsteknik (CST), Science for Life Laboratory, SciLifeLab
QC 20250731
Abstract
Biological neural networks exhibit remarkable diversity, where each neuron type is genetically encoded with certain chemical specificity and morphological properties. These neuron-level specifications, when put together into a network, give rise to various microcircuits in the brain. This thesis investigates how these single neuron properties determine microcircuit connectivity. In turn, the network dynamics emerge from and are constrained by the underlying connectivity.
The chemical specificity is reflected in the neuronal compositions and type-specific connection probabilities. In the cortical microcircuit, pyramidal neurons and three major classes of inhibitory interneurons have a preference for presynaptic sources and postsynaptic targets. At the population level, it gives rise to redundancy in rate coding and non-linearity to the neuronal transfer function. In turn, it supports flexible modulations of across-trial variability by tuning the input distribution across trials. In the striatal microcircuit, fast-spiking interneurons (FSI), despite constituting only around 1\% of the striatal population, exert powerful feedforward inhibition due to their dense and strong axonal projections. The connectivity of FSIs leads to a significant sharing of feedforward inhibition in the major striatal population, medium spiny neurons (MSN). In turn, this shared inhibition results in variable population responses across trials and bidirectional control of the correlation transfer from cortical stimuli to striatal activities.
The morphology of individual neurons is deeply related to the network connectivity as well. In Parkinson’s disease (PD), striatal microcircuits undergo structural changes: FSIs exhibit axonal sprouting, amplifying shared inhibition and synchrony, while MSNs display dendritic atrophy, weakening corticostriatal drive. Compensatory mechanisms, such as synaptic scaling or rewiring, could potentially restore functional balance. In a more general setting, spatial constraints shape higher-order structures of microcircuit connectivity. The arrangement of neurons and the shape of their neurites induce connection preferences in terms of overlaps between axons, dendrites, and axon/dendrite. Such overlaps represent the second-order approximation of connections. A spatially coherent pattern of overlap would generate spatiotemporal activity patterns as an implementation of cell assemblies.
A combination of chemical specificity and morphological properties leads to a complete theoretical framework from single neurons to networks. Experimental observations of type-specific connection rules (both transcriptomic and morphological) would enable the abstraction of distinct brain regions into concrete microcircuits. These microcircuits serve as a biologically plausible baseline of neural network models.