Biology subjects

Naumann, L. B.

Publications and source records attributed to Naumann, L. B..

4 recordsLinked to original sources

bioRxiv2024

Layer-specific control of inhibition by NDNF interneurons

Neuronal processing of external sensory input is shaped by internally-generated top-down information. In the neocortex, top-down projections predominantly target layer 1, which contains NDNF-expressing interneurons, nestled between the dendrites of pyramidal cells (PCs). Here, we propose that NDNF interneurons shape cortical computations by presynap-tically inhibiting the outputs of somatostatin-expressing (SOM) interneurons via GABAergic volume transmission in layer 1. Whole-cell patch clamp recordings from genetically identified NDNF INs in layer 1 of the auditory cortex show that SOM-to-NDNF synapses are indeed modulated by ambient GABA. In a cortical microcircuit model, we then demonstrate that this mechanism can control inhibition in a layer-specific way and introduces a competition for dendritic inhibition between NDNF and SOM interneurons. This competition is mediated by a unique mutual inhibition motif between NDNF interneurons and the synaptic outputs of SOM interneurons, which can dynamically prioritise different inhibitory signals to the PC dendrite. NDNF interneurons can thereby control information flow in pyramidal cells by redistributing dendritic inhibition from fast to slow timescales and by gating different sources of dendritic inhibition, as exemplified in a predictive coding application. This work corroborates that NDNF interneurons are ideally suited to control information flow within cortical layer 1.

neuroscience↗

bioRxiv2021

Invariant neural subspaces maintained by feedback modulation

Sensory systems reliably process incoming stimuli in spite of changes in context. Most recent models accredit this context invariance to an extraction of increasingly complex sensory features in hierarchical feedforward networks. Here, we study how context-invariant representations can be established by feedback rather than feedforward processing. We show that feedforward neural networks modulated by feedback can dynamically generate invariant sensory representations. The required feedback can be implemented as a slow and spatially diffuse gain modulation. The invariance is not present on the level of individual neurons, but emerges only on the population level. Mechanistically, the feedback modulation dynamically reorients the manifold of neural activity and thereby maintains an invariant neural subspace in spite of contextual variations. Our results highlight the importance of population-level analyses for understanding the role of feedback in flexible sensory processing.

neuroscience↗

bioRxiv2020

Learning excitatory-inhibitory neuronal assemblies in recurrent networks

In sensory circuits with poor feature topography, stimulus-specific feedback inhibition necessitates carefully tuned synaptic circuitry. Recent experimental data from mouse primary visual cortex (V1) show that synapses between pyramidal neurons and parvalbumin-expressing (PV) inhibitory interneurons tend to be stronger for neurons that respond to similar stimulus features. The mechanism that underlies the formation of such excitatory-inhibitory (E/I) assemblies is unresolved. Here, we show that activity-dependent synaptic plasticity on input and output synapses of PV interneurons generates a circuit structure that is consistent with mouse V1. Using a computational model, we show that both forms of plasticity must act synergistically to form the observed E/I assemblies. Once established, these assemblies produce a stimulus-specific competition between pyramidal neurons. Our model suggests that activity-dependent plasticity can enable inhibitory circuits to actively shape cortical computations.

neuroscience↗

bioRxiv2020

Presynaptic inhibition rapidly stabilises recurrent excitation in the face of plasticity

Hebbian plasticity, a mechanism believed to be the substrate of learning and memory, detects and further enhances correlated neural activity. Because this constitutes an unstable positive feedback loop, it requires additional homeostatic control. Computational work suggests that in recurrent networks, the homeostatic mechanisms observed in experiments are too slow to compensate instabilities arising from Hebbian plasticity and need to be complemented by rapid compensatory processes. We suggest presynaptic inhibition as a candidate that rapidly provides stability by compensating recurrent excitation induced by Hebbian changes. Presynaptic inhibition is mediated by presynaptic GABA receptors that effectively and reversibly attenuate transmitter release. Activation of these receptors can be triggered by excess network activity, hence providing a stabilising negative feedback loop that weakens recurrent interactions on sub-second timescales. We study the stabilising effect of presynaptic inhibition in a recurrent networks, in which presynaptic inhibition is implemented as a multiplicative reduction of recurrent synaptic weights in response to increasing inhibitory activity. We show that networks with presynaptic inhibition display a gradual increase of firing rates with growing excitatory weights, in contrast to traditional excitatory-inhibitory networks. This alleviates the positive feedback loop between Hebbian plasticity and network activity and thereby allows homeostasis to act on timescales similar to those observed in experiments. Our results generalise to spiking networks with a biophysically more detailed implementation of the presynaptic inhibition mechanism. In conclusion, presynaptic inhibition provides a powerful compensatory mechanism that rapidly reduces effective recurrent interactions and thereby stabilises Hebbian learning.

neuroscience↗