Biology subjects

Duszkiewicz, A. J.

Publications and source records attributed to Duszkiewicz, A. J..

3 recordsLinked to original sources

bioRxiv2023

Hyperpolarization-Activated Currents Drive Neuronal Activation Sequences in Sleep

Sequential neuronal patterns are believed to support information processing in the cortex, yet their origin is still a matter of debate. We report that neuronal activity in the mouse head-direction cortex (HDC, i.e., the post-subiculum) was sequentially activated along the dorso-ventral axis during sleep at the transition from hyperpolarized "DOWN" to activated "UP" states, while representing a stable direction. Computational modelling suggested that these dynamics could be attributed to a spatial gradient of hyperpolarization-activated current (Ih), which we confirmed in ex vivo slice experiments and corroborated in other cortical structures. These findings open up the possibility that varying amounts of Ih across cortical neurons could result in sequential neuronal patterns, and that travelling activity upstream of the entorhinal-hippocampal circuit organises large-scale neuronal activity supporting learning and memory during sleep. HighlightsO_LINeuronal Activation Sequence in HDC: neuronal activity was sequentially reinstated along the dorsoventral axis of the HDC at UP state but not DOWN state onset. C_LIO_LIRole of Ih in Sequence Generation: Incorporating the hyperpolarization-activated current (Ih) into computational models, we identified its pivotal role in UP/DOWN dynamics and neuronal activity sequences. C_LIO_LIEx Vivo Verification: slice physiology revealed a dorsoventral gradient of Ih in the HDC. C_LIO_LIImplications Beyond HDC: the gradient of Ih could account for the sequential organization of neuronal activity across various cortical areas. C_LI

neuroscience↗

bioRxiv2022

Signature of random connectivity in the distributionof neuronal tuning curves.

Understanding the relationship between circuit properties and the organization of neuronal population activity is a fundamental question in neuroscience. The fine tuning of neuronal activity to specific values of environmental or internal features are canonical examples of how information is encoded in the brain, possibly resulting from precisely organized inputs. Yet, in the cortex, finely tuned neurons are often recorded together with neurons whose tuning is much less specific, for example those of inhibitory neurons, and the connectivity statistics accounting for the overall distribution of tuning curves is unclear. Here, using recordings in the mouse head-direction system, we first show both in simulation and analytically that random linear combinations of ideal finely tuned inputs reproduce the distribution of fast-spiking neuron tuning curves, a class of neurons believed to operate in the linear regime. This transformation preserves, on the population level, the singular spectrum of the input tuning curves but the relative power of each singular component is independently distributed in each output cell, leading to a distribution ranging from uni-modal to symmetrically tuned cells. We then generalize the model to a non-linear transformation of the inputs, combined with background inhibition. Using recordings from input neurons in the thalamus, where tuning curves are near-ideal, the model reproduces for various levels of inhibition the entire range of observed neuronal responses in the cortex, from precisely and narrowly tuned neurons to multipeak excitatory cells, as well as symmetrical tuning curves of inhibitory neurons. We replicate these findings in a dataset of hippocampal recordings. In conclusion, the full distribution of tuning curves is a signature of input connectivity statistics, which, for fast-spiking neurons and thalamocortical circuits, is likely to be random rather than specifically organized.

neuroscience↗

bioRxiv2022

Reciprocal representation of encoded features by cortical excitatory and inhibitory neuronal populations.

In the cortex, the interplay between excitation and inhibition determines the fidelity of neuronal representations. However, while the receptive fields of excitatory neurons are often fine-tuned to the encoded features, the principles governing the tuning of inhibitory neurons are still elusive. We addressed this problem by recording populations of neurons in the postsubiculum (PoSub), a cortical area where the receptive fields of most excitatory neurons correspond to a specific head-direction (HD). In contrast to PoSub-HD cells, the tuning of fast-spiking (FS) cells, the largest class of cortical inhibitory neurons, was broad and heterogeneous. However, we found that PoSub-FS cell tuning curves were often fine-tuned in the spatial frequency domain, which resulted in various radial symmetries in their HD tuning. In addition, recordings and specific optogenetic manipulations of the upstream thalamic populations as well as computational models suggest that this population co-tuning in the frequency domain has a local origin. Together, these findings provide evidence that the resolution of neuronal tuning is an intrinsic property of local cortical networks, shared by both excitatory and inhibitory cell populations.

neuroscience↗