Chacron, M. J. | bioRxiv Science

bioRxiv2024

Burst firing optimizes invariant coding of natural communication signals by electrosensory neural populations

Accurate perception of objects within the environment independent of context is essential for an organisms survival. While neurons that respond in an invariant manner to identity-preserving transformations of objects are thought to provide a neural correlate of context-independent perception, how these emerge in the brain remains poorly understood. Here we demonstrate that burst firing in neural populations can give rise to an invariant representation of highly heterogeneous natural communication stimuli. Multi-unit recordings from central sensory neural populations showed that considering burst spike trains led to invariant representations at the population but not the single neuron level. Computational modeling further revealed that optimal invariance is achieved for levels of burst firing seen experimentally. Taken together, our results demonstrate a novel function for burst firing towards establishing invariant representations of sensory input in neural populations.

neuroscience↗

bioRxiv2023

Decoding the relative contributions of extrinsic and intrinsic mechanisms in mediating heterogeneous spiking activities of sensory neurons in vivo using computational modeling

Neurons ubiquitously display heterogeneities in spiking activity even within a given cell type. To date, the relative contributions of extrinsic mechanisms (e.g., synaptic bombardment) and intrinsic mechanisms (e.g., conductances, cell morphology) towards determining spiking activity remain poorly understood. Here we address this important question using a novel approach that combines biophysical techniques, in which extracellular in vivo recordings of electrosensory pyramidal cells within weakly electric fish, are combined with computational modeling. Specifically, by varying parameters, a conductance-based computational model successfully reproduced the highly heterogeneous spiking activities seen experimentally. Model parameters that varied the most were then used to gauge the relative contributions of extrinsic vs. intrinsic mechanisms. Overall, extrinsic synaptic input was predicted to be the main factor accounting for spiking heterogeneities. We tested this prediction experimentally by performing two different manipulations: i) pharmacologically inactivating feedback; ii) applying the neuromodulator serotonin. Our model predicted that feedback inactivation should reduce while serotonin application should increase spiking heterogeneities. Experiments corroborated these predictions. Importantly, for serotonin application, increased heterogeneity occurred despite a strong reduction in intrinsic membrane conductance, further demonstrating that extrinsic synaptic input is the primary determinant of spiking heterogeneities in vivo. Taken together, our results demonstrate that devising a computational model to capture spiking heterogeneities in vivo and assessing which parameters are responsible can successfully determine the relative contributions of extrinsic vs. intrinsic inputs. We expect this approach to be generalizable to other systems and species.

neuroscience↗