Biology subjects

Ujfalussy, B. B.

Publications and source records attributed to Ujfalussy, B. B..

5 recordsLinked to original sources

bioRxiv2025

Currentscape analysis of dendritic inputs during place field dynamics

Neuronal activity is driven by the complex interplay between various membrane currents, often located in distinct domains of the spatially extended dendritic tree. How the effect of these currents propagates to the soma and contributes to neuronal output under in vivo conditions is not fully understood. Here, we develop a new method to measure and visualise the contributions of individual membrane currents to the somatic response in spatially extended biophysical model neurons. Our approach relies on the iterative decomposition of the axial current flowing between neighbouring compartments in proportion to the underlying membrane currents measured in the model. We apply this method to visualise the inputs driving hippocampal place cell activity. Our method provides a compact and intuitive description of the various dendritic events underlying subthreshold activity, spiking, or burst firing. By contrasting the dendritic input currents preceding spiking and bursting, we demonstrate that both could occur at highly variable input levels to proximal dendrites (basal and oblique), and that strong distal inputs facilitates, rather than controls, the generation of complex spike bursts. Our method opens a novel window onto single-neuron computations that will help to design better models and to interpret the results of in vivo imaging experiments.

neuroscience↗

bioRxiv2021

Sampling motion trajectories during hippocampal theta sequences

Efficient planning in complex environments requires that uncertainty associated with current inferences and possible consequences of forthcoming actions is represented. Representation of uncertainty has been established in sensory systems during simple perceptual decision making tasks but it remains unclear if complex cognitive computations such as planning and navigation are also supported by probabilistic neural representations. Here we capitalized on gradually changing uncertainty along planned motion trajectories during hippocampal theta sequences to capture signatures of uncertainty representation in population responses. In contrast with prominent theories, we found no evidence of encoding parameters of probability distributions in the momentary population activity recorded in an open-field navigation task in rats. Instead, uncertainty was encoded sequentially by sampling motion trajectories randomly in subsequent theta cycles from the distribution of potential trajectories. Our analysis is the first to demonstrate that the hippocampus is well equipped to contribute to optimal planning by representing uncertainty.

neuroscience↗

bioRxiv2019

Impact of functional synapse clusters on neuronal response selectivity.

Clustering of functionally similar synapses in dendrites is thought to affect input-output transformation by inducing dendritic nonlinearities. However, neither the in vivo impact of synaptic clusters on somatic membrane potential (sVm), nor the rules of cluster formation are elucidated. We developed a computational approach to measure the effect of functional synaptic clusters on sVm response of biophysical model CA1 and L2/3 pyramidal neurons to behaviorally relevant in vivo-like inputs. Large-scale dendritic spatial inhomogeneities in synaptic tuning properties did influence sVm, but small synaptic clusters appearing randomly with unstructured connectivity did not. With structured connectivity, ~10-20 synapses per cluster was optimal for clustering-based tuning, but larger responses were achieved by 2-fold potentiation of the same synapses. We further show that without nonlinear amplification of the effect of random clusters, action potential-based, global plasticity rules can not generate functional clustering. Our results suggest that clusters likely form via local synaptic interactions, and have to be moderately large to impact sVm responses.

neuroscience↗

bioRxiv2017

Global, multiplexed dendritic computations under in vivo-like conditions

Dendrites integrate inputs in highly non-linear ways, but it is unclear how these non-linearities contribute to the overall input-output transformation of single neurons. Here, we developed statistically principled methods using a hierarchical cascade of linear-nonlinear subunits (hLN) to model the dynamically evolving somatic response of neurons receiving complex spatio-temporal synaptic input patterns. We used the hLN to predict the membrane potential of a detailed biophysical model of a L2/3 pyramidal cell receiving in vivo-like synaptic input and reproducing in vivo dendritic recordings. We found that more than 90% of the somatic response could be captured by linear integration followed a single global non-linearity. Multiplexing inputs into parallel processing channels could improve prediction accuracy by as much as additional layers of local non-linearities. These results provide a data-driven characterisation of a key building block of cortical circuit computations: dendritic integration and the input-output transformation of single neurons during in vivo-like conditions.

neuroscience↗

bioRxiv2017

Robust and efficient coding with grid cells

The neuronal code arising from the coordinated population activity of grid cells in the rodent entorhinal cortex can uniquely represent space across large distances but the precise conditions for efficient coding are unknown. Here we present a number-theoretic analysis of grid coding and derive an upper bound on the distance that a population of grid cells can represent without error. We show that in the absence of neuronal noise, the capacity of the system would be extremely sensitive to the choice of the grid periods. However, when the accuracy of the representation is limited by neuronal noise, the capacity becomes gradually more robust against the choice of grid scales as the number of modules increases and remains near optimal even for random scale choices. Our study reveals that robust and efficient coding can be achieved without parameter tuning in the case of grid cell representation.

neuroscience↗