Biology subjects

Wybo, W. A. M.

Publications and source records attributed to Wybo, W. A. M..

4 recordsLinked to original sources

bioRxiv2024

Hierarchy of prediction errors shapes the learning of context-dependent sensory representations

How sensory information is interpreted depends on context, yet the neural mechanisms by which context shapes sensory processing remain poorly understood. To address this question, we developed a computational model constrained by in vivo functional imaging of cortical neurons in mice during reversal learning of a tactile sensory discrimination task. During learning, layer 2/3 somatosensory neurons enhanced their response to reward-predictive stimuli. The model accounted for these observations through selective top-down gain amplification of apical dendritic inputs, accompanied by reduced reward-prediction errors and increased confidence in outcome predictions. Upon rule-reversal, the lateral orbitofrontal cortex, through disinhibitory VIP interneurons, encoded a context-prediction error signaling a loss of confidence. The hierarchy of reward- and context-prediction errors across cortical areas is mirrored in top-down signals modulating apical activity of simulated pyramidal neurons in the primary sensory cortex. The model explains how contextual changes are detected and how reward- and context-prediction error signals, originating in different cortical regions, interact to reshape the sensory representation.

neuroscience↗

bioRxiv2022

Dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways

While sensory representations in the brain depend on context, it remains unclear how such modulations are implemented at the biophysical level, and how processing layers further in the hierarchy can extract useful features for each possible contextual state. Here, we first demonstrate that thin dendritic branches are well suited to implementing contextual modulation of feedforward processing. Such neuron-specific modulations exploit prior knowledge, encoded in stable feedforward weights, to achieve transfer learning across contexts. In a network of biophysically realistic neuron models with context-independent feedforward weights, we show that modulatory inputs to thin dendrites can solve linearly non-separable learning problems with a Hebbian, error-modulated learning rule. Finally, we demonstrate that local prediction of whether representations originate either from different inputs, or from different contextual modulations of the same input, results in representation learning of hierarchical feedforward weights across processing layers that accommodate a multitude of contexts.

neuroscience↗

bioRxiv2020

Data-driven reduction of dendritic morphologies with preserved dendro-somatic responses

Dendrites shape information flow in neurons. Yet, there is little consensus on the level of spatial complexity at which they operate. We present a flexible and fast method to obtain simplified neuron models at any level of complexity. Through carefully chosen parameter fits, solvable in the least squares sense, we obtain optimal reduced compartmental models. We show that (back-propagating) action potentials, calcium-spikes and NMDA-spikes can all be reproduced with few compartments. We also investigate whether afferent spatial connectivity motifs admit simplification by ablating targeted branches and grouping the affected synapses onto the next proximal dendrite. We find that voltage in the remaining branches is reproduced if temporal conductance fluctuations stay below a limit that depends on the average difference in input impedance between the ablated branches and the next proximal dendrite. Further, our methodology fits reduced models directly from experimental data, without requiring morphological reconstructions. We provide a software toolbox that automatizes the simplification, eliminating a common hurdle towards including dendritic computations in network models.

neuroscience↗

bioRxiv2018

Dynamic compartmentalization in neurons enables branch specific learning.

The dendritic trees of neurons play an important role in the information processing in the brain. While it is tacitly assumed that dendrites require independent compartments to perform most of their computational functions, it is still not understood how they compartmentalize into functional subunits. Here we show how these subunits can be deduced from the structural and electrical properties of dendrites. We devised a mathematical formalism that links the dendritic arborization to an impedance-based tree-graph and show how the topology of this tree-graph reveals independent dendritic compartments. This analysis reveals that coopera-tivity between synapses decreases less than depolarization with increasing electrical separation, and thus that surprisingly few independent subunits coexist on dendritic trees. We nevertheless find that balanced inputs or shunting inhibition can modify this topology and increase the number and size of compartments in a context-dependent, temporal manner. We also find that this dynamic recompartmentalization can enable branch-specific learning of stimulus features.

neuroscience↗