Biology subjects

Azhar, D.

Publications and source records attributed to Azhar, D..

3 recordsLinked to original sources

bioRxiv2025

Connectivity biases generate a learning hierarchy in the Drosophila mushroom body

Learning and memory centers must balance maximizing coding capacity with prioritizing biologically relevant information. Expansion layers, a circuit motif common to many learning and memory centers, including the insect mushroom body, transform dense sensory representations into sparse, distributed ones, and theoretical models propose that random connectivity within these layers maximizes coding capacity by generating highly discriminable responses. Yet this solution creates a fundamental problem: purely random connectivity treats all stimuli equally, without prioritizing survival-relevant cues over neutral ones. Here, we show that the Drosophila melanogaster mushroom body resolves this capacity-selectivity trade-off through systematic biases in projection neuron-Kenyon cell connectivity. Although connectivity is random at the single-cell level, some projection neuron types connect up to 15-fold more frequently than others. These biases translate directly into function: Kenyon cell responses scales with projection neuron connectivity, and the breadth of odor-evoked responses predicts learning performance. Odors activating more than 20% of Kenyon cells drive robust associative memories, whereas those activating fewer than 10% are poorly learned. VL1 projection neurons are a notable exception: despite their weak connectivity, they elicit broad Kenyon cell activity but fail to support learning, revealing a circuit-level gate on learning. These results show that the mushroom body embeds a learning hierarchy in its connectivity architecture, prioritizing ethologically relevant odors while preserving coding capacity for diverse associations.

neuroscience↗

bioRxiv2025

Hippocampal time cell dynamics evolve with learning to reflect cognitive demands

The hippocampus creates cognitive maps, or internal representations that reflect knowledge of the external world. Hippocampal time cells are thought to represent the temporal structure of experiences, or temporal context. However, it remains unknown whether hippocampal time cells display learning dynamics that reflect increased knowledge of the temporal relationships that define a context. To address this gap, we utilized a behavioral paradigm with a shaping curriculum that allows animals to systematically acquire knowledge of temporal structure, ultimately enabling them to perform a temporal Delayed Non-Match to Sample (tDNMS) task. We conducted two-photon calcium imaging on large populations of CA1 neurons as mice progressed through the curriculum--from their initial exposure to the task structure to their successful discrimination of context at the end of training. Time cells were present from the outset, yet their activity evolved with experience, both at the single-cell level and across the population. Notably, at key moments in the curriculum, time cell dynamics adapted to reflect whether mice generalized across contexts or discriminated between them. Our findings suggest that CA1 time cells not only represent temporal context but may also reflect the processes by which temporal relationships are utilized. Hippocampal time cells therefore serve as a cognitive map, representing temporal relationships in a manner that reflects cognitive demands.

neuroscience↗

bioRxiv2025

Structured experience shapes strategy learning and neural dynamics in the medial entorhinal cortex

Animals can solve new, complex tasks by reusing and adapting what theyve learned before. This kind of flexibility depends not just on having prior experience, but on how that experience was structured in the first place. The design of early training curriculum is especially important: poorly structured experiences can hinder abstraction and limit generalization, while carefully structured training promotes more flexible and adaptive behavior. Yet, the neural mechanisms supporting this process remain unclear. To investigate how early training shapes learning we first trained recurrent neural networks (RNNs) on variants of an odor-timing task previously used to study complex timing behavior in mice. We then tested the RNN predictions on how previous experience affects generalization using behavioral and electrophysiological recordings in mice trained on the same task using staged training sequences. RNNs and mice trained without well-structured early experience developed rigid strategies and made repeated errors. In contrast, those given more balanced early training were better able to generalize and showed similar neural activity patterns that reflected the tasks underlying temporal structure. Using dynamical systems approaches, we reveal a mechanism for this effect: networks trained with appropriately structured curricula developed distinct dynamical motifs that support the correct abstractions when complexity was increased. Networks that lacked early training or received remedial curricula developed single fixed-point solutions that failed to generalize beyond the training stimuli. Together, these findings demonstrate that it is not just the presence of prior experience, but its structure, that governs how flexible and generalizable knowledge emerges in both biological systems and computational models.

neuroscience↗