Biology subjects

Aljadeff, J.

Publications and source records attributed to Aljadeff, J..

5 recordsLinked to original sources

bioRxiv2026

Working memory retention by medial entorhinal cortex low-dimensional neural dynamics

The ability to retain information over tens of seconds is essential for carrying out many routine tasks. Recurrently connected circuits are thought to support memory over these timescales, yet how they maintain and represent remembered information remains unclear. We asked whether the medial entorhinal cortex (mEC), where recurrent attractor dynamics generate grid-cell firing patterns and single toroidal attractor manifolds in two-dimensional environments, also supports low-dimensional neural activity patterns during memory retention. During a working-memory task, delay-period mEC dynamics did not show key features of grid firing in simple environments. Instead, broader mEC populations, not limited to grid cells, expressed multiple sequences and recurring activity patterns. Periodically repeating delay-period sequences retained task-relevant information, including past locations and future turn directions. These findings suggest that the mEC network generates low-dimensional dynamics more broadly, with grid-cell activity representing one manifestation and more complex manifold topologies supporting task-related representations during working memory.

neuroscience↗

bioRxiv2024

Desegregation of neuronal predictive processing

Neural circuits construct internal world-models to guide behavior. The predictive processing framework posits that neural activity signaling sensory predictions and concurrently computing prediction-errors is a signature of those internal models. Here, to understand how the brain generates predictions for complex sensorimotor signals, we investigate the emergence of high-dimensional, multi-modal predictive representations in recurrent networks. We find that robust predictive processing arises in a network with loose excitatory/inhibitory balance. Contrary to previous proposals of functionally specialized cell-types, the network exhibits desegregation of stimulus and prediction-error representations. We confirmed these model predictions by experimentally probing predictive-coding circuits using a rich stimulus-set to violate learned expectations. When constrained by data, our model further reveals and makes concrete testable experimental predictions for the distinct functional roles of excitatory and inhibitory neurons, and of neurons in different layers along a laminar hierarchy, in computing multi-modal predictions. These results together imply that in natural conditions, neural representations of internal models are highly distributed, yet structured to allow flexible readout of behaviorally-relevant information. The generality of our model advances the understanding of computation of internal models across species, by incorporating different types of predictive computations into a unified framework.

neuroscience↗

bioRxiv2023

Shallow networks run deep: Peripheral preprocessing facilitates odor classification

The mammalian brain implements sophisticated sensory processing algorithms along multilayered ( deep) neural-networks. Strategies that insects use to meet similar computational demands, while relying on smaller nervous systems with shallow architectures, remain elusive. Using Drosophila as a model, we uncover the algorithmic role of odor preprocessing by a shallow network of compartmentalized olfactory receptor neurons. Each compartment operates as a ratiometric unit for specific odor-mixtures. This computation arises from a simple mechanism: electrical coupling between two differently-sized neurons. We demonstrate that downstream synaptic connectivity is shaped to optimally leverage amplification of a hedonic value signal in the periphery. Furthermore, peripheral preprocessing is shown to markedly improve novel odor classification in a higher brain center. Together, our work highlights a far-reaching functional role of the sensory periphery for downstream processing. By elucidating the implementation of powerful computations by a shallow network, we provide insights into general principles of efficient sensory processing algorithms.

neuroscience↗

bioRxiv2020

Altered spike timing-dependent plasticity rules in physiological calcium

Like many forms of long-term synaptic plasticity, spike-timing-dependent plasticity (STDP) depends on intracellular Ca2+ signaling for its induction. Yet, all in vitro studies devoted to STDP used abnormally high external Ca2+ concentration. We measured STDP at the CA3-CA1 hippocampal synapses under different extracellular Ca2+ concentrations and found that the sign, shape and magnitude of plasticity strongly depend on Ca2+. A pre-post protocol that results in robust LTP in high Ca2+, yielded only LTD or no plasticity in the physiological Ca2+ range. LTP could be restored by either increasing the number of post-synaptic spikes or increasing the pairing frequency. A calcium-based plasticity model in which depression and potentiation depend on post-synaptic Ca2+ transients was found to fit quantitatively all the data, provided NMDA receptor-mediated non-linearities were implemented. In conclusion, STDP rule is profoundly altered in physiological Ca2+ but specific activity regimes restore a classical STDP profile.

neuroscience↗

bioRxiv2017

Quantifying the thermostability landscape separating plant sesquiterpene synthase orthologs using Maximum Entropy

As global temperatures rise due to climate change, crops are becoming increasingly vulnerable to failure. We tested the accessibility of thermodynamic phenotypic diversity within limited positional changes using 29=512 mutants of a terpene synthase (TPS), Tobacco 5-epi-Aristolchene synthase. First, we measured the thermal unfolding curves of each mutant and found that mutations shifted the Tms both higher and lower, including a cohort of mutants that failed to fold. The low correlation coefficient between the Tms of these mutants and the proportions of each terpene product output by the enzymes revealed that thermostability and product output are independent traits. Maximum Noise Entropy analyses were used to analyze the impact of the 9 mutational positions on thermostability, revealing that three of these positions were mainly responsible for the trait of increased Tm. These positions form a functional network as measured by the nonlinearity of their combined effects on thermostability. Unexpectedly, the strongly destabilizing positions combine nonlinearly to ameliorate each others deleterious effects resulting in a synergistic dampening. Taken together, our study shows the high potential for specialized metabolic enzyme engineering but also reveals a complex interconnected system of amino acids that will continue to evade perfect predictability.

bioengineering↗