Elad Schneidman

The paper develops a framework to model cortical connectivity using representational learning, achieving accurate predictions of synapses and network motifs without detailed cell-type classifications.

The paper profiles single-cell RNA sequencing data from sex-shared neurons in C. elegans to reveal molecular dimorphism and sex-biased gene expression patterns.

This paper introduces a new class of population code models based on reshaped sparse non-linear random projections, showing improved accuracy and efficiency in modeling neural activity from behaving monkeys.

This paper uses statistical generative models to reconstruct the developing connectome of C. elegans, identifying key features and developmental stages.

The paper develops probabilistic generative models for neural connectomes across species, showing that simple design principles can accurately replicate structural and functional properties.

The paper studies sex-specific differences in nociceptive circuits of C. elegans and shows how network topology influences dimorphic behavior through synaptic rewiring.

The paper identifies architectural features that predict functional similarity in neural networks by analyzing synaptic inputs and outputs.

This paper introduces a probabilistic neural model inspired by sparse random connectivity that efficiently estimates spiking patterns in neural circuits and outperforms existing models.

The paper tracks neural dynamics during learning in primate brain regions using a geometric framework, revealing differential roles of dACC and striatum in behavior adaptation.

This study investigates how social information is encoded in the medial prefrontal cortex of mice and shows impaired social representation in Cntnap2 knockout mice, linking prefrontal dynamics to autism-related social dysfunction.

The paper analyzes spiking patterns in monkey prefrontal cortex during a visual task, showing strong correlations in encoding but simple decoding.