Julijana Gjorgjieva

The paper shows that combining different STDP rules generates diverse neural network motifs, but no single rule can produce all possible structures.

The paper proposes a computational model of neuronal assemblies that uses dendritic compartments to prevent catastrophic forgetting and enables flexible learning of new stimuli without disrupting existing connections.

The paper shows that excitatory and inhibitory synaptic scaling mechanisms interact to shape memory specificity during associative learning.

The paper shows that temporal diversity in Drosophila visual circuits improves coding efficiency for natural motion by distributing encoding across complementary timescales.

This study identifies right-hemispheric language networks at single-neuron resolution in a patient with aphasia, showing task-specific encoding and functional organization.

The paper investigates how synaptic density and relative connectivity conservation maintain circuit stability during development in Drosophila using EM and computational modeling.

This study shows that mice can generalize sensorimotor task rules between touch and vision using abstract representations of peri-personal space in the cortex, identifying a specific dorsal stream area as critical for this ability.

This study shows that brain-wide microstrokes disrupt hippocampal network stability, leading to cognitive impairments, with stable coding place cells predicting recovery.

This study shows that pre-training neural networks with retinal waves improves motion prediction in natural scenes and accelerates learning.

The paper proposes a mechanistic model that explains dendritic growth and synaptic organization through activity-independent cues and local activity-dependent plasticity, showing how these processes shape neuronal morphology and function.

This study shows that dopamine D1 receptor activation in the anterior insula increases anxiety-related behaviors and disrupts neural coding of exposed versus protected spaces.

This study shows that sensory experience influences the direction of representational drift in mouse visual cortex, while synaptic volatility affects its magnitude.

The paper shows how inhibitory synaptic plasticity rules can stabilize neural activity and generate structured excitatory-inhibitory connectivity motifs in recurrent neural networks.

This study shows recurrent neural networks develop and evolve repeating spike sequences through synaptic plasticity, with structured inputs enabling stable yet flexible encoding.

The paper investigates how spontaneous activity guides the development of multisensory integration in the developing higher-order cortex by establishing co-aligned topographic projections.

The paper proposes a learning framework where synapse-type-specific competition stabilizes synaptic connectivity, leading to structured networks with functional properties like response normalization and orientation-specific suppression.

The paper demonstrates how short-term plasticity mechanisms in cortical circuits can generate nonlinear phenomena like response reversal through interactions between interneuron subtypes.

The paper analyzes how inhibition stabilization and the paradoxical effect in recurrent neural networks are influenced by neuronal nonlinearities and short-term plasticity, showing non-monotonic transitions between different regimes.

The paper examines brain activity during walking in Drosophila, finding global but heterogeneous patterns of neuronal activity associated with both spontaneous and forced walking.

The paper shows that Drosophila uses circuitry beyond photoreceptors to achieve luminance-invariant vision across different timescales through bidirectional gain control.

The paper shows single spikes can trigger repeatable firing sequences in cortical networks through strong and weak connections, enabling controlled propagation and routing of activity.

This paper proposes a novel form of nonlinear inhibitory plasticity that stabilizes excitatory synaptic strengths and enables learning through disinhibition.

This study shows inhibitory synaptic plasticity generates novelty responses in a network model by altering inhibition levels based on stimulus familiarity.

The paper proposes a biophysically motivated synaptic plasticity model to explain synaptic clustering in cortical dendrites across species and scales.

The paper shows that different interneuron subtypes modulate excitatory and inhibitory firing rates differently after sensory deprivation in a cortical network model.

The paper investigates how efficient population coding in neural systems depends on stimulus convergence and noise sources, finding phase transition-like behavior in information transfer.

This study investigates how spontaneous activity patterns in the developing mouse visual cortex contribute to network connectivity through distinct plasticity rules, showing local events shape selectivity and global events regulate connection strength.

This study shows oxytocin reduces spontaneous activity in the visual cortex by enhancing inhibition via somatostatin interneurons, affecting synaptic refinement and sensory processing.

This study identifies how homeostatic mechanisms regulate higher-order network properties in rodents during visual deprivation, revealing distinct roles for synaptic scaling and intrinsic plasticity.

This study shows that non-random neural connectivity can emerge through triplet spike timing dependent plasticity without external input or symmetric STDP rules.