Claudia Clopath A

This paper introduces a model where neurons adjust synaptic strengths to minimize somato-dendritic mismatch, enabling network learning and reproducing experimental observations of plasticity.

This paper proposes that neural networks can learn by modulating neuron excitability instead of changing synaptic strengths, enabling task switching and robustness against interference.

This paper develops a computational model showing how transient excitability and synaptic consolidation in the hippocampus and cortex stabilize memory despite neural drift.

The paper shows that sleep reverses hippocampal remapping by reinstating familiar representations, and this effect is mediated by prefrontal cortex activity.

This study shows that a common connectivity profile in recurrent networks generates diverse neural sequences, which are maintained across brain regions and species.

The paper shows that left-right alternation in hippocampal theta sequences is driven by medial entorhinal cortex dynamics, not intentional planning.

The paper shows that transcription-specific synaptic plasticity in distinct neuronal ensembles drives memory computations in mice.

This study shows that representational drift in neural networks arises from predictive excitatory-inhibitory plasticity, with changes in neuron preferences and stable ensemble coding.

The paper proposes a model of hippocampal pyramidal neurons that use predictive learning through dendritic compartments and synaptic plasticity to encode spatial and behaviorally relevant features.

The paper shows that temporal predictability determines which evoked activity dimensions are included in spontaneous neural manifolds, affecting their dimensionality and geometry.

The paper studies how auditory cortical neurons integrate natural stimuli, finding they can perform multiple integration functions, unlike artificial networks with fixed activation functions.

The paper develops a model of compartment-specific spike-timing dependent plasticity (STDP) that explains the enrichment of retinotopically displaced inputs on distal dendrites in visual cortex.

This paper reconstructs natural movies from mouse visual cortex activity using single-cell recordings and achieves high-quality reconstructions with pixel-level accuracy.

This study shows how rapid learning in mice involves structured changes in motor cortex connectivity, rerouting preparatory activity to control a conditioned neuron.

This paper proposes a computational framework where the cortico-cerebellar network learns low-dimensional action embeddings and the basal ganglia use reinforcement learning to learn value and policies, demonstrating its relevance to motor learning.

This paper identifies a frontal motor circuit that transforms abstract economic decisions into spatial actions through dynamic reconfiguration.

The paper shows that rats flexibly shift working memory formats between egocentric and allocentric tasks, with different cortical circuits involved based on task structure.

This study shows that PV cell activation improves visual discrimination performance in easy tasks but not difficult ones, with effects depending on activation timing and task difficulty.

This paper introduces a framework called Homeostatic Binary Networks that learns to represent overlapping memory patterns through recurrent dynamics and plasticity mechanisms.

The paper proposes a computational model where semantic vocabulary is shared between episodic and semantic memory, enhancing recall and compositional consolidation through replay mechanisms.

This paper shows that recurrent neural networks can develop compositional representations through unsupervised learning, enabling generalization to novel task combinations.

This paper introduces a novel learning rule called Predictive Alignment for recurrent neural networks that suppresses chaos to generate patterned activities using biologically plausible plasticity.

This study tests if local neural circuits can generate predictive signals using a recurrent spiking network trained with local plasticity rules, replicating experimental prediction error patterns.

This paper introduces a generalized BTSP rule for neural network learning and shows it can perform unsupervised and supervised tasks but faces challenges with network depth.

This paper proposes biologically plausible synaptic plasticity rules to embed stochastic environmental dynamics into spontaneous neural activity in a spiking neural network model.

This study identifies a movement-based dopamine signal in mice that acts as a value-free teaching signal for learning sound-action associations, working alongside reward prediction error.

This study shows that neural signatures of motor memories emerge naturally during learning in a BCI paradigm, reflecting task savings without requiring specific mechanisms.

This study shows that feedback-based motor control and motor adaptation share a common implementation in M1 circuits, as demonstrated through a recurrent neural network trained with error feedback.

The paper identifies a neural pathway involving plHVAs and vLGN that enables learning to suppress fear responses through endocannabinoid-mediated plasticity.

SIMPL optimizes neural representations of behaviour by iteratively refining latent variables and tuning curves, revealing more precise spatial encoding than traditional methods.