Flexible cognition utilizes reusable components to enable rapid adaptation of behavior to different contexts or tasks. Analysis of artificial neural networks trained on multiple tasks suggested that t...

Drowning is not the violent, splashing call for help that most people expect.

In this issue of Neuron, Pezon et al. introduce neural circuit models with flexible connectivity structure that can generate low-dimensional population dynamics with different distributions of single-...

Large nonlinear recurrent neural networks with random couplings generate high-dimensional, potentially chaotic activity whose structure is of interest in neuroscience, machine learning, ecology, and o...

Dimensionality reduction methods are widely used in neuroscience to investigate two complementary aspects of neural activity: the distribution of sing…

Neural population activity can be described either by low-dimensional dynamics on neural manifolds or by single-neuron selectivities. Using a theoretical approach, Pezon et al. relate these two statistical descriptions to circuit structure in recurrent networks. Their results reveal both degeneracies and specific constraints in how circuit structure shapes neural activity.

WWTNS is a weekly digital seminar on Zoom targeting the theoretical neuroscience community. Its aim is to be a platform to exchange ideas among theoreticians.

Neural activity during the performance of a stereotyped behavioral task is often described as low-dimensional, occupying only a limited region in the space of all firing-rate patterns. This region has...

Animals must exploit environmental regularities to make adaptive decisions, yet the learning algorithms that enabels this flexibility remain unclear. A central question across neuroscience, cognitive science, and machine learning, is whether learning relies on generative or discriminative strategies. Generative learners build internal models the sensory world itself, capturing its statistical structure; discriminative learners map stimuli directly onto choices, ignoring input statistics. These strategies rely on fundamentally different internal representations and entail distinct computational trade-offs: generative learning supports flexible generalisation and transfer, whereas discriminative learning is efficient but task-specific. We compared humans, rats, and mice performing the same auditory categorisation task, where category boundaries and rewards were fixed but sensory statistics varied. All species adapted their behaviour near-optimally, consistent with a normative observer constrained by sensory and decision noise. Yet their underlying algorithms diverged: humans predominantly relied on generative representations, mice on discriminative boundary-tracking, and rats spanned both regimes. Crucially, end-point performance concealed these differences, only learning trajectories and trial-to-trial updates revealed the divergence. These results show that similar near-optimal behaviour can mask fundamentally different internal representations, establishing a comparative framework for uncovering the hidden strategies that support statistical learning. ### Competing Interest Statement The authors have declared no competing interest. Wellcome Trust, https://ror.org/029chgv08, 219880/Z/19/Z, 225438/Z/22/Z, 219627/Z/19/Z Gatsby Charitable Foundation, GAT3755 UK Research and Innovation, https://ror.org/001aqnf71, EP/Z000599/1

The authors show that connectome datasets alone are generally not sufficient to predict neural activity. However, pairing connectivity information with neural recordings can produce accurate predictio...

How can we make sense of large-scale recordings of neural activity across learning? Theories of neural network learning with their origins in statistical phy...

The architecture of early olfactory processing is a striking example of convergent evolution. Typically, a panel of broadly tuned receptors is selectively expressed in sensory neurons (each neuron exp...

In biological neural circuits, the dynamics of neurons and synapses are tightly coupled. We study the consequences of this coupling and show that it enables a novel form of working memory. In recurren...

Nature Reviews Neuroscience - The goal of theoretical neuroscience is to uncover principles of neural computation through careful design and interpretation of mathematical models. Here, I examine...

Studies of the dynamics of nonlinear recurrent neural networks often assume independent and identically distributed couplings, but large-scale connectomics data indicate that biological neural circuit...

Hardcastle and Marshall et al. show that striatal function is domain specific, required for task-related but not spontaneously expressed movements. This functional distinction is reflected in starkly ...

Webseiten der Georg-August-Universität Göttingen

Neural circuits comprise multiple interconnected regions, each with complex dynamics. The interplay between local and global activity is thought to...

Frozen stabilization enables small unstructured neural networks to form robust continuous memories and exhibit separation of timescales without fine-tuning or symmetries.

The latent circuit model identifies low-dimensional mechanisms of task execution from heterogenous neural responses. This approach reveals a latent inhibitory mechanism for context-dependent decisions...

The latent circuit model identifies low-dimensional mechanisms of task execution from heterogenous neural responses. This approach reveals a latent inhibitory mechanism for context-dependent decisions...

A major challenge in neuroscience is reconciling idealized theoretical models with complex, heterogeneous experimental data. We address this challenge through the lens of continuous-attractor networks...