Forgotten memories in Drosophila persist as silent memory traces and can be recovered by reminder cues. Manipulating reminders can create false memories, showing that memory reconstruction flexibly in...

Biological and neuromorphic recurrent neural networks (RNNs) are subject to spatial and temporal locality constraints on the information that can plausibly be used during learning. A common strategy t...

Galloni et al. introduce “dendritic target propagation”: a Dale’s law-compliant learning algorithm for cortical microcircuits with soma- and dendrite-targeting inhibition and realistic connectivity co...

There are several plausible algorithms for cortical function that are specific enough to make testable predictions of the interactions between functionally identified cell types. Many of these algorithms are based on some variant of predictive processing. Here we set out to experimentally distinguish between two such predictive processing variants. A central point of variability between them lies in the proposed vertical communication between layer 2/3 and layer 5, which stems from the diverging assumptions about the computational role of layer 5. One assumes a hierarchically organized architecture and proposes that, within a given node of the network, layer 5 conveys unexplained bottom-up input to prediction error neurons of layer 2/3. The other proposes a non-hierarchical architecture in which internal representation neurons of layer 5 provide predictions for the local prediction error neurons of layer 2/3. We show that the functional influence of layer 2/3 cell types on layer 5 is incompatible with the hierarchical variant, while the functional influence of layer 5 cell types on prediction error neurons of layer 2/3 is incompatible with the non-hierarchical variant. Given these data, we can constrain the space of plausible algorithms of cortical function. We propose a model for cortical function based on a combination of a joint embedding predictive architecture (JEPA) and predictive processing that makes experimentally testable predictions. ### Competing Interest Statement The authors have declared no competing interest. Swiss National Science Foundation, https://ror.org/00yjd3n13 Novartis Foundation, https://ror.org/04f9t1x17 European Research Council, https://ror.org/0472cxd90, 865617

Spatial attention supports navigation by prioritizing information from selected locations. A candidate neural mechanism is provided by theta-paced sweeps in grid- and place-cell population activity, which sample nearby space in a left-right-alternating pattern coordinated by parasubicular direction signals. During exploration, this alternation promotes uniform spatial coverage, but whether sweeps can be flexibly tuned to locations of particular interest remains unclear. Using large-scale Neuropixels recordings in freely-behaving rats, we show that sweeps and direction signals are rapidly and dynamically modulated: they track moving targets during pursuit, precede orienting responses during immobility, and reverse during backward locomotion — without prior spatial learning. Similar modulation occurs during REM sleep. Canonical head-direction signals remain head-aligned. These findings identify sweeps as a flexible, attention-like mechanism for selectively sampling allocentric cognitive maps. ### Competing Interest Statement The authors have declared no competing interest. European Research Council, Synergy Grant 951319 (EIM) The Research Council of Norway, Centre of Neural Computation 223262 (EIM, MBM), Centre for Algorithms in the Cortex 332640 (EIM, MBM), National Infrastructure grant (NORBRAIN, 295721 and 350201) The Kavli Foundation, https://ror.org/00kztt736 Ministry of Science and Education, Norway (EIM, MBM) Faculty of Medicine and Health Sciences; NTNU, Norway (AZV)

Careers on Simons Foundation

Author summary Place cells of the hippocampus form unique activity patterns in different environments, a process called remapping. However, it is not clear what the relationship is between changes in ...

Artificial neural networks applied to psychology, neuroscience, and climate change

Artificial intelligence is rapidly advancing our mechanistic understanding of the shared structure between the brain and higher-order behaviours. In this Review, Mathis and Mathis synthesize state-of-...

Scenes are complex, yet structured collections of parts, including objects and surfaces, that exhibit spatial and semantic relations to one another. An effective visual system therefore needs unified ...

Neural networks rely on learning synaptic weights. However, this overlooks other neural parameters that can also be learned and may be utilized by the brain. One such parameter is the delay: the brain...

Spiking Neural networks as Universal Function Approximators

Spiking Neural networks as Universal Function Approximators

Spiking Neural networks as Universal Function Approximators

Generative biology holds the promise to transform our ability to design and understand living systems by creating novel proteins, pathways, and organisms with tailored functions that address challenge...

Spiking Neural networks as Universal Function Approximators

Neuroscientist Magdalena Solyga shares her latest study and her experience publishing with eLife.

Simons Computational Neuroscience Imbizo summer school in Cape Town, South Africa

Synaptic plasticity is the basis of learning and memory, but the link between synaptic changes and neural function remains elusive. Here, we used automated search algorithms to obtain thousands of str...

FMI researchers have provided new insights into how the brain organizes and processes memories, thanks to a study that looks at the balance between excitatory and inhibitory neurons. Memory networks h...