Neuroscience seminars

Distinct synaptic plasticity rules operate across dendritic compartments in vivo during learning

A combinatorial neural code for long-term motor memory

Behavioral time scale synaptic plasticity underlies CA1 place fields

Connectomic reconstruction of a cortical column

Binary and analog variation of synapses between cortical pyramidal neurons

Systems consolidation reorganizes hippocampal engram circuitry

In 2023, the FlyWire Consortium released the neuronal wiring diagram of an adult fly brain. This contains as a corollary the first complete wiring diagram of a visual system, which has been used to identify all 200+ cell types that are intrinsic to the Drosophila optic lobe. About half of these cell types were previously unknown, and less than 20% have ever been recorded by a physiologist. I will argue that plausible functions for many cell types can be guessed by interpreting the wiring diagram. VVTNS Fifth Season Closing Lecture. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2025-06-25. Recording duration: 00:42:39.

Neocortical synaptic engrams for remote contextual memories

Structure and function of the hippocampal CA3 module

Measurements from hippocampal place cells in small enclosures have led to a classical view of highly stereotyped neural tuning functions with smooth, unimodal tuning fields that uniformly tile the environment. However, recent experiments conducted in large spaces across multiple species have revealed a much more irregular neural code than suggested by the classical view. Indeed, place cells in large environments typically fire in multiple locations, and the multiple firing fields of individual cells, as well as those of the entire population, vary considerably in size and shape. We recently showed that a simple mathematical model, wherein firing fields are generated by thresholding realizations of a random Gaussian process, accounts for the statistical properties of place fields in precise quantitative detail. This model captures the observed statistics of field sizes and positions and generates new quantitative predictions on field shapes and topologies. Moreover, these statistics are universal across species, but sensitive to the size and dimensionality of the environment. We quantitatively verified these predictions using multiple recent datasets from bats and rodents in one, two, and three dimensions, across both small and large environments. Collectively, these findings imply that common mechanism underlie the diverse statistical features observed in different experiments and suggest that synaptic projections to CA1 are predominantly random. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2025-05-28. Recording duration: 00:42:45.

Visual restoration after decades of blindness is now becoming possible by means of retinal and cortical prostheses, as well as emerging stem cell and gene therapeutic approaches. After restoring visual perception, however, a key question remains. Are there optimal means and methods for retraining the visual cortex to process visual inputs, and for learning or relearning to “see”? Up to this point, it has been largely assumed that if the sensory loss is visual, then the rehabilitation focus should also be primarily visual. However, the other senses play a key role in visual rehabilitation due to the plastic repurposing of visual cortex during blindness by audition and somatosensation, and also to the reintegration of restored vision with the other senses. I will present multisensory neuroimaging results, cortical thickness changes, as well as behavioral outcomes for patients with Retinitis Pigmentosa (RP), which causes blindness by destroying photoreceptors in the retina. These patients have had their vision partially restored by the implantation of a retinal prosthesis, which electrically stimulates still viable retinal ganglion cells in the eye. Our multisensory and structural neuroimaging and behavioral results suggest a new, holistic concept of visual rehabilitation that leverages rather than neglects audition, somatosensation, and other sensory modalities.

We have entered a golden age of artificial intelligence research, driven mainly by the advances in the artificial neural networks over the last several decades. Applications of these techniques—to machine vision, speech recognition, autonomous vehicles, natural language, and many other domains—are coming so quickly that many observers predict that the long-elusive goal of “Artificial General Intelligence” (AGI) is within our grasp. However, we still cannot build a machine capable of building a nest, stalking prey, or loading a dishwasher. I will describe how evolution may have shaped the algorithms that the brain is using to solve some of these challenging problems. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2025-05-21. Recording duration: 00:44:20.

Synaptic architecture of a memory engram in the mouse hippocampus

My talk will consist of three subtopics. The brain remembers episodes not in isolation but with their contextual relationships, such as spatial or temporal proximity. This is an essential feature of the brain’s memory, but the underlying mechanism is yet to be explored. Cell assemblies, or engrams, may provide neural representations for such relationships. First, I will show a class of associative memory models that encode and retrieve multiple memory contents linked by an arbitrary graph structure through experience and demonstrate the crucial role of the balance between two inhibitory subnetwork types in the flexible retrieval of relational memories. Secondly, I propose a theoretical framework to generate a cognitive map, i.e., neural representations of relationships between memory items. This framework aims at the predictive function of the hippocampus and is based on successor representations proposed for reinforcement learning. Intriguingly, the model provides a unified account for grid cells in spatial navigation and concept cells in natural language processing. Finally, I will discuss another crucial role of the hippocampal memory system, memory replay, in a spiking neural network model. Unlike the conventional associative memory models that maintain attractor memory states, this model attempts to maximize the capacity of replayed activity patterns. Our model suggests the crucial role of inhibitory plasticity in optimizing spontaneous memory replay. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2025-05-14. Recording duration: 00:46:49.

The brain is one of the most energy intense organs. Some of this energyis used for neural information processing, however, fruitfly experiments have shown that also learning is metabolically costly. We will present estimates of this cost and introduce a general model of this cost, and compare it to costs in computers. Next, we turn to a supervised artificial network setting and explore a number of strategies that cansave energy need for plasticity. Either by modifying the objective function, by restricting plasticity, or by using less costly transient forms of plasticity. Finally, we will discuss adaptive strategies and possible relevance for biological learning. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2025-05-07. Recording duration: 00:34:58.

Join Us for the Memory Decoding Journal Club! A collaboration of the Carboncopies Foundation and BPF Aspirational Neuroscience. This time, we’re diving into a groundbreaking paper: "Motor learning selectively strengthens cortical and striatal synapses of motor engram neurons