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1. Neural learning rules for generating flexible predictions and computing the successor representation

   https://doi.org/10.7554/eLife.80680  

   Fang, Ching; Aronov, Dmitriy; Abbott, LF; Mackevicius, Emily L (March 2023, eLife)

   Memories are an important part of how we think, understand the world around us, and plan out future actions. In the brain, memories are thought to be stored in a region called the hippocampus. When memories are formed, neurons store events that occur around the same time together. This might explain why often, in the brains of animals, the activity associated with retrieving memories is not just a snapshot of what happened at a specific moment-- it can also include information about what the animal might experience next. This can have a clear utility if animals use memories to predict what they might experience next and plan out future actions. Mathematically, this notion of predictiveness can be summarized by an algorithm known as the successor representation. This algorithm describes what the activity of neurons in the hippocampus looks like when retrieving memories and making predictions based on them. However, even though the successor representation can computationally reproduce the activity seen in the hippocampus when it is making predictions, it is unclear what biological mechanisms underpin this computation in the brain. Fang et al. approached this problem by trying to build a model that could generate the same activity patterns computed by the successor representation using only biological mechanisms known to exist in the hippocampus. First, they used computational methods to design a network of neurons that had the biological properties of neural networks in the hippocampus. They then used the network to simulate neural activity. The results show that the activity of the network they designed was able to exactly match the successor representation. Additionally, the data resulting from the simulated activity in the network fitted experimental observations of hippocampal activity in Tufted Titmice. One advantage of the network designed by Fang et al. is that it can generate predictions in flexible ways,. That is, it canmake both short and long-term predictions from what an individual is experiencing at the moment. This flexibility means that the network can be used to simulate how the hippocampus learns in a variety of cognitive tasks. Additionally, the network is robust to different conditions. Given that the brain has to be able to store memories in many different situations, this is a promising indication that this network may be a reasonable model of how the brain learns. The results of Fang et al. lay the groundwork for connecting biological mechanisms in the hippocampus at the cellular level to cognitive effects, an essential step to understanding the hippocampus, as well as its role in health and disease. For instance, their network may provide a concrete approach to studying how disruptions to the ways neurons make and break connections can impair memory formation. More generally, better models of the biological mechanisms involved in making computations in the hippocampus can help scientists better understand and test out theories about how memories are formed and stored in the brain. 

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2. Fast deep neural correspondence for tracking and identifying neurons in C. elegans using semi-synthetic training

   https://doi.org/10.7554/eLife.66410  

   Yu, Xinwei; Creamer, Matthew S; Randi, Francesco; Sharma, Anuj K; Linderman, Scott W; Leifer, Andrew M (July 2021, eLife)

   Understanding the intricacies of the brain often requires spotting and tracking specific neurons over time and across different individuals. For instance, scientists may need to precisely monitor the activity of one neuron even as the brain moves and deforms; or they may want to find universal patterns by comparing signals from the same neuron across different individuals. Both tasks require matching which neuron is which in different images and amongst a constellation of cells. This is theoretically possible in certain ‘model’ animals where every single neuron is known and carefully mapped out. Still, it remains challenging: neurons move relative to one another as the animal changes posture, and the position of a cell is also slightly different between individuals. Sophisticated computer algorithms are increasingly used to tackle this problem, but they are far too slow to track neural signals as real-time experiments unfold. To address this issue, Yu et al. designed a new algorithm based on the Transformer, an artificial neural network originally used to spot relationships between words in sentences. To learn relationships between neurons, the algorithm was fed hundreds of thousands of ‘semi-synthetic’ examples of constellations of neurons. Instead of painfully collated actual experimental data, these datasets were created by a simulator based on a few simple measurements. Testing the new algorithm on the tiny worm Caenorhabditis elegans revealed that it was faster and more accurate, finding corresponding neurons in about 10ms. The work by Yu et al. demonstrates the power of using simulations rather than experimental data to train artificial networks. The resulting algorithm can be used immediately to help study how the brain of C. elegans makes decisions or controls movements. Ultimately, this research could allow brain-machine interfaces to be developed. 

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3. Growing Brains in Recurrent Neural Networks for Multiple Cognitive Tasks

   Liu, Ziming; Khan, Mikail; Fiete, Ila R.; Tegmark, Max (December 2023, NeurIPS 2023 Workshop NeurReps)

   Recurrent neural networks (RNNs) trained on a diverse ensemble of cognitive tasks, as described by Yang et al. (2019); Khona et al. (2023), have been shown to exhibit functional modularity, where neurons organize into discrete functional clusters, each specialized for specific shared computational subtasks. However, these RNNs do not demonstrate anatomical modularity, where these functionally specialized clusters also have a distinct spatial organization. This contrasts with the human brain which has both functional and anatomical modularity. Is there a way to train RNNs to make them more like brains in this regard? We apply a recent machine learning method, brain-inspired modular training (BIMT), to encourage neural connectivity to be local in space. Consequently, hidden neuron organization of the RNN forms spatial structures reminiscent of those of the brain: spatial clusters which correspond to functional clusters. Compared to standard L1 regularization and absence of regularization, BIMT exhibits superior performance by optimally balancing between task performance and sparsity. This balance is quantified both in terms of the number of active neurons and the cumulative wiring length. In addition to achieving brain-like organization in RNNs, our findings also suggest that BIMT holds promise for applications in neuromorphic computing and enhancing the interpretability of neural network architectures. 

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4. Ex vivo brain preparation to Analyze vocal pathways of Xenopus frogs.

   https://doi.org/10.1101/pdb.prot106872  

   Yamaguchi, Ayako (April 2021, Cold Spring Harbor protocols)

   Understanding the neural basis of behavior is a challenging task for technical reasons. Most methods of recording neural activity require animals to be immobilized, but neural activity associated with most behavior cannot be recorded from an anesthetized, immobilized animal. Using amphibians, however, there has been some success in developing in vitro brain preparations that can be used for electrophysiological and anatomical studies. Here, we describe an ex vivo frog brain preparation from which fictive vocalizations (the neural activity that would have produced vocalizations had the brain been attached to the muscle) can be elicited repeatedly. When serotonin is applied to the isolated brains of male and female African clawed frogs, Xenopus laevis, laryngeal nerve activity that is a facsimile of those that underlie sex-specific vocalizations in vivo can be readily recorded. Recently, this preparation was successfully used in other species within the genus including Xenopus tropicalis and Xenopus victorianus. This preparation allows a variety of techniques to be applied including extracellular and intracellular electrophysiological recordings and calcium imaging during vocal production, surgical and pharmacological manipulation of neurons to evaluate their impact on motor output, and tract tracing of the neural circuitry. Thus, the preparation is a powerful tool with which to understand the basic principles that govern the production of coherent and robust motor programs in vertebrates. 

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5. The connectome of an insect brain

   https://doi.org/10.1126/science.add9330  

   Winding, Michael; Pedigo, Benjamin D.; Barnes, Christopher L.; Patsolic, Heather G.; Park, Youngser; Kazimiers, Tom; Fushiki, Akira; Andrade, Ingrid V.; Khandelwal, Avinash; Valdes-Aleman, Javier; et al (March 2023, Science)

   INTRODUCTION A brainwide, synaptic-resolution connectivity map—a connectome—is essential for understanding how the brain generates behavior. However because of technological constraints imaging entire brains with electron microscopy (EM) and reconstructing circuits from such datasets has been challenging. To date, complete connectomes have been mapped for only three organisms, each with several hundred brain neurons: the nematode C. elegans , the larva of the sea squirt Ciona intestinalis , and of the marine annelid Platynereis dumerilii . Synapse-resolution circuit diagrams of larger brains, such as insects, fish, and mammals, have been approached by considering select subregions in isolation. However, neural computations span spatially dispersed but interconnected brain regions, and understanding any one computation requires the complete brain connectome with all its inputs and outputs. RATIONALE We therefore generated a connectome of an entire brain of a small insect, the larva of the fruit fly, Drosophila melanogaster. This animal displays a rich behavioral repertoire, including learning, value computation, and action selection, and shares homologous brain structures with adult Drosophila and larger insects. Powerful genetic tools are available for selective manipulation or recording of individual neuron types. In this tractable model system, hypotheses about the functional roles of specific neurons and circuit motifs revealed by the connectome can therefore be readily tested. RESULTS The complete synaptic-resolution connectome of the Drosophila larval brain comprises 3016 neurons and 548,000 synapses. We performed a detailed analysis of the brain circuit architecture, including connection and neuron types, network hubs, and circuit motifs. Most of the brain’s in-out hubs (73%) were postsynaptic to the learning center or presynaptic to the dopaminergic neurons that drive learning. We used graph spectral embedding to hierarchically cluster neurons based on synaptic connectivity into 93 neuron types, which were internally consistent based on other features, such as morphology and function. We developed an algorithm to track brainwide signal propagation across polysynaptic pathways and analyzed feedforward (from sensory to output) and feedback pathways, multisensory integration, and cross-hemisphere interactions. We found extensive multisensory integration throughout the brain and multiple interconnected pathways of varying depths from sensory neurons to output neurons forming a distributed processing network. The brain had a highly recurrent architecture, with 41% of neurons receiving long-range recurrent input. However, recurrence was not evenly distributed and was especially high in areas implicated in learning and action selection. Dopaminergic neurons that drive learning are amongst the most recurrent neurons in the brain. Many contralateral neurons, which projected across brain hemispheres, were in-out hubs and synapsed onto each other, facilitating extensive interhemispheric communication. We also analyzed interactions between the brain and nerve cord. We found that descending neurons targeted a small fraction of premotor elements that could play important roles in switching between locomotor states. A subset of descending neurons targeted low-order post-sensory interneurons likely modulating sensory processing. CONCLUSION The complete brain connectome of the Drosophila larva will be a lasting reference study, providing a basis for a multitude of theoretical and experimental studies of brain function. The approach and computational tools generated in this study will facilitate the analysis of future connectomes. Although the details of brain organization differ across the animal kingdom, many circuit architectures are conserved. As more brain connectomes of other organisms are mapped in the future, comparisons between them will reveal both common and therefore potentially optimal circuit architectures, as well as the idiosyncratic ones that underlie behavioral differences between organisms. Some of the architectural features observed in the Drosophila larval brain, including multilayer shortcuts and prominent nested recurrent loops, are found in state-of-the-art artificial neural networks, where they can compensate for a lack of network depth and support arbitrary, task-dependent computations. Such features could therefore increase the brain’s computational capacity, overcoming physiological constraints on the number of neurons. Future analysis of similarities and differences between brains and artificial neural networks may help in understanding brain computational principles and perhaps inspire new machine learning architectures. The connectome of the Drosophila larval brain. The morphologies of all brain neurons, reconstructed from a synapse-resolution EM volume, and the synaptic connectivity matrix of an entire brain. This connectivity information was used to hierarchically cluster all brains into 93 cell types, which were internally consistent based on morphology and known function. 

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