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1. A Normative Theory for Causal Inference and Bayes Factor Computation in Neural Circuits

   Zhang, Wenhao ; Wu, Si ; Doiron, Brent ; Lee, Tai Sing ( January 2019 , Advances in neural information processing systems)

   This study provides a normative theory for how Bayesian causal inference can be implemented in neural circuits. In both cognitive processes such as causal reasoning and perceptual inference such as cue integration, the nervous systems need to choose different models representing the underlying causal structures when making inferences on external stimuli. In multisensory processing, for example, the nervous system has to choose whether to integrate or segregate inputs from different sensory modalities to infer the sensory stimuli, based on whether the inputs are from the same or different sources. Making this choice is a model selection problem requiring the computation of Bayes factor, the ratio of likelihoods between the integration and the segregation models. In this paper, we consider the causal inference in multisensory processing and propose a novel generative model based on neural population code that takes into account both stimulus feature and stimulus reliability in the inference. In the case of circular variables such as heading direction, our normative theory yields an analytical solution for computing the Bayes factor, with a clear geometric interpretation, which can be implemented by simple additive mechanisms with neural population code. Numerical simulation shows that the tunings of the neurons computing Bayes factor are consistent with the "opposite neurons" discovered in dorsal medial superior temporal (MSTd) and the ventral intraparietal (VIP) areas for visual-vestibular processing. This study illuminates a potential neural mechanism for causal inference in the brain. 

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2. Multisensory integration by polymodal sensory neurons dictates larval settlement in a brainless cnidarian larva

   https://doi.org/10.1111/mec.16968  

   Birch, Sydney ; Plachetzki, David ( May 2023 , Molecular Ecology)

   Multisensory integration (MSI) combines information from more than one sensory modality to elicit behaviours distinct from unisensory behaviours. MSI is best understood in animals with complex brains and specialized centres for parsing different modes of sensory information, but dispersive larvae of sessile marine invertebrates utilize multimodal environmental sensory stimuli to base irreversible settlement decisions on, and most lack complex brains. Here, we examined the sensory determinants of settlement in actinula larvae of the hydrozoanEctopleura crocea(Cnidaria), which possess a diffuse nerve net. A factorial settlement study revealed that photo‐, chemo‐ and mechanosensory cues each influenced the settlement response in a complex and hierarchical manner that was dependent on specific combinations of cues, an indication of MSI. Additionally, sensory gene expression over development peaked with developmental competence to settle, which in actinulae, requires cnidocyte discharge. Transcriptome analyses also highlighted several deep homological links between cnidarian and bilaterian mechano‐, chemo‐, and photosensory pathways. Fluorescent in situ hybridization studies of candidate transcripts suggested cellular partitioning of sensory function among the few cell types that comprise the actinula nervous system, where ubiquitous polymodal sensory neurons expressing putative chemo‐ and photosensitivity interface with mechanoreceptive cnidocytes. We propose a simple multisensory processing circuit, involving polymodal chemo/photosensory neurons and mechanoreceptive cnidocytes, that is sufficient to explain MSI in actinulae settlement. Our study demonstrates that MSI is not exclusive to complex brains, but likely predated and contextualized their evolution.

    

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3. Sensory prosthetics - clinical and scientific utility of a vestibular implant

   Haburcakova, C. ; Merfeld, D. ; Gong, W. ; Guinand, N. ; Perez, Fornos A. ; Thompson, L.A. ; Guyot, J.P. ; Lewis, R.F. ( April 2017 , Neurology)

   Objective: To determine if a vestibular prosthesis could improve function in subjects with severe vestibular damage and could be used it as a scientific tool to investigate central vestibular processing. Background: Damage to the vestibular labyrinth is common and usually permanent. We therefore developed and tested a vestibular implant (VI) that is designed to mimic the information normally provided by the vestibular labyrinth to determine if we can reduce vestibular-mediated deficits and study temporal integration of sensory cues in the brain. Design/Methods: Monkeys had electrodes implanted in the semicircular canals of one ear and then severe bilateral vestibular damage was induced with aminoglycosides. Eye movements, perception, and balance were tested before and after vestibular damage and with the VI activated, which supplied head motion information to the brain via electrical stimulation delivered by the implanted electrodes. Humans also had electrode implantation (done in conjunction with a cochlear implant, CI) and they were tested on a temporal binding psychophysical task Results: Stimulation provided by VI in vestibulopathic monkeys improved their balance, perception of spatial orientation, and eye movement responses. Timing experiments in humans using CI and VI stimuli showed that unlike past experiments that used motion to generate the vestibular signal, CI and VI signals were received by the cerebral cortex with the same latency and were perceived as simultaneous, but this timing perception was highly sensitive to adaption. Conclusions: VI improves oculomotor, postural, and perceptual behavior in vestibulopathic monkeys and could prove to be an effective way to improve these functions in patients with permanent labyrinthine damage. Timing experiments show that when novel stimuli are used, the brain synthesizes them in accordance with their arrival at the cortex, but that experience can rapidly recalibrate this timing relationship, which may be why normal stimuli that are experienced habitually lack this characteristic. 

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4. 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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5. Multisensory integration in the mouse cortical connectome using a network diffusion model

   https://doi.org/10.1162/netn_a_00164  

   Shadi, Kamal ; Dyer, Eva ; Dovrolis, Constantine ( January 2020 , Network Neuroscience)

   null (Ed.)

   Author Summary Having a structural network representation of connectivity in the brain is instrumental in analyzing communication dynamics and neural information processing. In this work, we make steps towards understanding multisensory information flow and integration using a network diffusion approach. In particular, we model the flow of evoked activity, initiated by stimuli at primary sensory regions, using the asynchronous linear threshold (ALT) diffusion model. The ALT model captures how evoked activity that originates at a given region of the cortex “ripples through” other brain regions (referred to as an activation cascade). We apply the ALT model to the mouse connectome provided by the Allen Institute for Brain Science. A first result, using functional datasets based on voltage-sensitive dye (VSD) imaging, is that the ALT model, despite its simplicity, predicts the temporal ordering of each sensory activation cascade quite accurately. We further apply this model to study multisensory integration and find that a small number of brain regionsthe claustrum and the parietal temporal cortex being at the top of the listare involved in almost all cortical sensory streams. This suggests that the cortex relies on an hourglass architecture to first integrate and compress multisensory information from multiple sensory regions, before utilizing that lower dimensionality representation in higher level association regions and more complex cognitive tasks. 

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