Neurons and circuits for odor processing in the piriform cortex

Increased understanding of the early stages of olfaction has lead to a renewed interest in the higher brain regions responsible for forming unified ‘odor images’ from the chemical components detected by the nose. The piriform cortex, which is one of the first cortical destinations of olfactory information in mammals, is a primitive paleocortex that is critical for the synthetic perception of odors. Here we review recent work that examines the cellular neurophysiology of the piriform cortex. Exciting new findings have revealed how the neurons and circuits of the piriform cortex process odor information, demonstrating that, despite its superficial simplicity, the piriform cortex is a remarkably subtle and intricate neural circuit

Cortical-hippocampal processing: prefrontal-hippocampal contributions to the spatiotemporal relationship of events

The hippocampus and prefrontal cortex play distinct roles in the generation and retrieval of episodic memory. The hippocampus is crucial for binding inputs across behavioral timescales, whereas the prefrontal cortex is found to influence retrieval. Spiking of hippocampal principal neurons contains environmental information, including information about the presence of specific objects and their spatial or temporal position relative to environmental and behavioral cues. Neural activity in the prefrontal cortex is found to map behavioral sequences that share commonalities in sensory input, movement, and reward valence. Here I conducted a series of four experiments to test the hypothesis that external inputs from cortex update cell assemblies that are organized within the hippocampus. I propose that cortical inputs coordinate with CA3 to rapidly integrate information at fine timescales. Extracellular tetrode recordings of neurons in the orbitofrontal cortex were performed in rats during a task where object valences were dictated by the spatial context in which they were located. Orbitofrontal ensembles, during object sampling, were found to organize all measured task elements in inverse rank relative to the rank previously observed in the hippocampus, whereby orbitofrontal ensembles displayed greater differentiation for object valence and its contextual identity than spatial position. Using the same task, a follow-up experiment assessed coordination between prefrontal and hippocampal networks by simultaneously recording medial prefrontal and hippocampal activity. The circuit was found to coordinate at theta frequencies, whereby hippocampal theta engaged prefrontal signals during contextual sampling, and the order of engagement reversed during object sampling. Two additional experiments investigated hippocampal temporal representations. First, hippocampal patterns were found to represent conjunctions of time and odor during a head-fixed delayed match-to-sample task. Lastly, I assessed the dependence of hippocampal firing patterns on intrinsic connectivity during the delay period versus active navigation of spatial routes, as rats performed a delayed-alternation T-maze. Stimulation of the ventral hippocampal commissure induced remapping of hippocampal activity during the delay period selectively. Despite temporal reorganization, different hippocampal populations emerged to predict temporal position. These results show hippocampal representations are guided by stable cortical signals, but also, coordination between cortical and intrinsic circuitry stabilizes flexible CA1 temporal representations

AI of Brain and Cognitive Sciences: From the Perspective of First Principles

Nowadays, we have witnessed the great success of AI in various applications, including image classification, game playing, protein structure analysis, language translation, and content generation. Despite these powerful applications, there are still many tasks in our daily life that are rather simple to humans but pose great challenges to AI. These include image and language understanding, few-shot learning, abstract concepts, and low-energy cost computing. Thus, learning from the brain is still a promising way that can shed light on the development of next-generation AI. The brain is arguably the only known intelligent machine in the universe, which is the product of evolution for animals surviving in the natural environment. At the behavior level, psychology and cognitive sciences have demonstrated that human and animal brains can execute very intelligent high-level cognitive functions. At the structure level, cognitive and computational neurosciences have unveiled that the brain has extremely complicated but elegant network forms to support its functions. Over years, people are gathering knowledge about the structure and functions of the brain, and this process is accelerating recently along with the initiation of giant brain projects worldwide. Here, we argue that the general principles of brain functions are the most valuable things to inspire the development of AI. These general principles are the standard rules of the brain extracting, representing, manipulating, and retrieving information, and here we call them the first principles of the brain. This paper collects six such first principles. They are attractor network, criticality, random network, sparse coding, relational memory, and perceptual learning. On each topic, we review its biological background, fundamental property, potential application to AI, and future development.Comment: 59 pages, 5 figures, review articl

Neural Dynamics, Adaptive Computations, and Sensory Invariance in an Olfactory System

Sensory stimuli evoke spiking activities that are patterned across neurons and time in the early processing stages of olfactory systems. What features of these spatiotemporal neural response patterns encode stimulus-specific information (i.e. ‘neural code’), and how they are translated to generate behavioral output are fundamental questions in systems neuroscience. The objective of this dissertation is to examine this issue in the locust olfactory system. In the locust antennal lobe (analogous to the vertebrate olfactory bulb), a neural circuit directly downstream to the olfactory sensory neurons, even simple stimuli evoke neural responses that are complex and dynamic. We found each odorant activated a distinct neural ensemble during stimulus presentation (ON response) and following its termination (OFF response). Our results indicate that the ON and OFF ensemble neural activities differed in their ability to recruit recurrent inhibition, entrain field-potential oscillations, and more importantly in their relevance to behavior (initiate versus reset conditioned responses). Furthermore, when the same stimulus was encountered in a multitude of ways, we found that the neural response patterns in individual neurons varied unpredictably. Intriguingly, a simple, linear logical classifier (OR-of-ANDs) that can decode information distributed in flexible subsets of ON neurons was sufficient to achieve robust recognition. We found that the incorporation of OFF neurons could enhance pattern discriminability and reduce false positives thereby further improving performance. These results indicate that a trade-off between stability and flexibility in sensory coding can be achieved using a simple computational logic. Lastly, we examined how the ON and OFF neural ensembles varied with stimulus intensity. We found that neurons that were ON responsive at low intensity switched and became OFF responsive at higher intensities. Similarly, OFF responsive neurons at low intensity were recruited and responded during odor stimulation at higher intensities. We found a competitive network involving two sub-categories of GABAergic local neurons can mediate this switch between ON and OFF responsive ensembles and how they vary as a function of stimulus intensity. In sum, our results provide a comprehensive understanding of how a relatively simple invertebrate olfactory system could perform complex adaptive computations with very simple individual components

A Model of the Network Architecture of the Brain that Supports Natural Language Processing

For centuries, neuroscience has proposed models of the neurobiology of language processing that are static and localised to few temporal and inferior frontal regions. Although existing models have offered some insight into the processes underlying lower-level language features, they have largely overlooked how language operates in the real world. Here, we aimed at investigating the network organisation of the brain and how it supports language processing in a naturalistic setting. We hypothesised that the brain is organised in a multiple core-periphery and dynamic modular architecture, with canonical language regions forming high-connectivity hubs. Moreover, we predicted that language processing would be distributed to much of the rest of the brain, allowing it to perform more complex tasks and to share information with other cognitive domains. To test these hypotheses, we collected the Naturalistic Neuroimaging Database of people watching full length movies during functional magnetic resonance imaging. We computed network algorithms to capture the voxel-wise architecture of the brain in individual participants and inspected variations in activity distribution over different stimuli and over more complex language features. Our results confirmed the hypothesis that the brain is organised in a flexible multiple core-periphery architecture with large dynamic communities. Here, language processing was distributed to much of the rest of the brain, together forming multiple communities. Canonical language regions constituted hubs, explaining why they consistently appear in various other neurobiology of language models. Moreover, language processing was supported by other regions such as visual cortex and episodic memory regions, when processing more complex context-specific language features. Overall, our flexible and distributed model of language comprehension and the brain points to additional brain regions and pathways that could be exploited for novel and more individualised therapies for patients suffering from speech impairments

Cross-modal correspondences in non-human mammal communication

For both humans and other animals, the ability to combine information obtained through different senses is fundamental to the perception of the environment. It is well established that humans form systematic cross-modal correspondences between stimulus features that can facilitate the accurate combination of sensory percepts. However, the evolutionary origins of the perceptual and cognitive mechanisms involved in these cross-modal associations remain surprisingly underexplored. In this review we outline recent comparative studies investigating how non-human mammals naturally combine information encoded in different sensory modalities during communication. The results of these behavioural studies demonstrate that various mammalian species are able to combine signals from different sensory channels when they are perceived to share the same basic features, either be- cause they can be redundantly sensed and/or because they are processed in the same way. Moreover, evidence that a wide range of mammals form complex cognitive representations about signallers, both within and across species, suggests that animals also learn to associate different sensory features which regularly co-occur. Further research is now necessary to determine how multisensory representations are formed in individual animals, including the relative importance of low level feature-related correspondences. Such investigations will generate important insights into how animals perceive and categorise their environment, as well as provide an essential basis for understanding the evolution of multisensory perception in humans

Functional interactions between the hippocampus, medial entorhinal cortex and medial prefrontal cortex for spatial and nonspatial processing

Memory formation and recall depend on a complex circuit that includes the hippocampus and associated cortical regions. The goal of this thesis was to understand how two of the cortical connections, the medial entorhinal cortex (MEC) and medial prefrontal cortex (mPFC), influence spatial and nonspatial activity in the hippocampus. Cells in the MEC exhibit prominent spatially selective activity and have been hypothesized to drive place representation in the hippocampus. In Experiment 1 the MEC was transiently inactivated using the inhibitory opsin ArchaerhodopsinT (ArchT), and simultaneous recordings from CA1 were made as rats ran on an elliptical track. In response to MEC disruption some cells in the hippocampus shifted the preferred location of activity, some changed firing rate and others were unaffected. The new representation that developed following MEC disruption remained stable despite the fact that inhibition was transient. If the MEC is the source of spatial activity in the hippocampus the activity would be either time-locked to periods of inhibition or unstable throughout the period of inconsistent input. These results show that the MEC guides spatial representation in the hippocampus but does not directly drive spatial firing. The mPFC is generally thought to guide behavior in response to contextual elements. Experiment 2 examined the interaction between the mPFC and the hippocampus as rats performed a contextual discrimination task. Recordings were made in CA1, and the mPFC was disrupted using ArchT during the odor sampling phase of the discrimination. As animals perform this task neurons in the hippocampus respond to a conjunction of odor and location which indicates an association of what and where information in the hippocampus. Optogenetic disruption of the mPFC led to a decrease in nonspatial representation. Individual cells showed lower levels of odor selectivity, but there was no change in the level of spatial representation. This indicates that the mPFC is important for determining how the hippocampus represents nonspatial information but does not alter the spatial representation. The results are discussed within a model of memory formation that includes binding spatial and nonspatial information in the hippocampus