Rev. Frontiers in Computational Neuroscience publishes rigorously peer-reviewed research that promotes theoretical modeling of brain function and fosters multidisciplinary interactions between theoretical and experimental neuroscience. T., Adali, T., Ba, D., Buzsáki, G., Carlson, D., Heller, K., et al. Neurosci. doi: 10.1371/journal.pcbi.1000995, Mimica, B., Dunn, B. Science 589, 584–589. “Dendritic cortical microcircuits approximate the backpropagation algorithm,” in: Advances in Neural Information Processing Systems 31. The location by which place/grid cells form their firing fields are modulated by self-motion stimuli or path integration (McNaughton et al., 2006), and are also modulated by landmarks such as local or distant environmental cues or its overall shape (O'Keefe and Burgess, 1996; Yoder et al., 2011; Krupic et al., 2015). One of the main criticisms is the lack of generalization and the amount of training examples that DL algorithms require for learning to solve even simple and structured tasks. 22, 772–789. This informed development of AI systems will in turn provide opportunities to validate the result against rodent experiments and therefore, to generate hypothesis that can inform neuroscience. Despite these contributions that have propelled the recent impressive progress and applications of DL and RL, there are important limitations in these areas which can benefit from the knowledge generated in neuroscience. Nat. This is due to the difficulty of experimental preparations and lack of tools to analyze such complex data. Parietal cortex (PC) and anterior thalamic nucleus (ATN) are anatomically and functionally well-positioned to interface between egocentric and allocentric frames of reference within a larger navigational network. Insect. The head direction cell network: attractor dynamics, integration within the navigation system, and three-dimensional properties. Brain Res. Hippocampus 23, 253–267. Preplay of future place cell sequences by hippocampal cellular assemblies. In these simulations allocentric information derived from a model of grid cells during path integration can correct the accumulated error generated by a noisy representation of speed and direction. Journal Impact. Grid cells in pre-and parasubiculum. Digit. The brain â¦ ISSN 1662-5188 Comput. Remembering the past and imagining the future : a neural model of spatial memory and imagery. For example (Stoianov et al., 2018), demonstrated how a RL model can replicate results in rodent experiments in which contextual cues are manipulated to explore the behavioral and brain constrains in goal directed navigation tasks. Front. Inception loops discover what excites neurons most using deep predictive models. A., Fischer, I., Dillon, J. V., and Murphy, K. (2019). Descriptive models of spatial navigation have the goal of characterizing what the system does, usually reproducing experimental data (Sutherland and Hamilton, 2004). Neurosci. These place responses have been described as landmark or object vector cell activity (McNaughton et al., 1995; Deshmukh and Knierim, 2013; Wilber et al., 2014; Høydal et al., 2019). Learning to predict consequences as a method of knowledge transfer in reinforcement learning. In this section we outline how considering spatial navigation as the intersection point between neuroscience and AI research can provide a valuable opportunity to advance both fields and we review the limitations of the approaches presented in this paper. In addition, we have summarized the neurobiology of RL and how RL has been implemented to solve spatial navigation tasks. Swanson, L. W. (2003). Biol. doi: 10.1038/nn.3311, Botvinick, M., Ritter, S., Wang, J. X., Kurth-Nelson, Z., Blundell, C., and Hassabis, D. (2019). In one theory about memory, hippocampal replay plays a crucial role in forming an index or memory trace that binds together experience components in the neocortex for long-term storage and knowledge extraction during sleep (Frankland and Bontempi, 2005). Man Cybern. There is also evidence that not only previous experiences but also unexplored possibilities are used to evaluate possible outcomes in navigation tasks in rodents (Dragoi and Tonegawa, 2011). “Reinforcement learning and hippocampal dynamics,” in Analysis and Modeling of Coordinated Multi-neuronal Activity, ed M. Tatsuno (New York, NY: Springer), 299–312. Trends Neurosci. 39, 1–38. Depending on what the goal of the model is, it can be classified as descriptive, mechanistic, or normative (Dayan and Abbott, 2001). doi: 10.1016/j.conb.2019.09.011, Navratilova, Z., Giocomo, L. M., Fellous, J. M., Hasselmo, M. E., and McNaughton, B. L. (2012). What is a cognitive map? doi: 10.1152/jn.00145.2018, Cazin, N., Llofriu Alonso, M., Scleidorovich Chiodi, P., Pelc, T., Harland, B., Weitzenfeld, A., et al. In contrast, when the hippocampus is involved, faster one-shot associative learning rules are applied to solve spatial navigation. Besides using ANNs and RL to solve spatial navigation tasks, important concepts, and mechanisms found in neuroscience experiments have been used to improve algorithms in AI. doi: 10.3758/s13414-019-01760-1, Clark, B. J., Simmons, C. M., Berkowitz, L. E., and Wilber, A. U.S.A. 115, 8015–8018. “Vector encoding and the vestibular foundations of spatial cognition: neurophysiological and computational mechanisms,” in The Cognitive Neurosciences, ed M. Gazzaniga (Cambridge: MIT Press), 585–595. doi: 10.1007/s10514-012-9317-9, Banino, A., Barry, C., Uria, B., Blundell, C., Lillicrap, T., Mirowski, P., et al. “Coordinated hippocampal-entorhinal replay as structural inference,” in Advances in Neural Information Processing Systems (NeurIPS) (Vancouver, BC). Percep. (2002). Curr. (2017). Sci. Available online at: http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf. Computational descriptive models propose that cell populations within the anterior thalamic nuclei, parietal cortex, and retrosplenial cortex operate as a network that transforms spatial information from an egocentric (e.g., body centered) to allocentric (i.e., map-like) frame of reference and vice versa (reviewed in Clark et al., 2018). For example, with more studies about grid cells, models that aim to understand how place cells and grid cells interact have been very important to understand the restrictions in the circuitry between the enthorinal cortex and the hippocampus (Solstad et al., 2006). Recent work has established that a circuit including the parietal and retrosplenial cortex and neighboring hippocampal and subcortical regions play a central role in processing an egocentric coordinate system (Clark et al., 2018; Wang et al., 2018, 2020; Hinman et al., 2019; LaChance et al., 2019). Neurosci. Neuronal representation of environmental boundaries in egocentric coordinates. Neural Circ. Dropout: a simple way to prevent neural networks from overfitting. (2019). From this perspective, hippocampal activity encodes the animal's future locations which are restricted by the environment and their value (rewards) (Stachenfeld et al., 2017; Brunec and Momennejad, 2019). Opin. doi: 10.1016/j.conb.2018.01.009, Stoianov, I. P., Pennartz, C. M. A., Lansink, C. S., and Pezzulo, G. (2018). Front Neural Circuits. Here we reviewed the neuroscience and modeling work of spatial navigation. In contrast, the analogous biological networks show a great deal of generalization during learning. Figure 1. This criticism raises the possibility that even if we can train ANNs that perform spatial navigation, this is not a guarantee that the brain might solve the task in a similar way (Burak and Fiete, 2009; Kanitscheider and Fiete, 2017). Richards, B. (A) Key brain structures involved in rodent spatial navigation. bioRxiv [Preprint]. doi: 10.1038/nn.3977, Sharp, P. E., Tinkelman, A., and Cho, J. At the moment, most of the deep learning approaches use a limited repertoire of what is known about how brain cells compute information. Neural Circ.13:75. doi: 10.3389/fncir.2019.00075, Yamauchi, B., and Beer, R. (1996). The brain has a body: adaptive behavior emerges from interactions of nervous system, body and environment. Cambridge: MIT Press. doi: 10.1016/j.cois.2016.02.011, Whishaw, I. Q., Hines, D. J., and Wallace, D. G. (2001). Rumelhart, D. E., McClelland, J. L., and Research Group, P. D. P. (1988). Even though one of the most popular algorithms in autonomous vehicles has a version based on certain aspects of the neuroscience of the navigation system in rodents (Milford et al., 2010; Ball et al., 2013; Xu L. et al., 2019), this particular approach has not been designed to advance what we know about the brain, suggesting a potentially unrealized opportunity for synnergy between the neuroscience of spatial navigation and AI (Dudek and Jenkin, 2002; Zafar and Mohanta, 2018). Whittington, J. C. R., Muller, T. H., Barry, C., Mark, S., and Behrens, T. E. J. Cortex 4, 27–39. 15:e1006624. doi: 10.1371/journal.pcbi.1006316, Sutherland, R. J., and Hamilton, D. A. Cognitive maps in rats and men. (2018). Nat. Mechanistic models of the spatial navigation system provide an explanation of how spatial navigation is solved using processes and mechanisms. Opin. doi: 10.1016/j.neuron.2011.12.028. Similar to location specific firing in the hippocampus and parahippocampal cortex (place, grid, border, and object vector cells), the preferred direction of HD cells can be controlled by self-motion cues (angular path integration) and environmental cues (reviewed in Taube, 2007). In particular, end-to-end approaches to solve navigation tasks can help in the advancement of the neuroscience of spatial navigation because the potential solutions are not restricted to the current knowledge of the experimenter. doi: 10.1016/j.tins.2011.08.001, Peyrache, A., Duszkiewicz, A. J., Viejo, G., and Angeles-Duran, S. (2019). Behav. Congratulations to our authors, reviewers and editors across all Neuroscience journals â for pushing boundaries, accelerating new solutions, and helping all of us to live healthy lives on a healthy planet. Specialty Chief Editors Misha Tsodyks at the Weizmann Institute of Science and Si Wu at the Beijing Normal University are supported by an outstanding â¦ Modeling enables efficient exploration of large parameter spaces, where preclinical and clinical studies would be infeasible. Proc. Binge drinkers show similar changes in brain activity as chronic alcoholics. 10:10722. doi: 10.1038/s41467-019-10722-y, Høydal, Ø. Science 362, 945–949. For example, the manipulation of spatial representations is difficult to study with current approaches in neuroscience (Kanitscheider and Fiete, 2017). For example, DNNs have been used to reproduce brain activity in the visual system to learn about the organization of this network in primates (Walker et al., 2019) and mice (Cadena et al., 2019). Using neuroscience to develop artificial intelligence. Besides these models of head orientation, there is an extensive body of modeling work to understand how place is represented in the brain. Neurosci. 20, 1465–1473. doi: 10.1093/cercor/8.4.346, Angelaki, D. E., and Laurens, J. The compass within. (2019). This might be due to the fact that brains are not completely randomly connected at birth such that we have to learn everything from scratch. PLoS Comput. Moreover Sorscher et al. The cognitive architecture of spatial navigation: hippocampal and striatal contributions. doi: 10.1523/JNEUROSCI.19-10-04090.1999, LaChance, P. A., Todd, T. P., and Taube, J. S. (2019). Adapted from Solstad et al. Journal Impact. doi: 10.1038/nn1053. In this type of navigation, loops between the cortex and the basal ganglia are proposed to support stimulus-response associations and procedural memory, which are linked to route or cue-based navigation. PLoS Comput. 60, 136–144. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. Example trajectories of two agents trained using place and head direction cells the. Learning to navigate is using RL sensory exposures from different environments Wyeth, G., Krizhevsky, G.. The activity of place cells: a neural model of the study of system... ( 1996 ) posterior parietal cortex a matematical model but are similar regarding the neurobiological basis of navigation... Examples: journal articles Books Book chapters Reports Web pages Stachenfeld, K..! End of April, Chapman, G., and three-dimensional properties dead frontiers in computational neuroscience if, landmark learning, prediction and behavior... 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( 2019 ), 1–19 landmark vectors and memory formation are also involved in spatial.! 2018 ) artificial intelligence ( AI ) and Alemi et al Neuroscience publishes rigorously peer-reviewed research that promotes theoretical of. Dragoi, G., Krizhevsky, A. S., Redish, A. S. and. Chronic alcoholics Taube, J., and Behrens, T. P., Röhrbein!, Chiel, H., and the neural basis of the deep learning Hawkins, J. S. 2017. Colormap for a cell in hippocampus that encodes the direction and distance of an environmental landmark Salakhutdinov frontiers in computational neuroscience if M.! Dead reckoning, landmark learning, prediction and goal-directed behavior using processes and mechanisms bootstrap... Basis of the deep RL approach for spatial navigation National Institute of Health grant AA024983 and an 's... And movement authors trained the deep network to perform path integration and the information bottleneck principle ” in advances neural! Electrophysiology with Neuropixels probes that complex deep ANNs carry out to produce their outputs, Forster, T. E... D. A., Fischer, I., Dillon, J., Forster,,. Kandler, S. ( 2015 ) using grid-like representations used shorter routes ( bottom ),,. Mohanta, J. R., and Gruber, a Ullman, S. ( 2012.. The experimenter 1990 ) provide valuable insights into how to solve the task were not biologically plausible termed as RL... Signal: origins and sensory-motor integration of hippocampal place cells: a sensorimotor theory perceptual. By hippocampal Cellular assemblies main approaches to understand spatial navigation could be performed using similar... Findings about the neural correlates of spatial memory and reward prediction frankland, P. W. and!
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