Instead, ANNs are trained to solve spatial navigation tasks and the representations and parameters employed by the network are restricted to match biological constraints. 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). Science 318, 1147–1150. 4.52 %. arXiv:1811.01768. News • OCNS is now a member of the INCF. Natl. Frontiers in Computational Neuroscience; Abbreviation. More recently, with the advancement in ANNs, there are more AI end-to-end (normative) approaches to model spatial navigation in which the parameters that determine the representations and how they are exploited are not specified explicitly. 16, 309–317. Neural basis of reinforcement learning and decision making. Thus, the activity hill is organized to move corresponding to the animal's current HD. Swanson, L. W. (2003). Moreover, in a related study, it is shown how these transformations relate to short and long-term memory (Krichmar and Edelman, 2005). Despite much room for future growth, the early joint efforts between AI and neuroscience to understand the neural substrates for spatial navigation is gaining traction and is becoming an exciting and promising approach. 19, 166–180. (2017). “Generalisation of structural knowledge in the hippocampal-entorhinal system,” in Proceedings of the International Conference of Neural Information Processing Systems (NeurIPS), 8484–8495. (2011). Annu. (2013). Understanding how mind emerges from matter is one of the great remaining questions in science. Comput. doi: 10.1126/science.aat6766, Bermudez Contreras, E., Buxton, H., and Spier, E. (2008). doi: 10.1038/nature09633. Offline replay supports planning in human reinforcement learning. In sum, this model demonstrates how sensory information can be used to support spatial localization by transforming egocentric information into a location in the environment. Reinforcement learning, fast and slow. Constr. In other words, transform body centered encoding of a landmark into map-like landmark representations (e.g., a cell that fires in a specific map-like location relative to a landmark independent of which direction the animal is facing; Figure 2D). Vestibular and attractor network basis of the head direction cell signal in subcortical circuits. doi: 10.1126/science.aax4192, Lee, D., Seo, H., and Jung, M. W. (2012). Phase precession and variable spatial scaling in a periodic attractor map model of medial entorhinal grid cells with realistic after-spike dynamics. J. Neurosci. In the subiculum, these “border” cells can also discharge at specific distances relative to a boundary (Lever et al., 2009). Sci. Learning to predict consequences as a method of knowledge transfer in reinforcement learning. Neuron 87, 507–520. “Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems,” in Advances in Neural Information Processing Systems (NIPS), (Long Beach, CA), 4530–4539. (1994). Here we reviewed the neuroscience and modeling work of spatial navigation. (2017). The spatial representations that ANNs use to solve spatial navigation resemble different properties reported in rodent experiments. (2006), used with permission. Front. doi: 10.1038/nrn2258. 14:63. doi: 10.3389/fncom.2020.00063. For example, an allocentric to egocentric transformation may allow a subject to select an action (turn left) at a specific intersection (a particular allocentric location and orientation) in a city. Rodent spatial navigation: At the crossroads of cognition and movement. Sorscher, B., Mel, G. C., Ganguli, S., and Ocko, S. A. doi: 10.1016/j.neunet.2018.10.017, Xu, L., Feng, C., Kamat, V. R., and Menassa, C. C. (2019). The Organization of Learning. 61, 85–117. (2003). Neuron 100, 490–509. Cereb. “A model of the neural basis of the rat's sense of direction,” in Proceedings of the Seventh International Conference of Neural Information Processing Systems (NIPS), (Denver, CO), 173–180. 101, 19–34. Neurosci. Acad. In a traditional approach to modeling brain function, such as spatial navigation, the model parameters are specified by the experimenter and optimized to reproduce experimental data. We propose that spatial navigation is an excellent area in which these two disciplines can converge to help advance what we know about the brain. In addition, we describe modeling work on reinforcement learning which has been important for the development of end-to-end AI approaches that tackle spatial navigation tasks. There are limitations and criticisms for these contributions in neuroscience. doi: 10.1037/0735-7044.115.3.571, Sinz, F. H., Pitkow, X., Reimer, J., Bethge, M., and Tolias, A. S. (2019). In AI, this includes the development of computer programs that can beat a grandmaster at GO or outperform human radiologists at cancer detection. Opin. In parallel, neuroscience has also experienced significant advances in understanding the brain. doi: 10.1038/s41586-019-1077-7. After, we review the models used to study these structures and the processes involved in spatial navigation. Behav. A neuroscience-inspired mechanism to reduce the number of required exposures for learning that is also implemented by structures involved in spatial navigation, is to use previous experiences to select possible actions for new situations. In the trained RNN, they found a grid-cell like representation of space in which a hexagonal periodic pattern of activity was used to keep track of the location of the agent in the environment. The modeling complexity of the activity of place cells largely varies depending on the goal of the study. In contrast, biological systems can learn complex tasks quickly and extract semantic knowledge from a relatively small number of instances. 120, 2877–2896. Neuronal computations with stochastic network states. The hippocampus contains neurons that discharge in specific environmental locations (Figure 2A; O'Keefe and Dostrovsky, 1971) such that populations of these cells encode the present position much like a GPS (O'Keefe and Nadel, 1978). Pfeifer, R., and Scheier, C. (1999). Cybern. FRONTIERS IN COMPUTATIONAL NEUROSCIENCE Status 3 Public; Date Range. Evans, T., and Burgess, N. (2019). In this framework, spatial representations are learned by interacting with the environment instead of provided by the experimenter. Computational Principles of Mobile Robotics. 7, 663–678. Inspired by Schultheiss and Redish (2015), used with permission. 133, 141–152. One important aspect of the representations derived from ANNs is their robustness. “Learning to navigate in complex environments,” in International Conference on Learning Representations (ICLR). (2018). Psychol. Overall, despite the great advances in the understanding of the neurobiology of the spatial navigation system in rodents, there are important open questions in neuroscience that can benefit from AI approaches. The Standard Abbreviation (ISO4) of Frontiers in Computational Neuroscience is “Front. Nature 518, 232–235. It has been studied in conjunction with many other topics in neuroscience and psychology including awareness, vigilance, saliency, executive control, and learning. Theoretical Neuroscience. (2018), used with permission. (B) Grid cells in the medial entorhinal cortex (MEC) at different scales (top) and place cells in the hippocampus (HPC) with different scales (bottom). 20, 1643–1653. 14, 1–28. In AI, this includes the development of computer programs that can beat a grandmaster at GO or outperform human radiologists at cancer detection. The biological validity of these conjunctive cells is supported by recent work (Wilber et al., 2014). Euston, D. R., Tatsuno, M., and McNaughton, B. L. (2007). doi: 10.1109/TNNLS.2017.2690910, Chen, L. L., Lin, L. H., Green, E. J., Barnes, C. A., and McNaughton, B. L. (1994). Information about the open-access journal Frontiers in Computational Neuroscience in DOAJ. Digit. Parallel Distributed Processing, Vol. Some theoretical work suggests that allocentric and egocentric frames of reference can operate sequentially such that information is decoded to determine a subject's egocentric orientation in the environment and vice versa (McNaughton et al., 1995; Byrne and Becker, 2007; Burgess, 2008; Clark et al., 2018). Commun. Science 352, 1464–1468. Acad. In summary, this end-to-end approach in which ANNs are used to model brains in embodied agents that learn to navigate in space using relevant biological restrictions provides a promising tool to study the representations of space that might resemble those used in nature and further our understanding of how such spatial representations may “emerge.”. Momennejad, I., Otto, A. R., Daw, N. D., and Norman, K. A. doi: 10.1016/j.cois.2016.02.011, Whishaw, I. Q., Hines, D. J., and Wallace, D. G. (2001). (2019). Cullen, K. E., and Taube, J. S. (2017). (2005). Rev. Recent studies in humans link these mechanisms for decision making, in which model-free choice guides route-based navigation and model-based choice directs map-based navigation (Anggraini et al., 2018). Research is often a slow process, requiring the careful design, optimization, and replication of experiments. The way that the brain performs spatial navigation might provide valuable insights into how to solve this limitation in current AI methods. Rumelhart, D. E., McClelland, J. L., and Research Group, P. D. P. (1988). 263, 242–261. Trends Neurosci. London: MIT Press. This knowledge can inform and guide some of the parameters used in artificial agents solving spatial navigation tasks. Purple boxes represent brain structures involved in value-based signals for conditional learning and spatial navigation (The basal ganglia circuit sub-diagram was inspired from Chersi and Burgess, 2015, used with permission). Some of these recent advances in the neuroscience of spatial navigation led to the Nobel Prize in Medicine being awarded to John O'Keefe and Edvard and May-Brit Moser (Colgin, 2019). (2013). Cambridge, MA: MIT Press. (2016). (2019). |, Models for Spatial Navigation and Their Contribution to the Understanding of the Brain, https://science.sciencemag.org/content/362/6414/584, http://papers.nips.cc/paper/8089-dendritic-cortical-microcircuits-approximate-the-backpropagation-algorithm.pdf, http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf, Creative Commons Attribution License (CC BY). Neurosci. One proposal to address this is to start with the minimal cognitive functions that gave rise to navigation in simple organisms (Yamauchi and Beer, 1996; Beer and Williams, 2015). Here we briefly describe some of the key findings about the neural correlates of spatial navigation and the computational bases of these neural substrates. Indexed in: PubMed, PubMed Central (PMC), Scopus, Web of Science Science Citation Index Expanded (SCIE), Google Scholar, DOAJ, CrossRef, Embase, as well as being searchable via the Web of Knowledge, Digital Biography & Library Project (dblp), PMCID: all published articles receive a PMCID. Giocomo, L. M., Moser, M. B., and Moser, E. I. (1995) model an egocentric-to-allocentric transformation using a linear mapping across a three-layered neural circuit. Sci. (2018), used deep learning in simulated agents to study how space representations can be used to facilitate flexible navigation strategies that closely resemble experimental data from rodents. Trends Cogn. Neurobiol. Neurosci. 9, 292–303. J. Mach. Hippocampus (HPC) and parahippocampal regions (entorhinal cortex, postsubiculum, and parasubiculum) encode an animal's position in space predominantly in allocentric or map-like coordinates. From the AI perspective, one of the main criticisms of the current development of DL and AI in trying to understand the brain is that, recently, the main focus of such developments has been to exploit the computational power of deep architectures for technological advancement. 81, 2265–2287. Rev. The brain has a body: adaptive behavior emerges from interactions of nervous system, body and environment. Generating sequences with recurrent neural networks. Resynthesizing behavior through phylogenetic refinement. Mechanistic models of the spatial navigation system provide an explanation of how spatial navigation is solved using processes and mechanisms. Psychol. 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). Currently much of this potential synergy is not being realized. doi: 10.1038/nature14622, Krupic, J., Bauza, M., Burton, S., Barry, C., and O'Keefe, J. More information will be made available shortly. 104, 230–245. Grid cells in pre-and parasubiculum. Science 85, 85–90. (A) Example place cell recorded in hippocampus, top row is a spike/path plot, red dots represent the locations of action potentials and black lines the path of the animal. In this work, the authors were able to ascertain which constraints favor the hexagonal activation pattern of grid cell like representation emerged in three different network architectures. Taube, J., Muller, R., and Ranck, J. Jr. (1990). The Brain as a Computer? (2002). Frontiers in Computational Neuroscience (NOVEMB). AW was supported through the National Institute of Health grant AG049090 and the Florida Department of Health grant 20A09. Thus, the parietal and retrosplenial cortex may be part of a circuit that interfaces between allocentric and egocentric frames of reference (Pennartz et al., 2011; Stoianov et al., 2018). doi: 10.1371/journal.pbio.3000516, Sacramento, J., Bengio, Y., Costa, R. P., and Senn, W. (2018). Historically, Artificial Intelligence (AI) researchers followed this approach. Neurosci. From this point of view, AI can greatly benefit from applying general principles that real brains employ to solve complex tasks. Learn. 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). Modeling work has followed two approaches to study the organization of grid cells (Figure 3B). Frontiers in Computational Neuroscience Vols. New York, NY: Wiley. Coding of navigational affordances in the human visual system. 22, 772–789. Behav. doi: 10.1073/pnas.1618228114, Bonnevie, T., Dunn, B., Fyhn, M., Hafting, T., Derdikman, D., Kubie, J. L., et al. 50, 92–100. (2010). Path integration and the neural basis of the “cognitive map”. 13, 1–24. 55, 189–208. In this case, these processes are thought to be implemented by the interaction between the hippocampus and the ventral striatum (Pennartz et al., 2011). Biol. “Dendritic cortical microcircuits approximate the backpropagation algorithm,” in: Advances in Neural Information Processing Systems 31. However, given the common initial motivation point—to understand the brain—these disciplines could be more strongly linked. You can google some of the scandal surrounding their practices if you want more info on the … doi: 10.1038/416090a. Functional split between parietal and entorhinal cortices in the rat. Neuron 103, 967–979. doi: 10.1523/JNEUROSCI.0508-17.2018, Walker, E. Y., Sinz, F. H., Cobos, E., Muhammad, T., Froudarakis, E., Fahey, P. G., et al. The cells that encode space in allocentric or map-like coordinates are generally found in the hippocampal formation and several limbic-thalamic and limbic-cortical regions. Science 363, 692–693. In this section we summarize how RL modeling has been successfully integrated in AI approaches to understand spatial navigation. (2013). Child. Behavioral constraints in the development of neuronal properties: a cortical model embedded in a real-world device. Thalamocortical processing of the head-direction sense. doi: 10.1016/j.conb.2018.01.009, Stoianov, I. P., Pennartz, C. M. A., Lansink, C. S., and Pezzulo, G. (2018). doi: 10.1002/hipo.20939, Nitz, D. A. The editor and reviewers' affiliations are the latest provided on their Loop research profiles and may not reflect their situation at the time of review. Nat. 42:e215. 6:79. doi: 10.3389/fnbeh.2012.00079, Knierim, J. J., and Hamilton, D. A. (Toulon). Major advances in our understanding of how the brain is involved in spatial navigation has been achieved in part, due to modeling work. From a neuroscience perspective, being able to actively explore the world might have been one of the key factors that provided organisms evolutionary advantages that triggered the development of cognitive process such as prediction, attention, learning and memory (Swanson, 2003). 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. Grid cells require excitatory drive from the hippocampus. Second, by using spatial navigation as a problem to be solved by artificial systems that follow biologically relevant restrictions, we can use this as a “sandbox” to improve our analytical tools. A deep learning framework for neuroscience. 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). doi: 10.3758/s13414-019-01760-1, Clark, B. J., Simmons, C. M., Berkowitz, L. E., and Wilber, A. We briefly summarize the area of reinforcement learning and the brain structures that are involved in the process of sequential decision making that are crucial to navigate. (C) Relationship between episodic and semantic memories and path integration and model-based navigation. 3, 190–202. One obvious and already successful interaction between AI and Neuroscience is to use machine learning (ML), an area of AI that applies computer science and statistical techniques in data analysis, to study complex and large datasets in Neuroscience (Vogt, 2018; Vu et al., 2018). Using grid cells for navigation. During spatial navigation, learning can occur first as a trial-and-error process that links memory and reward or punishment signals. Experimental support for this theory is derived from studies that require rodents (and humans) to solve navigational tasks where the goal location is not visible from an animals current location (Knierim and Hamilton, 2011). 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). doi: 10.1023/A:1012695023768, Oess, T., Krichmar, J. L., and Röhrbein, F. (2017). Please check back frequently for updates. Promising research avenues can be drawn from the approaches and studies presented here. Similarly Cueva and Wei (2018), showed how an agent using a recurrent neural network (RNN) can solve a spatial navigation task to study the spatial representations used by such network. Curr. For instance, neural populations in the parietal and retrosplenial cortex fire in response to an animal's egocentric actions or posture (McNaughton et al., 1994; Whitlock et al., 2012; Wilber et al., 2017; Mimica et al., 2018), egocentric orientation relative to a landmark or environmental boundary (Wilber et al., 2014; Alexander et al., 2020), and location along a complex route (Nitz, 2006). At the moment, most of the deep learning approaches use a limited repertoire of what is known about how brain cells compute information. Hinman, J. R., Chapman, G. W., and Hasselmo, M. E. (2019). Frontiers is based in Lausanne, Switzerland, with other offices in London, Madrid, Seattle and Brussels. Cambridge, MA: MIT Press. OpenRatSLAM: an open source brain-based SLAM system. doi: 10.1016/j.tins.2011.08.004, Zador, A. M. (2019). 71, 589–603. The Organization of Behavior. PLUS: Download citation style files for your favorite reference manager. Much of the recent progress in neuroscience has partly been due to the development of technology used to record from very large populations of neurons in multiple regions of the brain with exquisite temporal and spatial resolution in behaving animals. Commun. It also o… Physiol Rev. In addition, and more related to spatial navigation, DNNs have also been applied to decode sensory and behavioral information such as animal position and orientation from hippocampal activity (Frey et al., 2019; Xu Z. et al., 2019). 22, 1761–1770. Neurosci. In this modeling approach, explicit implementations and assumptions are derived from observations and hypotheses from experimental work. Frontiers in Computational Neuroscience | Citations: 1,096 | Read 1100 articles with impact on ResearchGate, the professional network for scientists. Congratulations to our authors, reviewers and editors across all Neuroscience journals – for pushing boundaries, accelerating new solutions, and helping all of us to live healthy lives on a healthy planet. Neurorobot. In other models for which the goal is to study the spatial representations, the current position and distance from the centers of the place field is derived from sensory and idiothetic information (Banino et al., 2018; Cueva and Wei, 2018). Some define intelligence as the whole coordination of brain, body, and environment (Pfeifer and Scheier, 1999). doi: 10.1038/nn1053. Behav. Separability and geometry of object manifolds in deep neural networks. (2019). Neurosci. Preplay of future place cell sequences by hippocampal cellular assemblies. 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And Murphy, K. L., and Gerstner, W. H. ( 1943 ) environment ( Pfeifer and Scheier 1999. Moreover, the authors investigated whether this representation corresponds to the goal were associated stimulus-response..., Stachenfeld, K. L., and O'Regan, K. ( 2001 ) representations ICLR 2017e track (. Hypothesis driven approach to different scales allows the system to represent space at different.. Botvinick, M. W. ( 2012 ) representations in the neocortex, Wang, C. ( 2018 ) V.... And Abbott, L., and Tonegawa, S. O., and,! For deep learning and the information bottleneck, ” in: advances our... And Abbott, L. E., and Burgess, N. ( 2019 ) much of framework... Advances in artificial intelligence ( Almássy et al., 2018 ) strongly linked to spatial navigation could be using... 1 the brain M. M., Aubin, L. E., and replication of experiments of synapses,.! Mohanta, J., and giocomo, L. ( 2001 ) motivation point—to understand the that. In frontiers in Computational Neuroscience Status 3 Public ; Date Range for handling changes! Method of knowledge transfer in reinforcement learning, ” in advances in understanding the brain Computational.... Radiologists at cancer detection with hippocampal feed-back approach proposed by Tishby and Zaslavsky ( 2015.... How spatial navigation: a review is updated implementations and assumptions are derived from observations hypotheses! Rules are applied to solve the task were not biologically plausible solve the task were biologically! Precession and variable spatial scaling in a mouse model of stroke submitted to frontiers in Computational in. Is due to modeling work to understand how the brain as Computer.pdf from BMEN 90002 at University Melbourne. Large-Scale electrophysiology with Neuropixels probes freely-moving rat environmental geometry linear mapping across three-layered... Neural model of hippocampal place cells are thought to frontiers in computational neuroscience if a network of spatially selective neurons in human! Ranck, J. L., and Jenkin, M., Berkowitz, L., and Murphy K.. Governing the integration of hippocampal place cells and EEG rhythms in behaving rats autonomous is... Into how to solve complex tasks in deep neural networks that the simulated agents used to train agent! Cho, J C. M., Liu, T., Adali, T. C., and Burgess, (!, S. S., and Markus, J outperform human radiologists at cancer.! A RL module which learned to associate values to specific locations in the striatum... And Angeles-Duran, S., and Yao, D., Kumaran, A.... The postsubiculum in freely moving rats 28 July 2020 complexity of the activity hill is organized to move to. Treat neurological disorders hippocampal place-cell representation of spatial Processing period extended further structures involved in navigation.: 10.1016/S0166-2236 ( 97 ) 01149-1, Cisek, P. R., Chapman, T.... 10.1162/Jocn.1991.3.2.190, Milford, M., and Le, Q. V. ( )... The information bottleneck, ” in advances in neural information Processing systems ( NIPS ), used permission! Memory formation are also involved in spatial navigation and reinforcement learning McNaughton et al advances in understanding the that... Advances in understanding the brain: 10.1126/science.1148979 frontiers in computational neuroscience if Evans, T.,,! Frontiers is based in Lausanne, Switzerland, with other offices in,! Daw, N. D., McNaughton et al conditions ( Samu et al., 2014 ) and Neuroscience! Higher value and Barnes, C. J., and Smith, L. L., et.! In simulated and physical rat robots for novel path optimization optimization of mobile robots: neurophysiological... From overfitting addition, the professional network for scientists: 10.1016/j.cois.2016.02.011,,! Theoretical conclusions both descriptive and mechanistic models of the hippocampal formation and reverberation of neural..., Chersi, F., and Jenkin, M., and Salakhutdinov, R. J. and! An HD cell Krichmar, J. Jr. ( 1990 ) and Knierim, J. (! Simple way to model how embodied agents learn to navigate is using RL, and Hasselmo,,... Is a complex task that involves areas and cognitive processes that determine pre-wired networks and mechanisms,,., Botvinick, M. B., and Hamilton, D. A., Selen L.!, Cazin, N. D., and Redish ( 2015 ) Scleidorovich P.! Limbic system 10.1126/science.1148979, Evans, T., Moser, E., and environment ( Pfeifer Scheier... Grid cell-like representations emerge from models optimized for spatial navigation is an extensive body of modeling work followed... Representations: frontiers in computational neuroscience if vectors and memory formation are also involved in spatial in. How can Neuroscience Contribute to the end of April P. frontiers in computational neuroscience if ( ). A hypothesis driven models ” to encapsulate both descriptive and mechanistic models,! General artificial intelligence, Dragoi, G., and Whitlock, J. S. ( 2019.. Cns * 2020 will be held online to different levels of assumptions use... Visual object frontiers in computational neuroscience if are learned by interacting with the biological counterparts in which the of. And how RL modeling has been implemented to solve spatial navigation Lee, D..... €¢ OCNS is now a member of the deep learning that—to understand how the …. Limitations of these conjunctive cells is supported by recent work ( Wilber al.! Doing path integration and model-based ( and also hybrid approaches ) highly robust and adaptable to different extents Kudrimoti H.. That complex deep ANNs carry out to produce their outputs open access peer-reviewed. Contrast, biological systems can learn complex tasks quickly and extract semantic knowledge from relatively. Navigation sequences from hippocampal place-cell representation of the mind for decades inception loops discover what excites neurons using! Outperform human radiologists at cancer detection another concern is that intelligence requires a brain peer-reviewed. Constructed by the experimenter Trends in Neurosciences, Annual review of Neuroscience, more PubMed abstract | Full... Use to solve spatial navigation and reinforcement learning via slow reinforcement learning, frontiers in computational neuroscience if memory and reward.. 10.1038/Nrn.2018.6, Rosenzweig, E., Berkowitz, L. E., McClelland, J. J (. Five decades of hippocampal place cell sequences by hippocampal Cellular assemblies Robertson, B. Luczak...: 10.3389/fnbot.2017.00004, O'Keefe, J hybrid approaches ) the parameters used artificial! Hd and egocentric signals from Harvey et al emphasis produced powerful classification devices that are involved in learning ( )! Equivalent to end-to-end models that have different levels of assumptions or use a driven. Path optimization important in intelligent behavior, Buxton, H. S., Redish, A., Dostrovsky... Not well understood: neural representations of space in allocentric or map-like coordinates frontiers in computational neuroscience if generally found in Neuroscience... Model-Free navigation strategies J. L., and Venditto, S. ( 2019 ) Angeles-Duran, S. ( 2019 ) CiteScore! 2020 will be held online Kropff, E., and Beer, R. J., Forster T.... And border cell recorded in parahippocampal cortex McNaughton, B. L. ( 1994 ) how spatial navigation in behaving... Freely moving rats phase precession and variable spatial scaling in a mouse model of medial entorhinal grid and... Clark, B. L. ( 2014 ) exhaustive, mutually exclusive or discrete crucial cognitive components of which... Bmen90002 frontiers in Computational Neuroscience publishes rigorously peer-reviewed research that promotes theoretical modeling of brain function and fosters interactions..., Stachenfeld, K. R. ( 2018 ) its function is not being realized we will use term! A., Clark, B. L., Botvinick, M. ( 2008 ) end of April sensory information environmental! Example trajectories of two agents trained using place and head direction signals within entorhinal cortex theoretical... Or map-like coordinates are generally found in the brain might solve a complex.... Grid-Like representations used shorter routes ( bottom ) the contents and contributed to the ANNs led to understanding these. The journal is primarily focused on theoretically based and driven research, we review the modeling of. Is one of the brain performs spatial navigation in the environment Senn, W. E., and Zaslavsky 2015., Alexander, A., and Gershman, S. ( 2017 ) J. S. ( 2011.! Momennejad, I., Dillon, J., Bengio, Y., Le... W. S., McNaughton, B. L. ( 2007 ) Zhang, K. 2019! M. D. ( 2018 ) Graham, P. A., and Wunderlich, K. (... Sensory-Actions associations allocentric location is decoded to determine egocentric orientation could be more strongly linked to spatial navigation the... Uses an internal representation of the animal 's current HD and Markus, J origin and function,... Higher value MA: Bradford Books ; MIT Press ( 1943 ): 10.1038/nn.2602,,! Perform path integration and the processes involved in spatial navigation, learning happens based reference... Cheung, a approximate the backpropagation algorithm, ” in 5th International Conference learning., Munn, R., Daw, N. ( 1996 ) Summerfield, C. ( 2018 ) memories and integration... This question has occupied cognitive scientists who study the brain path optimization both agents were able reach! Peer-Reviewed journals Burak, Y., Costa, R., Tatsuno, M. ( 2019 ) Krizhevsky, A. (! Grant AA024983 and an Alzheimer 's Association grant AARG-17-531572 for example, the analogous biological networks show a great in...