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). (2006). Giocomo, L. M., Moser, M. B., and Moser, E. I. In addition, cells have been identified in both the hippocampus and medial entorhinal cortex that discharge at a specific direction and distance in relation to a cue (Figure 2D). Commun. If we accept that premise, in order to understand intelligence, natural, or artificial, we must study the brain. McNaughton, B. L., Knierim, J. J., and Wilson, M. A. In this work, the authors show that the representations that are exploited by the trained network resemble characteristics of the biological spatial navigation system such as place cells that remap between environments (thought to represent the neural substrate of unique cognitive maps for different locations). Rev. (2011). DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Binge drinkers show similar changes in brain activity as chronic alcoholics. Even more, these computations enabling neural representations of the animal location carried out in the parietal and entorhinal cortices have been proposed as a general mechanism implemented across the neocortex to represent spatial relationships between objects and as a general mechanism for many conceptual “spaces” (Constantinescu et al., 2016; Behrens et al., 2018; Hawkins et al., 2019). In the following sections we first go into detail about why spatial navigation could be used to learn about the brain and advance AI. A new model in Frontiers in Computational Neuroscience finds differences in basal ganglia connection strengths between healthy and Parkinson's disease brains. Understanding how mind emerges from matter is one of the great remaining questions in science. 38, 1601–1607. Specialty Chief Editors Misha Tsodyks at the Weizmann Institute of Science and Si Wu at the Beijing Normal University are supported by an outstanding Editorial Board of international experts. McNaughton, B. L., Mizumori, S. J. Y., Barnes, C. A., Leonard, B. J., Marquis, M., and Green, E. J. J. Neurosci. doi: 10.1038/s41467-020-14578-5, Colgin, L. L. (2019). Opin. doi: 10.1523/JNEUROSCI.0511-14.2014, Wilber, A. doi: 10.1016/j.neuron.2019.08.034. (B) Grid cells in the medial entorhinal cortex (MEC) at different scales (top) and place cells in the hippocampus (HPC) with different scales (bottom). Neurosci. Modeling work has followed two approaches to study the organization of grid cells (Figure 3B). Driverless: Intelligent Cars and the Road Ahead. Vestibular and attractor network basis of the head direction cell signal in subcortical circuits. In a more recent model (Bush et al., 2015) modeled grid cells to support vector navigation and provide provide a framework for testing experimental predictions. 110, 91–103. PLoS Biol. Neurosci. Neuron 100, 490–509. Biobehav. doi: 10.1038/nn.3968. Vector-based navigation using grid-like representations in artificial agents. (B) Schematic illustrating the general pattern of anatomical connectivity and the functional shift in frames of reference encoded by the brain regions that comprise the neural circuitry of spatial navigation. For neural networks specifically, we lack a unifying mathematical framework to unambiguously describe the emergent behavior of the network in terms of its underlying structure (Bassett and Sporns, 2017). This emphasis produced powerful classification devices that are poor at generalizing and at extracting semantic knowledge. A comparison of neural decoding methods and population coding across thalamo-cortical head direction cells. 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. This replay mechanism has been proposed to reduce the number of “iterations” required to explore an environment in RL (Cazé et al., 2018; Momennejad et al., 2018; Cazin et al., 2019). Neuron 49, 747–756. Behav. arXiv [Preprint]. Cortex 4, 27–39. Pozzi, I., Bohté, S., and Roelfsema, P. (2019). Science 85, 85–90. 6:79. doi: 10.3389/fnbeh.2012.00079, Knierim, J. J., and Hamilton, D. A. (2019). In parallel, neuroscience has also experienced significant advances in understanding the brain. Some define intelligence as the whole coordination of brain, body, and environment (Pfeifer and Scheier, 1999). doi: 10.1017/S0140525X19001997, Brunec, I. K., and Momennejad, I. At a more straightforward level, machine learning provides an analysis tool to make sense of brain activity and behavior in animal models in neuroscience. One is the complexity, because by employing such models it is difficult to analyze them and to fully explain their behavior, particularly when using end-to-end approaches. 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). Euston, D. R., Tatsuno, M., and McNaughton, B. L. (2007). Hippocampus-dependent emergence of spatial sequence coding in retrosplenial cortex. Cambridge, UK: Cambridge University Press. doi: 10.1038/s41593-019-0520-2, Roelfsema, P. R., and Holtmaat, A. (B,C) Example grid cell and border cell recorded in parahippocampal cortex. Comput. An important related element in the mammalian spatial navigation system is learning. doi: 10.1209/0295-5075/124/50001, Hafting, T., Fyhn, M., Molden, S., Moser, M. B., and Moser, E. I. Neurosci. Using this strategy, these agents solved the goal directed navigation tasks using shortcuts when possible, compared to agents only using place and head direction cells to navigate (Figure 3E). Lipson, H., and Kurman, M. (2016). Neural Netw. Here we have applied the Random Forest (RF) method to detect differences in the pharmacological MRI (phMRI) response in rats to treatment … 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). Behav. Percep. Sci. Another perspective is that intelligence can exist without actuators and even without an environment. (2012). doi: 10.1038/s41593-018-0189-y, Cazé, R., Khamassi, M., Aubin, L., and Girard, B. Swanson, L. W. (2003). J. Neurosci. Annu. Neurosci. Nat. Additional Titles: Front Comput Neurosci Published: Lausanne, Switzerland : Frontiers Research Foundation, 2007-Additional Creators: Frontiers Research Foundation Access Online: serialssolutions.com. Science 352, 1464–1468. 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. 6:eaaz2322. Frontiers in Computational Neuroscience welcomes submissions of the following article types: Brief Research Report, Correction, Data Report, Editorial, General Commentary, Hypothesis and Theory, Methods, Mini Review, Opinion, Original Research, Perspective, Review, Specialty Grand Challenge, Systematic Review and Technology and Code. Nat. For example Cohen et al. (2003). 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. You can google some of the scandal surrounding their practices if you want more info on the …

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