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1. Neurophysiological coding of space and time in the hippocampus, entorhinal cortex, and retrosplenial cortex

   https://doi.org/10.1177/2398212820972871  

   Alexander, Andrew S.; Robinson, Jennifer C.; Dannenberg, Holger; Kinsky, Nathaniel R.; Levy, Samuel J.; Mau, William; Chapman, G. William; Sullivan, David W.; Hasselmo, Michael E. (January 2020, Brain and Neuroscience Advances)

   null (Ed.)

   Neurophysiological recordings in behaving rodents demonstrate neuronal response properties that may code space and time for episodic memory and goal-directed behaviour. Here, we review recordings from hippocampus, entorhinal cortex, and retrosplenial cortex to address the problem of how neurons encode multiple overlapping spatiotemporal trajectories and disambiguate these for accurate memory-guided behaviour. The solution could involve neurons in the entorhinal cortex and hippocampus that show mixed selectivity, coding both time and location. Some grid cells and place cells that code space also respond selectively as time cells, allowing differentiation of time intervals when a rat runs in the same location during a delay period. Cells in these regions also develop new representations that differentially code the context of prior or future behaviour allowing disambiguation of overlapping trajectories. Spiking activity is also modulated by running speed and head direction, supporting the coding of episodic memory not as a series of snapshots but as a trajectory that can also be distinguished on the basis of speed and direction. Recent data also address the mechanisms by which sensory input could distinguish different spatial locations. Changes in firing rate reflect running speed on long but not short time intervals, and few cells code movement direction, arguing against path integration for coding location. Instead, new evidence for neural coding of environmental boundaries in egocentric coordinates fits with a modelling framework in which egocentric coding of barriers combined with head direction generates distinct allocentric coding of location. The egocentric input can be used both for coding the location of spatiotemporal trajectories and for retrieving specific viewpoints of the environment. Overall, these different patterns of neural activity can be used for encoding and disambiguation of prior episodic spatiotemporal trajectories or for planning of future goal-directed spatiotemporal trajectories. 

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2. The Visual Cortex in Context

   https://doi.org/10.1146/annurev-vision-091517-034407  

   Froudarakis, Emmanouil; Fahey, Paul G.; Reimer, Jacob; Smirnakis, Stelios M.; Tehovnik, Edward J.; Tolias, Andreas S. (September 2019, Annual Review of Vision Science)

   null (Ed.)

   In this article, we review the anatomical inputs and outputs to the mouse primary visual cortex, area V1. Our survey of data from the Allen Institute Mouse Connectivity project indicates that mouse V1 is highly interconnected with both cortical and subcortical brain areas. This pattern of innervation allows for computations that depend on the state of the animal and on behavioral goals, which contrasts with simple feedforward, hierarchical models of visual processing. Thus, to have an accurate description of the function of V1 during mouse behavior, its involvement with the rest of the brain circuitry has to be considered. Finally, it remains an open question whether the primary visual cortex of higher mammals displays the same degree of sensorimotor integration in the early visual system. 

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3. Feedforward attentional selection in sensory cortex

   https://doi.org/10.1038/s41467-023-41745-1  

   Westerberg, Jacob A; Schall, Jeffrey D; Woodman, Geoffrey F; Maier, Alexander (December 2023, Nature Communications)

   Abstract Salient objects grab attention because they stand out from their surroundings. Whether this phenomenon is accomplished by bottom-up sensory processing or requires top-down guidance is debated. We tested these alternative hypotheses by measuring how early and in which cortical layer(s) neural spiking distinguished a target from a distractor. We measured synaptic and spiking activity across cortical columns in mid-level area V4 of male macaque monkeys performing visual search for a color singleton. A neural signature of attentional capture was observed in the earliest response in the input layer 4. The magnitude of this response predicted response time and accuracy. Errant behavior followed errant selection. Because this response preceded top-down influences and arose in the cortical layer not targeted by top-down connections, these findings demonstrate that feedforward activation of sensory cortex can underlie attentional priority. 

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4. Rethinking retrosplenial cortex: Perspectives and predictions

   https://doi.org/10.1016/j.neuron.2022.11.006  

   Alexander, Andrew S.; Place, Ryan; Starrett, Michael J.; Chrastil, Elizabeth R.; Nitz, Douglas A. (January 2023, Neuron)
5. Functional Architecture of the Cerebral Cortex

   Leopold DA, Strick PL (January 2020, Strüngmann Forum Reports)

   Recent research in the neurosciences has revealed a wealth of new information about the structural organization and physiological operation of the cerebral cortex. These details span vast spatial scales and range from the expression, arrangement, and interaction of molecular gene products at the synapse to the organization of computational networks across the whole brain. This chapter highlights recent discoveries that have laid bare important aspects of the brain’s functional architecture. It begins by describing the dynamic and contingent arrangement of subcellular elements in synaptic connections. Amid this complexity, several common neural circuit motifs, identifi ed across multiple species and preparations, shape the electrophysiological signaling in the cortex. It then turns to the topic of network organization, spurred by routine capacity for noninvasive MRI in humans, where interdisciplinary tools are lending new insights into large-scale principles of brain organization. Discussion follows on one of the most important aspects of brain architecture; namely, the plasticity that affords an animal fl exible behavior. In closing, refl ections are put forth on the nature of the brain’s complexity, and how its biological details might be best captured in computational models in the future. 

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