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Abstract Interacting with objects in our environment requires determining their locations, often with respect to surrounding objects (i.e., allocentrically). According to the scene grammar framework, these usually small, local objects are movable within a scene and represent the lowest level of a scene’s hierarchy. How do higher hierarchical levels of scene grammar influence allocentric coding for memory-guided actions? Here, we focused on the effect of large, immovable objects (anchors) on the encoding of local object positions. In a virtual reality study, participants (n = 30) viewed one of four possible scenes (two kitchens or two bathrooms), with two anchors connected by a shelf, onto which were presented three local objects (congruent with one anchor) (Encoding). The scene was re-presented (Test) with 1) local objects missing and 2) one of the anchors shifted (Shift) or not (No shift). Participants, then, saw a floating local object (target), which they grabbed and placed back on the shelf in its remembered position (Response). Eye-tracking data revealed that both local objects and anchors were fixated, with preference for local objects. Additionally, anchors guided allocentric coding of local objects, despite being task-irrelevant. Overall, anchors implicitly influence spatial coding of local object locations for memory-guided actions within naturalistic (virtual) environments.

Interacting with objects in our environment requires determining their locations, often with respect to surrounding objects (i.e., allocentrically). According to the scene grammar framework, these usually small, local objects are movable within a scene and represent the lowest level of a scene's hierarchy. How do higher hierarchical levels of scene grammar influence allocentric coding for memory-guided actions? Here, we focused on the effect of large, immovable objects (anchors) on the encoding of local object positions. In a virtual reality study, participants (n = 30) viewed one of four possible scenes (two kitchens or two bathrooms), with two anchors connected by a shelf, onto which were presented three local objects (congruent with one anchor) (Encoding). The scene was re-presented (Test) with 1) local objects missing and 2) one of the anchors shifted (Shift) or not (No shift). Participants, then, saw a floating local object (target), which they grabbed and placed back on the shelf in its remembered position (Response). Eye-tracking data revealed that both local objects and anchors were fixated, with preference for local objects. Additionally, anchors guided allocentric coding of local objects, despite being task-irrelevant. Overall, anchors implicitly influence spatial coding of local object locations for memory-guided actions within naturalistic (virtual) environments. [ABSTRACT FROM AUTHOR]

Introduction: Recent work on bats flying over long distances has revealed that single hippocampal cells have multiple place fields of different sizes. At the network level, a multi-scale, multi-field place cell code outperforms classical single-scale, single-field place codes, yet the performance boundaries of such a code remain an open question. In particular, it is unknown how general multi- field codes compare to a highly regular grid code, in which cells form distinct modules with different scales. Methods: In this work, we address the coding properties of theoretical spatial coding models with rigorous analyses of comprehensive simulations. Starting fromamulti-scale,multi-field network, we performed evolutionary optimization. The resulting multi-field networks sometimes retained the multi-scale property at the single-cell level but most often converged to a single scale, with all place fields in a given cell having the same size. We compared the results against a single-scale single-field code and a one-dimensional grid code, focusing on two main characteristics: the performance of the code itself and the dynamics of the network generating it. Results: Our simulation experiments revealed that, under normal conditions, a regular grid code outperforms all other codes with respect to decoding accuracy, achieving a given precision with fewer neurons and fields. In contrast, multi- field codes are more robust against noise and lesions, such as random drop- out of neurons, given that the significantly higher number of fields provides redundancy. Contrary to our expectations, the network dynamics of all models, from the original multi-scale models before optimization to the multi-field models that resulted from optimization, did not maintain activity bumps at their original locations when a position-specific external input was removed. Discussion: Optimizedmulti-field codes appear to strike a compromise between a place code and a grid code that reflects a trade-off between accurate positional encoding and robustness. Surprisingly, the recurrent neural network models we implemented and optimized for either multi- or single-scale, multi-field codes did not intrinsically produce a persistent "memory" of attractor states. These models, therefore, were not continuous attractor networks. [ABSTRACT FROM AUTHOR]

Based on a newly proposed spatial data model Spatial Chromatic Model (SCM), we developed a spatial coding scheme, called the full-coded Ordinary Arranged Chromatic Diagram (full-OACD). As a type of spatial tessellation, full-OACD partitions a geographic space into a number of subspaces, such as cells, edges, and vertices. These subspaces are called spatial particles and are assigned with unique codes chromatic codes. The generation, structure, computation, and properties of full-OACD are introduced. Relations between particulate chromatic codes and spatial topology are investigated. Full-OACD is a kind of new discrete spatial coordinate system where the information of real-world entities is embedded. Full-OACD provides an informative and meaningful spatial coding framework for spatial topological analysis and many other potential applications in geospatial information science. [ABSTRACT FROM AUTHOR]

Introduction: Spatial representations in the entorhinal cortex (EC) and hippocampus (HPC) are fundamental to cognitive functions like navigation and memory. These representations, embodied in spatial field maps, dynamically remap in response to environmental changes. However, current methods, such as Pearson's correlation coefficient, struggle to capture the complexity of these remapping events, especially when fields do not overlap, or transformations are non-linear. This limitation hinders our understanding and quantification of remapping, a key aspect of spatial memory function. Methods: We propose a family of metrics based on the Earth Mover's Distance (EMD) as a versatile framework for characterizing remapping. Results: The EMD provides a granular, noise-resistant, and rate-robust description of remapping. This approach enables the identification of specific cell types and the characterization of remapping in various scenarios, including disease models. Furthermore, the EMD's properties can be manipulated to identify spatially tuned cell types and to explore remapping as it relates to alternate information forms such as spatiotemporal coding. Discussion: We present a feasible, lightweight approach that complements traditional methods. Our findings underscore the potential of the EMD as a powerful tool for enhancing our understanding of remapping in the brain and its implications for spatial navigation, memory studies and beyond. [ABSTRACT FROM AUTHOR]

IntroductionRecent work on bats flying over long distances has revealed that single hippocampal cells have multiple place fields of different sizes. At the network level, a multi-scale, multi-field place cell code outperforms classical single-scale, single-field place codes, yet the performance boundaries of such a code remain an open question. In particular, it is unknown how general multi-field codes compare to a highly regular grid code, in which cells form distinct modules with different scales.MethodsIn this work, we address the coding properties of theoretical spatial coding models with rigorous analyses of comprehensive simulations. Starting from a multi-scale, multi-field network, we performed evolutionary optimization. The resulting multi-field networks sometimes retained the multi-scale property at the single-cell level but most often converged to a single scale, with all place fields in a given cell having the same size. We compared the results against a single-scale single-field code and a one-dimensional grid code, focusing on two main characteristics: the performance of the code itself and the dynamics of the network generating it.ResultsOur simulation experiments revealed that, under normal conditions, a regular grid code outperforms all other codes with respect to decoding accuracy, achieving a given precision with fewer neurons and fields. In contrast, multi-field codes are more robust against noise and lesions, such as random drop-out of neurons, given that the significantly higher number of fields provides redundancy. Contrary to our expectations, the network dynamics of all models, from the original multi-scale models before optimization to the multi-field models that resulted from optimization, did not maintain activity bumps at their original locations when a position-specific external input was removed.DiscussionOptimized multi-field codes appear to strike a compromise between a place code and a grid code that reflects a trade-off between accurate positional encoding and robustness. Surprisingly, the recurrent neural network models we implemented and optimized for either multi- or single-scale, multi-field codes did not intrinsically produce a persistent “memory” of attractor states. These models, therefore, were not continuous attractor networks.

A behavioral strategy crucial to survival is directed navigation to a goal, such as a food or home location. One potential neural substrate for supporting goal-directed navigation is the parahippocampus, which contains neurons that represent an animal’s position, orientation, and movement through the world, and that change their firing activity to encode behaviorally relevant variables such as reward. However, little prior work on the parahippocampus has considered how neurons encode variables during goal-directed navigation in environments that dynamically change. Here, we recorded single units from rat parahippocampal cortex while subjects performed a goal-directed task. The maze dynamically changed goal-locations via a visual cue on a trial-to-trial basis, requiring subjects to use cue-location associations to receive reward. We observed a mismatch-like signal, with elevated neural activity on incorrect trials, leading to rate-remapping. The strength of this remapping correlated with task performance. Recordings during open-field foraging allowed us to functionally define navigational coding for a subset of the neurons recorded in the maze. This approach revealed that head-direction coding units remapped more than other functional-defined units. Taken together, this work thus raises the possibility that during goal-directed navigation, parahippocampal neurons encode error information reflective of an animal’s behavioral performance.

Allocentric and egocentric reference frames are used to code the spatial position of action targets in reference to objects in the environment, i.e., relative to landmarks (allocentric), or the observer (egocentric). Previous research investigated reference frames in isolation, for example, by shifting landmarks relative to the target and asking participants to reach to the remembered target location. Systematic reaching errors were found in the direction of the landmark shift and used as a proxy for allocentric spatial coding. Here, we examined the interaction of both allocentric and egocentric reference frames by shifting the landmarks as well as the observer. We asked participants to encode a three-dimensional configuration of balls and to reproduce this configuration from memory after a short delay followed by a landmark or an observer shift. We also manipulated the number of landmarks to test its effect on the use of allocentric and egocentric reference frames. We found that participants were less accurate when reproducing the configuration of balls after an observer shift, which was reflected in larger configurational errors. In addition, an increase in the number of landmarks led to a stronger reliance on allocentric cues and a weaker contribution of egocentric cues. In sum, our results highlight the important role of egocentric cues for allocentric spatial coding in the context of memoryguided actions. [ABSTRACT FROM AUTHOR]

Based on a newly proposed spatial data model Spatial Chromatic Model (SCM), we developed a spatial coding scheme, called the full-coded Ordinary Arranged Chromatic Diagram (full-OACD). As a type of spatial tessellation, full-OACD partitions a geographic space into a number of subspaces, such as cells, edges, and vertices. These subspaces are called spatial particles and are assigned with unique codes chromatic codes. The generation, structure, computation, and properties of full-OACD are introduced. Relations between particulate chromatic codes and spatial topology are investigated. Full-OACD is a kind of new discrete spatial coordinate system where the information of real-world entities is embedded. Full-OACD provides an informative and meaningful spatial coding framework for spatial topological analysis and many other potential applications in geospatial information science.

The hippocampus is well known as a central site for memory processing-critical for storing and later retrieving the experiences events of daily life so they can be used to shape future behavior. Much of what we know about the physiology underlying hippocampal function comes from spatial navigation studies in rodents, which have allowed great strides in understanding how the hippocampus represents experience at the cellular level. However, it remains a challenge to reconcile our knowledge of spatial encoding in the hippocampus with its demonstrated role in memory-dependent tasks in both humans and other animals. Moreover, our understanding of how networks of neurons coordinate their activity within and across hippocampal subregions to enable the encoding, consolidation, and retrieval of memories is incomplete. In this chapter, we explore how information may be represented at the cellular level and processed via coordinated patterns of activity throughout the subregions of the hippocampal network.

In this paper, we propose a novel transmission scheme, called sparse layered MIMO (SL-MIMO), that combines non-orthogonal transmission and singular value decomposition (SVD) precoding. Non-orthogonality in SL-MIMO allows re-using of the eigen-channels which improves the spectral efficiency and error rate performance of the system through enhancing the coding gain and diversity gain. We also present a low-complexity message-passing (MP) detector for the proposed SL-MIMO system which performs quite close to maximum likelihood (ML). The joint moment generating function (MGF) of the ordered eigenvalues is calculated and used to derive a closed-form upper bound on the average word error probability (AWEP) of the SL-MIMO system, and this derived expression is then used to analyze the diversity gain of the system. We use our analytical results to design sub-optimal codebooks to minimize the error rate of the SL-MIMO system. Simulation results in $4\times 4$ and $6\times 6$ multiple-input multiple-output (MIMO) systems with 4-ary, 16-ary, and 64-ary constellations show that our proposed SL-MIMO scheme outperforms competing approaches such as X- and Y-codes in terms of system error rate performance. SL-MIMO has 5.6 dB advantage compared to X-codes and 4.7 dB advantage compared to Y-codes in $6\times 6$ MIMO system with a 64-ary constellation. [ABSTRACT FROM AUTHOR]

The hippocampus is well known as a central site for memory processing-critical for storing and later retrieving the experiences events of daily life so they can be used to shape future behavior. Much of what we know about the physiology underlying hippocampal function comes from spatial navigation studies in rodents, which have allowed great strides in understanding how the hippocampus represents experience at the cellular level. However, it remains a challenge to reconcile our knowledge of spatial encoding in the hippocampus with its demonstrated role in memory-dependent tasks in both humans and other animals. Moreover, our understanding of how networks of neurons coordinate their activity within and across hippocampal subregions to enable the encoding, consolidation, and retrieval of memories is incomplete. In this chapter, we explore how information may be represented at the cellular level and processed via coordinated patterns of activity throughout the subregions of the hippocampal network.

The medial entorhinal cortex (MEC) creates a map of local space, based on the firing patterns of grid, head-direction (HD), border, and object-vector (OV) cells. How these cell types are organized anatomically is debated. In-depth analysis of this question requires collection of precise anatomical and activity data across large populations of neurons during unrestrained behavior, which neither electrophysiological nor previous imaging methods fully afford. Here, we examined the topographic arrangement of spatially modulated neurons in the superficial layers of MEC and adjacent parasubiculum using miniaturized, portable two-photon microscopes, which allow mice to roam freely in open fields. Grid cells exhibited low levels of co-occurrence with OV cells and clustered anatomically, while border, HD, and OV cells tended to intermingle. These data suggest that grid cell networks might be largely distinct from those of border, HD, and OV cells and that grid cells exhibit strong coupling among themselves but weaker links to other cell types. [ABSTRACT FROM AUTHOR]

The perirhinal cortex is situated on the border between sensory association cortex and the hippocampal formation. It serves an important function as a transition area between the sensory neocortex and the medial temporal lobe. While the perirhinal cortex has traditionally been associated with object coding and the "what" pathway of the temporal lobe, current evidence suggests a broader function of the perirhinal cortex in solving feature ambiguity and processing complex stimuli. Besides fulfilling functions in object coding, recent neurophysiological findings in freely moving rodents indicate that the perirhinal cortex also contributes to spatial and contextual processing beyond individual sensory modalities. Here, we address how these two opposing views on perirhinal cortex—the object‐centered and spatial‐contextual processing hypotheses—may be reconciled. The perirhinal cortex is consistently recruited when different features can be merged perceptually or conceptually into a single entity. Features that are unitized in these entities include object information from multiple sensory domains, reward associations, semantic features and spatial/contextual associations. We propose that the same perirhinal network circuits can be flexibly deployed for multiple cognitive functions, such that the perirhinal cortex performs similar unitization operations on different types of information, depending on behavioral demands and ranging from the object‐related domain to spatial, contextual and semantic information. [ABSTRACT FROM AUTHOR]

Retinal motion can modulate visual sensitivity. For instance, low contrast drifting waveforms (targets) can be easier to detect when abutting the leading edges of movement in adjacent high contrast waveforms (inducers), rather than the trailing edges. This target-inducer interaction is contingent on the adjacent waveforms being consistent with one another - in-phase as opposed to out-of-phase. It has been suggested that this happens because there is a perceptually explicit predictive signal at leading edges of motion that summates with low contrast physical input - a 'predictive summation'. Another possible explanation is a phase sensitive 'spatial summation', a summation of physical inputs spread across the retina (not predictive signals). This should be non-selective in terms of position - it should be evident at leading, adjacent, and at trailing edges of motion. To tease these possibilities apart, we examined target sensitivity at leading, adjacent, and trailing edges of motion. We also examined target sensitivity adjacent to flicker, and for a stimulus that is less susceptible to spatial summation, as it sums to grey across a small retinal expanse. We found evidence for spatial summation in all but the last condition. Finally, we examined sensitivity to an absence of signal at leading and trailing edges of motion, finding greater sensitivity at leading edges. These results are inconsistent with the existence of a perceptually explicit predictive signal in advance of drifting waveforms. Instead, we suggest that phase-contingent target-inducer modulations of sensitivity are explicable in terms of a directionally modulated spatial summation.

When sharing virtual collaborative environments, operators exchange spatial statements that refer to the objects' positions in the virtual space. If operators are to understand each other, they need to develop a common spatial frame of reference and then choose a space coding to describe the objects' positions. In this paper, we consider how the content of a virtual environment can influence communication between users. We designed two studies in which one participant (the speaker) had to indicate the position of one object to another participant (the addressee). The virtual environment was sometimes enriched by additional (proximal and distal) visual cues. In study 1, we considered statements production. We observed that the speakers most often used the avatar of their partner as a spatial reference to indicate a localization in the virtual space (i.e., Addressee-Centered coding) despite it increases their mental workload. Nevertheless, in complex situations, they also used distal cues to speak to the addressees (i.e., Exocentric coding of the space). In study 2, we considered statements comprehension. Addressee-Centered coding and Exocentric coding were used by the speakers in various spatial configurations to indicate the object position. We observed that Exocentric coding is the most difficult to manage for the addressee. These results indicate that speakers implemented the principle of less collaborative effort by adopting a way of exchanging information based on an asymmetrical cognitive cost, taking into consideration each other's difficulties. This allows a balanced mental workload to be maintained between the two operators throughout the task. [ABSTRACT FROM AUTHOR]

Understanding the role of neuronal activity in cognition and behavior is a key question in neuroscience. Previously, in vivo studies have typically inferred behavior from electrophysiological data using probabilistic approaches including Bayesian decoding. While providing useful information on the role of neuronal subcircuits, electrophysiological approaches are often limited in the maximum number of recorded neurons as well as their ability to reliably identify neurons over time. This can be particularly problematic when trying to decode behaviors that rely on large neuronal assemblies or rely on temporal mechanisms, such as a learning task over the course of several days. Calcium imaging of genetically encoded calcium indicators has overcome these two issues. Unfortunately, because calcium transients only indirectly reflect spiking activity and calcium imaging is often performed at lower sampling frequencies, this approach suffers from uncertainty in exact spike timing and thus activity frequency, making rate-based decoding approaches used in electrophysiological recordings difficult to apply to calcium imaging data. Here we describe a probabilistic framework that can be used to robustly infer behavior from calcium imaging recordings and relies on a simplified implementation of a naive Baysian classifier. Our method discriminates between periods of activity and periods of inactivity to compute probability density functions (likelihood and posterior), significance and confidence interval, as well as mutual information. We next devise a simple method to decode behavior using these probability density functions and propose metrics to quantify decoding accuracy. Finally, we show that neuronal activity can be predicted from behavior, and that the accuracy of such reconstructions can guide the understanding of relationships that may exist between behavioral states and neuronal activity.

The hippocampal formation encodes maps of space and a key question in neuroscience is whether its spatial coding principles also provide a universal metric for the organization of nonspatial, conceptual information. Previous work demonstrated directional coding during navigation through a continuous stimulus feature space as well as mapping of distances in a feature space that was relevant for concept learning. Here we provide the first unambiguous evidence for a hippocampal representation of the actual concept space, by showing that the hippocampal distance signal selectively reflects the mapping of specifically conceptually relevant rather than of all feature dimensions. During fMRI scanning of 32 human participants (21 females), we presented everyday objects, which had beforehand been associated with specific values on three continuous feature dimensions. Crucially, only two dimensions were relevant to prior concept learning. We find that hippocampal responses to the objects reflect their relative distances in a space defined along conceptually relevant dimensions compared with distances in a space defined along all feature dimensions. These findings suggest that the hippocampus supports knowledge acquisition by dynamically encoding information in a space spanned along dimensions that are relevant in relation to define concepts. [ABSTRACT FROM AUTHOR]

Although Lorente de No' recognized the anatomical distinction of the hippocampal Cornu Ammonis (CA) 2 region, it had, until recently, been assigned no unique function. Its location between the key players of the circuit, CA3 and CA1, which along with the entorhinal cortex and dentate gyrus compose the classic trisynaptic circuit, further distracted research interest. However, the connectivity of CA2 pyramidal cells, together with unique patterns of gene expression, hints at a much larger contribution to hippocampal information processing than has been ascribed. Here we review recent advances that have identified new roles for CA2 in hippocampal centric processing, together with specialized functions in social memory and, potentially, as a broadcaster of novelty. These new data, together with CA2's role in disease, justify a closer look at how this small region exerts its influence and how it might best be exploited to understand and treat disease-related circuit dysfunctions. [ABSTRACT FROM AUTHOR]

Understanding the role of neuronal activity in cognition and behavior is a key question in neuroscience. Previously, in vivo studies have typically inferred behavior from electrophysiological data using probabilistic approaches including Bayesian decoding. While providing useful information on the role of neuronal subcircuits, electrophysiological approaches are often limited in the maximum number of recorded neurons as well as their ability to reliably identify neurons over time. This can be particularly problematic when trying to decode behaviors that rely on large neuronal assemblies or rely on temporal mechanisms, such as a learning task over the course of several days. Calcium imaging of genetically encoded calcium indicators has overcome these two issues. Unfortunately, because calcium transients only indirectly reflect spiking activity and calcium imaging is often performed at lower sampling frequencies, this approach suffers from uncertainty in exact spike timing and thus activity frequency, making rate-based decoding approaches used in electrophysiological recordings difficult to apply to calcium imaging data. Here we describe a probabilistic framework that can be used to robustly infer behavior from calcium imaging recordings and relies on a simplified implementation of a naive Baysian classifier. Our method discriminates between periods of activity and periods of inactivity to compute probability density functions (likelihood and posterior), significance and confidence interval, as well as mutual information. We next devise a simple method to decode behavior using these probability density functions and propose metrics to quantify decoding accuracy. Finally, we show that neuronal activity can be predicted from behavior, and that the accuracy of such reconstructions can guide the understanding of relationships that may exist between behavioral states and neuronal activity. [ABSTRACT FROM AUTHOR]

High-resolution remote sensing (HRRS) image scene classification takes an important role in many applications and has attracted much attention. Recently, notable efforts have been made to present massive methods for HRRS scene classification, wherein deep-learning-based methods demonstrate remarkable performance compared with state-of-the-art methods. However, HRRS images contain complex contextual relationships and large differences of object scale, which are significantly different from natural images. The existing deep-learning-based scene classification methods are originally designed for natural image processing and have not been optimized to adapt to the characteristics of HRRS images, which significantly affects the efficiency of the feature extraction and recognition accuracy. In addition, when designing a model for remote sensing tasks, the pretraining of the model is time-consuming. The enormous amount of pretraining time and computation resources necessarily increase the difficulty of producing an excellent model. In this letter, focusing on the problems above, we proposed a new convolutional neural network (CNN)-based scene classification method. The CNN-based scene classification method is constructed by spatial-scale-aware blocks and is efficient in extracting the abundant spatial features, but can also adaptively adjust feature responses to maximize the function of informative features in the classification results. In addition, an HRRS imagery-based learning strategy is utilized to obtain an initial model for fine-tuning the model parameters, which drastically reduces the pretraining time. The proposed method has been demonstrated using two HRRS data sets, and experimental results have proven the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

In stimulus-response compatibility tasks, performance is better when the handle of an object is oriented on the same side of the response than when the handle is oriented on the opposite side. Two major alternative accounts, the motor affordance and spatial accounts, have been proposed to explain this handle-hand compatibility effect. In two experiments, we tested between these two accounts by administering a go/no-go task to right-handed participants. Handled objects presented on a touchscreen were used as stimuli. Half of the participants had to reach-to-touch the stimuli by using their dominant hand, the other half by using their nondominant hand. Liftoff times (LTs), movement times (MTs) and spatial coordinates of the movement endpoints were recorded. Results from the LTs and MTs analyses showed no evidence of handle-hand compatibility effects. In contrast, the analyses of the spatial coordinates revealed that participants' touches were shifted more laterally towards the handle when the handles were oriented on the same side of the responding hand (Experiments 1 and 2). Furthermore, the right-hand touches landed higher (towards the handle) than the left-hand touches, especially when the vertical object dimension was particularly salient (Experiment 1). Overall, these results are in line with the activation of hand motor programs to reach and grasp the object as predicted by the motor account, at least for the right/dominant hand. [ABSTRACT FROM AUTHOR]

A behavioral strategy crucial to survival is directed navigation to a goal, such as a food or home location. One potential neural substrate for supporting goal-directed navigation is the parahippocampus, which contains neurons that represent an animal's position, orientation, and movement through the world, and that change their firing activity to encode behaviorally relevant variables such as reward. However, little prior work on the parahippocampus has considered how neurons encode variables during goal-directed navigation in environments that dynamically change. Here, we recorded single units from rat parahippocampal cortex while subjects performed a goal-directed task. The maze dynamically changed goal-locations via a visual cue on a trial-to-trial basis, requiring subjects to use cue-location associations to receive reward. We observed a mismatch-like signal, with elevated neural activity on incorrect trials, leading to rate-remapping. The strength of this remapping correlated with task performance. Recordings during open-field foraging allowed us to functionally define navigational coding for a subset of the neurons recorded in the maze. This approach revealed that head-direction coding units remapped more than other functional-defined units. Taken together, this work thus raises the possibility that during goal-directed navigation, parahippocampal neurons encode error information reflective of an animal's behavioral performance., Competing Interests: AG No competing interests declared, LG Reviewing editor, eLife, (© 2023, Gonzalez and Giocomo.)

Research suggests that responses to pictures of manipulable objects are facilitated when the location of the response is aligned with the side of the object handle. One interpretation of alignment effects is that object identification results in the automatic activation of actions associated with the object. Alignment effects are, however, not ubiquitously found. Yu, Abrams, and Zacks (2014) found an alignment effect when participants were instructed to imagine picking up the pictured objects while making upright-inverted judgments. Six other experiments, which did not use such instructions, found no alignment effect. One interpretation is that motor-imagery instructions draw attention to the graspable parts of an object, which results in the activation of actions associated with the object. This account predicts that alignment effects are restricted to responses with the left and right hand. An alternative interpretation is that motor-imagery instructions result in the formation of abstract spatial codes for left versus right. This spatial coding account predicts that alignment effects are present for other types of responses that involve a left-right dimension. Consistent with the latter account, we found that alignment effects were found even when participants responded with the index and middle finger of the same hand or with their left and right feet. Currently popular accounts of human cognition suggest that the actions associated with objects are automatically activated when one perceives an object. Our study presents evidence against this view. In fact, it suggests that not even when participants are instructed to think how they would pick up an object is such information readily activated. [ABSTRACT FROM AUTHOR]

Stimuli associated with large quantities are typically responded to faster with a right- than a left-side key, whereas stimuli associated with small quantities are typically responded to faster with a left- than a right-side key. This phenomenon is known as the spatial-quantity association of response codes (SQUARC) effect. Here, in two experiments, we explored whether a SQUARC effect can emerge for light versus heavy items. Participants judged whether the weight associated with a central target word, describing an animal (e.g. 'cow'; Experiment 1) or a material (e.g. 'iron'; Experiment 2), was lighter or heavier than the weight associated with a reference word. Responses were provided with a left- and a right-side button. Then, participants estimated the weight associated with target and reference words. In both experiments, evidence for a SQUARC effect emerged. Moreover, response times for each target word decreased with absolute difference between its rated weight and the rated weight of the reference word, in line with a distance effect. Overall, these results provide evidence of a possible spatial representation of weight. [ABSTRACT FROM AUTHOR]

The lateral entorhinal cortex (LEC) is a major cortical input area to the hippocampus, and it is crucial for associative object-place-context memories. An unresolved question is whether these associations are performed exclusively in the hippocampus or also upstream of it. Anatomical evidence suggests that the LEC processes both object and spatial information. We describe here a gradient of spatial selectivity along the antero-posterior axis of the LEC. We demonstrate that the LEC generates distinct spatial maps for different contexts that are independent of object coding and vice versa, thus providing evidence for pure spatial and pure object codes upstream of the hippocampus. While space and object coding occur by and large separately in the LEC, we identified neurons that encode for space and objects conjunctively. Together, these findings point to a scenario in which the LEC sustains both distinct space and object coding and associative space-object coding. [Display omitted] • The LEC comprises spatial neurons that are arranged in an antero-posterior gradient • LEC spatial neurons display remapping to changes in local and distal context • The LEC comprises context-dependent and context-independent object cells The LEC is best known for conveying non-spatial information to the hippocampus. In this study, Huang et al. provide evidence that the LEC comprises a plethora of spatial and object coding neurons, some of which exhibit associative coding, as would be required for the formation of episodic memory. [ABSTRACT FROM AUTHOR]

p35 is an activating co-factor of Cyclin-dependent kinase 5 (Cdk5), a protein whose dysfunction has been implicated in a wide-range of neurological disorders including cognitive impairment and disease. Inducible deletion of the p35 gene in adult mice results in profound deficits in hippocampal-dependent spatial learning and synaptic physiology, however the impact of the loss of p35 function on hippocampal in vivo physiology and spatial coding remains unknown. Here, we recorded CA1 pyramidal cell activity in freely behaving p35 cKO and control mice and found that place cells in the mutant mice have elevated firing rates and impaired spatial coding, accompanied by changes in the temporal organization of spiking both during exploration and rest. These data shed light on the role of p35 in maintaining cellular and network excitability and provide a physiological correlate of the spatial learning deficits in these mice.

The medial entorhinal cortex (MEC) is part of the brain's network for dynamic representation of location. The most abundant class of neurons in this circuit is the grid cell, characterized by its periodic, hexagonally patterned firing fields. While in developing animals some MEC cell types express adult-like firing patterns already on the first exposure to an open spatial environment, only days after eye opening, grid cells mature more slowly, over a 1-to-2-wk period after the animals leave their nest. Whether the later emergence of a periodic grid pattern reflects a need for experience with spatial environments has not been determined. We here show that grid-like firing patterns continue to appear during exploration of open square environments in rats that are raised for the first months of their life in opaque spherical environments, in the absence of stable reference boundaries to guide spatial orientation. While strictly periodic firing fields were initially absent in these animals, clear grid patterns developed after only a few trials of training. In rats that were tested in the same open environment but raised for the first months of life in opaque cubes, with sharp vertical boundaries, grid-like firing was from the beginning indistinguishable from that of nondeprived control animals growing up in large enriched cages. Thus, although a minimum of experience with peripheral geometric boundaries is required for expression of regular grid patterns in a new environment, the effect of restricted spatial experience is overcome with short training, consistent with a preconfigured experience-independent basis for the grid pattern.

p35 is an activating co-factor of Cyclin-dependent kinase 5 (Cdk5), a protein whose dysfunction has been implicated in a wide-range of neurological disorders including cognitive impairment and disease. Inducible deletion of the p35 gene in adult mice results in profound deficits in hippocampal-dependent spatial learning and synaptic physiology, however the impact of the loss of p35 function on hippocampal in vivo physiology and spatial coding remains unknown. Here, we recorded CA1 pyramidal cell activity in freely behaving p35 cKO and control mice and found that place cells in the mutant mice have elevated firing rates and impaired spatial coding, accompanied by changes in the temporal organization of spiking both during exploration and rest. These data shed light on the role of p35 in maintaining cellular and network excitability and provide a physiological correlate of the spatial learning deficits in these mice. [ABSTRACT FROM AUTHOR]

The hippocampal formation is involved in navigation, and its neuronal activity exhibits a variety of spatial correlates (e.g., place cells, grid cells). The quantification of the information encoded by spikes has been standard procedure to identify which cells have spatial correlates. For place cells, most of the established metrics derive from Shannon’s mutual information (Shannon, 1948), and convey information rate in bits/s or bits/spike (Skaggs et al., 1993, 1996). Despite their widespread use, the performance of these metrics in relation to the original mutual information metric has never been investigated. In this work, using simulated and real data, we find that the current information metrics correlate less with the accuracy of spatial decoding than the original mutual information metric. We also find that the top informative cells may differ among metrics, and show a surrogate-based normalization that yields comparable spatial information estimates. Since different information metrics may identify different neuronal populations, we discuss current and alternative definitions of spatially informative cells, which affect the metric choice. [ABSTRACT FROM AUTHOR]

When participants classify pictures of objects as upright or inverted with a left or right keypress, responses are faster if the response location (left/right) corresponds with the location of a handle (left/right) than if it does not. This result has typically been attributed to a grasping affordance (automatic activation of muscles associated with grasping the object with the ipsilateral hand), but several findings have indicated instead that the effect is a spatial correspondence effect, much like the Simon effect for object location. Pappas (2014) reported evidence he interpreted as showing that spatial coding predominates with silhouettes of objects, whereas photographs of objects yield affordance-based effects. We conducted two experiments similar to those of Pappas, using frying pans as stimuli, with our two experiments differing in whether the entire object was centered on the display screen or the base was centered. When the objects were centered, a positive correspondence effect relative to the handle was evident for the silhouettes but a negative correspondence effect for the photographs. When the base was centered, the handle was clearly located to the left or right side of the display, and both silhouettes and photographs produced correspondence effects of similar size relative to the handle location. Despite the main results being counter to the grasping affordance hypothesis, response-time distribution analyses suggest that, instead of activating automatically at fast responses, an effector-specific component of the hypothesized type may come into play for responses that are selected after the handle location has been identified. [ABSTRACT FROM AUTHOR]

Visual working memory is limited in capacity so it is essential to use it efficiently. Previous work has shown that statistical learning can help boost working memory efficiency by prioritizing the encoding and/or maintenance of objects most likely to be tested. In this study, we considered that the potential benefits of statistical learning could be limited by spatial constraints. Across three experiments, we found that statistical learning prioritizes working memory allocation to items based on their likelihood of being tested, but this prioritization is greatly modulated by spatial constraints. In particular, when two locations each had a high probability of being tested, we primarily observed performance benefits over low probable locations when these two locations were horizontally adjacent to one another. Vertically adjacent and diagonally arranged high probable locations produced no accuracy benefit over low probable locations and a modest response time benefit. These findings contrast with previously observed hemifield-independent effects (i.e., a “bilateral field advantage”) and reveal surprising limitations on the potential benefits of statistical learning. [ABSTRACT FROM AUTHOR]

Objective. In studying the spatial coding mechanism of visual evoked potentials, it is significant to construct a model that shows the relationship between steady-state visual evoked potential (SSVEP) responses to the local and global visual field stimulation. In order to investigate whether SSVEPs produced by sub-region stimulation can predict that produced by joint region stimulation, a sub-region combination scheme for spatial coding in a high-frequency SSVEP-based brain-computer interface (BCI) is developed innovatively. Approach. An annular visual field is divided equally into eight sub-regions. The 60 Hz visual stimuli in different sub-regions and joint regions are presented separately to participants. The SSVEP produced by the sub-region stimulation is superimposed to simulate the SSVEP produced by the joint region stimulation with different spatial combinations. A four-class spatially-coded BCI paradigm is used to evaluate the simulated classification performance, and the performance ranking of all simulated SSVEPs is obtained. Six representative stimulus patterns from two performance levels and three stimulus areas are applied to the online BCI system for each participant. Main results. The experimental result shows that the proposed scheme can implement a spatially-coded visual BCI system and realize satisfactory performance with imperceptible flicker. Offline analysis indicates that the classification accuracy and information transfer rate (ITR) are 89.69 ± 8.75% and 24.35 ± 7.09 bits min -1 with 3 s data length under the 3/8 stimulus area. The online BCI system reaches an average classification accuracy of 87.50 ± 9.13% with 3 s data length, resulting in an ITR of 22.48 ± 6.71 bits min -1 under the 3/8 stimulus area. Significance. This study proves the feasibility of using the sub-region's response to predict the joint region's response. It has the potential to extend to other frequency bands and lays a foundation for future research on more complex spatial coding methods., (© 2023 IOP Publishing Ltd.)