scholarly journals How environmental movement constraints shape the neural code for space

2021 ◽  
Author(s):  
Kate J. Jeffery
Keyword(s):  
Activity Patterns ◽  
Cell Types ◽  
Environmental Movement ◽  
Place Cells ◽  
Neural Code ◽  
Complex Environments ◽  
Representation Of Space ◽  
Functional Consequences ◽  
Self Motion ◽  
Spatial Cell

AbstractStudy of the neural code for space in rodents has many insights to offer for how mammals, including humans, construct a mental representation of space. This code is centered on the hippocampal place cells, which are active in particular places in the environment. Place cells are informed by numerous other spatial cell types including grid cells, which provide a signal for distance and direction and are thought to help anchor the place cell signal. These neurons combine self-motion and environmental information to create and update their map-like representation. Study of their activity patterns in complex environments of varying structure has revealed that this "cognitive map" of space is not a fixed and rigid entity that permeates space, but rather is variably affected by the movement constraints of the environment. These findings are pointing toward a more flexible spatial code in which the map is adapted to the movement possibilities of the space. An as-yet-unanswered question is whether these different forms of representation have functional consequences, as suggested by an enactivist view of spatial cognition.

scholarly journals Unsupervised learning and clustered connectivity enhance reinforcement learning in spiking neural networks

2020 ◽  
Author(s):  
Philipp Weidel ◽  
Renato Duarte ◽  
Abigail Morrison
Keyword(s):  
Reinforcement Learning ◽  
Unsupervised Learning ◽  
Activity Patterns ◽  
Receptive Fields ◽  
Place Cells ◽  
Spiking Neural Networks ◽  
Complex Environments ◽  
Proposed Model ◽  
Clustered Network ◽  
Better Than

ABSTRACTReinforcement learning is a learning paradigm that can account for how organisms learn to adapt their behavior in complex environments with sparse rewards. However, implementations in spiking neuronal networks typically rely on input architectures involving place cells or receptive fields. This is problematic, as such approaches either scale badly as the environment grows in size or complexity, or presuppose knowledge on how the environment should be partitioned. Here, we propose a learning architecture that combines unsupervised learning on the input projections with clustered connectivity within the representation layer. This combination allows input features to be mapped to clusters; thus the network self-organizes to produce task-relevant activity patterns that can serve as the basis for reinforcement learning on the output projections. On the basis of the MNIST and Mountain Car tasks, we show that our proposed model performs better than either a comparable unclustered network or a clustered network with static input projections. We conclude that the combination of unsupervised learning and clustered connectivity provides a generic representational substrate suitable for further computation.

scholarly journals Mouse entorhinal cortex encodes a diverse repertoire of self-motion signals

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Caitlin S. Mallory ◽  
Kiah Hardcastle ◽  
Malcolm G. Campbell ◽  
Alexander Attinger ◽  
Isabel I. C. Low ◽  
...  
Keyword(s):  
Entorhinal Cortex ◽  
Medial Temporal Lobe ◽  
Visual Cues ◽  
Neural Circuits ◽  
Sensory Cues ◽  
Medial Entorhinal Cortex ◽  
Representation Of Space ◽  
Self Motion ◽  
Information Streams ◽  
Representations Of Space

AbstractNeural circuits generate representations of the external world from multiple information streams. The navigation system provides an exceptional lens through which we may gain insights about how such computations are implemented. Neural circuits in the medial temporal lobe construct a map-like representation of space that supports navigation. This computation integrates multiple sensory cues, and, in addition, is thought to require cues related to the individual’s movement through the environment. Here, we identify multiple self-motion signals, related to the position and velocity of the head and eyes, encoded by neurons in a key node of the navigation circuitry of mice, the medial entorhinal cortex (MEC). The representation of these signals is highly integrated with other cues in individual neurons. Such information could be used to compute the allocentric location of landmarks from visual cues and to generate internal representations of space.

scholarly journals Biogenesis of NDUFS3-less complex I indicates TMEM126A/OPA7 as an assembly factor of the ND4-module

2020 ◽  
Author(s):  
Luigi D’Angelo ◽  
Elisa Astro ◽  
Monica De Luise ◽  
Ivana Kurelac ◽  
Nikkitha Umesh-Ganesh ◽  
...  
Keyword(s):  
Mitochondrial Disease ◽  
Optic Atrophy ◽  
Complex I ◽  
Disease Gene ◽  
Mitochondrial Respiratory Chain ◽  
Cell Types ◽  
Catalytic Core ◽  
Assembly Factor ◽  
Functional Consequences ◽  
And Function

ABSTRACTComplex I (CI) is the largest enzyme of the mitochondrial respiratory chain and its defects are the main cause of mitochondrial disease. To understand the mechanisms regulating the extremely intricate biogenesis of this fundamental bioenergetic machine, we analyzed the structural and functional consequences of the ablation of NDUFS3, a non-catalytic core subunit. We prove that in diverse mammalian cell types a small amount of functional CI can still be detected in the complete absence of NDUFS3. In addition, we have determined the dynamics of CI disassembly when the amount of NDUFS3 is gradually decreased. The process of degradation of the complex occurs in a hierarchical and modular fashion where the ND4-module remains stable and bound to TMEM126A. We have thus, uncovered the function of TMEM126A, the product of a disease gene causing recessive optic atrophy, as a factor necessary for the correct assembly and function of CI.

scholarly journals Path integration in place cells of developing rats

2018 ◽  
Vol 115 (7) ◽  
pp. E1637-E1646 ◽  
Author(s):  
Tale L. Bjerknes ◽  
Nenitha C. Dagslott ◽  
Edvard I. Moser ◽  
May-Britt Moser
Keyword(s):  
Path Integration ◽  
Grid Cell ◽  
Cell System ◽  
Place Cells ◽  
Postnatal Life ◽  
Motion Information ◽  
Grid Cells ◽  
Adult Rats ◽  
Self Motion ◽  
Made In

Place cells in the hippocampus and grid cells in the medial entorhinal cortex rely on self-motion information and path integration for spatially confined firing. Place cells can be observed in young rats as soon as they leave their nest at around 2.5 wk of postnatal life. In contrast, the regularly spaced firing of grid cells develops only after weaning, during the fourth week. In the present study, we sought to determine whether place cells are able to integrate self-motion information before maturation of the grid-cell system. Place cells were recorded on a 200-cm linear track while preweaning, postweaning, and adult rats ran on successive trials from a start wall to a box at the end of a linear track. The position of the start wall was altered in the middle of the trial sequence. When recordings were made in complete darkness, place cells maintained fields at a fixed distance from the start wall regardless of the age of the animal. When lights were on, place fields were determined primarily by external landmarks, except at the very beginning of the track. This shift was observed in both young and adult animals. The results suggest that preweaning rats are able to calculate distances based on information from self-motion before the grid-cell system has matured to its full extent.

scholarly journals Functional Consequences of Presynaptic Inhibition During Behaviorally Relevant Activity

2006 ◽  
Vol 96 (4) ◽  
pp. 2139-2143 ◽  
Author(s):  
M. Frerking ◽  
P. Ohliger-Frerking
Keyword(s):  
Presynaptic Inhibition ◽  
Activity Patterns ◽  
Pyramidal Cells ◽  
Neuronal Responses ◽  
Pass Filter ◽  
Behavioral Cues ◽  
Functional Consequences ◽  
Ca3 Pyramidal Cells ◽  
Analytical Approaches ◽  
High Pass

Presynaptic inhibition is a widespread mechanism for regulating transmitter release in the CNS. Presynaptic inhibitors act as a high-pass filter, but the functional consequence of this filtering during the synaptic processing of behaviorally relevant activity remains unknown. Here we use analytical approaches to examine the effects of presynaptic inhibition on synaptic output in response to activity patterns from CA3 pyramidal cells during the performance of a complex behavioral task. We calculate that presynaptic inhibition enhances the contrast between background activity and responses to environmental cues and that neuronal responses to location are subject to stronger contrast enhancement than neuronal responses to olfactory information. Our analysis suggests that presynaptic inhibition also enhances the importance of integrative inputs that respond to many behavioral cues during the task at the expense of specific inputs that respond to only a few of these cues.

scholarly journals Alternating sequences of future and past behavior encoded within hippocampal theta oscillations

Science ◽  
2020 ◽  
Vol 370 (6513) ◽  
pp. 247-250 ◽  
Author(s):  
Mengni Wang ◽  
David J. Foster ◽  
Brad E. Pfeiffer
Keyword(s):  
Activity Patterns ◽  
Place Cells ◽  
Theta Oscillations ◽  
Retrospective Evaluation ◽  
Past Behavior ◽  
Hippocampal Ca1 ◽  
Ongoing Behavior ◽  
Theta Phase ◽  
Hippocampal Theta ◽  
And Storage

Neural networks display the ability to transform forward-ordered activity patterns into reverse-ordered, retrospective sequences. The mechanisms underlying this transformation remain unknown. We discovered that, during active navigation, rat hippocampal CA1 place cell ensembles are inherently organized to produce independent forward- and reverse-ordered sequences within individual theta oscillations. This finding may provide a circuit-level basis for retrospective evaluation and storage during ongoing behavior. Theta phase procession arose in a minority of place cells, many of which displayed two preferred firing phases in theta oscillations and preferentially participated in reverse replay during subsequent rest. These findings reveal an unexpected aspect of theta-based hippocampal encoding and provide a biological mechanism to support the expression of reverse-ordered sequences.

scholarly journals A No-Go Theorem for One-Layer Feedforward Networks

2014 ◽  
Vol 26 (11) ◽  
pp. 2527-2540 ◽  
Author(s):  
Chad Giusti ◽  
Vladimir Itskov
Keyword(s):  
Activity Patterns ◽  
Maximal Activity ◽  
Neural Code ◽  
Surprising Result ◽  
Response Patterns ◽  
Neural Codes ◽  
Combinatorial Topology ◽  
Feedforward Networks ◽  
Combinatorial Codes ◽  
Recurrent Connections

It is often hypothesized that a crucial role for recurrent connections in the brain is to constrain the set of possible response patterns, thereby shaping the neural code. This implies the existence of neural codes that cannot arise solely from feedforward processing. We set out to find such codes in the context of one-layer feedforward networks and identified a large class of combinatorial codes that indeed cannot be shaped by the feedforward architecture alone. However, these codes are difficult to distinguish from codes that share the same sets of maximal activity patterns in the presence of subtractive noise. When we coarsened the notion of combinatorial neural code to keep track of only maximal patterns, we found the surprising result that all such codes can in fact be realized by one-layer feedforward networks. This suggests that recurrent or many-layer feedforward architectures are not necessary for shaping the (coarse) combinatorial features of neural codes. In particular, it is not possible to infer a computational role for recurrent connections from the combinatorics of neural response patterns alone. Our proofs use mathematical tools from classical combinatorial topology, such as the nerve lemma and the existence of an inverse nerve. An unexpected corollary of our main result is that any prescribed (finite) homotopy type can be realized by a subset of the form [Formula: see text], where [Formula: see text] is a polyhedron.

scholarly journals Functional connectivity of the entorhinal–hippocampal space circuit

2014 ◽  
Vol 369 (1635) ◽  
pp. 20120516 ◽  
Author(s):  
Sheng-Jia Zhang ◽  
Jing Ye ◽  
Jonathan J. Couey ◽  
Menno Witter ◽  
Edvard I. Moser ◽  
...  
Keyword(s):  
Entorhinal Cortex ◽  
Cell Types ◽  
Place Cells ◽  
Projection Neurons ◽  
Border Cells ◽  
Head Direction ◽  
Grid Cells ◽  
Head Direction Cells ◽  
Dynamic Interactions ◽  
Cell Groups

The mammalian space circuit is known to contain several functionally specialized cell types, such as place cells in the hippocampus and grid cells, head-direction cells and border cells in the medial entorhinal cortex (MEC). The interaction between the entorhinal and hippocampal spatial representations is poorly understood, however. We have developed an optogenetic strategy to identify functionally defined cell types in the MEC that project directly to the hippocampus. By expressing channelrhodopsin-2 (ChR2) selectively in the hippocampus-projecting subset of entorhinal projection neurons, we were able to use light-evoked discharge as an instrument to determine whether specific entorhinal cell groups—such as grid cells, border cells and head-direction cells—have direct hippocampal projections. Photoinduced firing was observed at fixed minimal latencies in all functional cell categories, with grid cells as the most abundant hippocampus-projecting spatial cell type. We discuss how photoexcitation experiments can be used to distinguish the subset of hippocampus-projecting entorhinal neurons from neurons that are activated indirectly through the network. The functional breadth of entorhinal input implied by this analysis opens up the potential for rich dynamic interactions between place cells in the hippocampus and different functional cell types in the entorhinal cortex (EC).

scholarly journals Spike interval coding of translatory optic flow and depth from motion in the fly visual system

2016 ◽  
Author(s):  
Kit D. Longden ◽  
Martina Wicklein ◽  
Benjamin J. Hardcastle ◽  
Stephen J. Huston ◽  
Holger G. Krapp
Keyword(s):  
Optic Flow ◽  
Visual Processing ◽  
Visual Motion ◽  
Receptive Fields ◽  
Cell Types ◽  
Spike Burst ◽  
Cluttered Environments ◽  
Single Spike ◽  
Self Motion ◽  
Spike Bursts

SummaryMany animals use the visual motion generated by travelling in a line, the translatory optic flow, to successfully navigate obstacles: near objects appear larger and to move more quickly than distant ones. Flies are experts at navigating cluttered environments, and while their visual processing of rotatory optic flow is understood in exquisite detail, how they process translatory optic flow remains a mystery. Here, we present novel cell types that have motion receptive fields matched to translation self-motion, the vertical translation (VT) cells. One of these, the VT1 cell, encodes forwards sideslip self-motion, and fires action potentials in clusters of spikes, spike bursts. We show that the spike burst coding is size and speed-tuned, and is selectively modulated by parallax motion, the relative motion experienced during translation. These properties are spatially organized, so that the cell is most excited by clutter rather than isolated objects. When the fly is presented with a simulation of flying past an elevated object, the spike burst activity is modulated by the height of the object, and the single spike rate is unaffected. When the moving object alone is experienced, the cell is weakly driven. Meanwhile, the VT2-3 cells have motion receptive fields matched to the lift axis. In conjunction with previously described horizontal cells, the VT cells have the properties required for the fly to successfully navigate clutter and encode its movements along near cardinal axes of thrust, lift and forward sideslip.

scholarly journals Cell segmentation-free inference of cell types from in situ transcriptomics data

2019 ◽  
Author(s):  
Jeongbin Park ◽  
Wonyl Choi ◽  
Sebastian Tiesmeyer ◽  
Brian Long ◽  
Lars E. Borm ◽  
...  
Keyword(s):  
Tissue Characterization ◽  
Cell Types ◽  
Cell Segmentation ◽  
Cell Type ◽  
Computational Framework ◽  
Transcriptomics Data ◽  
Novel Method ◽  
2D And 3D ◽  
Spatial Cell

AbstractMultiplexed fluorescence in situ hybridization techniques have enabled cell-type identification, linking transcriptional heterogeneity with spatial heterogeneity of cells. However, inaccurate cell segmentation reduces the efficacy of cell-type identification and tissue characterization. Here, we present a novel method called Spot-based Spatial cell-type Analysis by Multidimensional mRNA density estimation (SSAM), a robust cell segmentation-free computational framework for identifying cell-types and tissue domains in 2D and 3D. SSAM is applicable to a variety of in situ transcriptomics techniques and capable of integrating prior knowledge of cell types. We apply SSAM to three mouse brain tissue images: the somatosensory cortex imaged by osmFISH, the hypothalamic preoptic region by MERFISH, and the visual cortex by multiplexed smFISH. We found that SSAM detects regions occupied by known cell types that were previously missed and discovers new cell types.

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