scholarly journals Uncovering temporal structure in hippocampal output patterns

2018 ◽  
Author(s):  
Kourosh Maboudi ◽  
Etienne Ackermann ◽  
Brad Pfeiffer ◽  
David Foster ◽  
Kamran Diba ◽  
...  
Keyword(s):  
Spatial Memory ◽  
Animal Behavior ◽  
Markov Models ◽  
Hidden Markov ◽  
Temporal Structure ◽  
Cell Activity ◽  
Pyramidal Cells ◽  
Place Cell ◽  
Burst Event ◽  
Population Burst

AbstractThe place cell activity of hippocampal pyramidal cells has been described as the cognitive map substrate of spatial memory. Replay is observed during hippocampal sharp-wave ripple-associated population burst events and is critical for consolidation and recall-guided behaviors. To present, population burst event (PBE) activity has been analyzed as a phenomenon subordinate to the place code. Here, we use hidden Markov models to study PBEs observed during exploration of both linear mazes and open fields. We demonstrate that estimated models are consistent with temporal replay sequences and that the latent states correspond to a spatial map of the environment. Moreover, we demonstrate the identification of hippocampal replay without recourse to the place code, using only PBE model congruence. These results suggest that downstream regions may rely on PBEs to form a substrate for memory. Additionally, by forming models independent of animal behavior, we lay the groundwork for studies of non-spatial memory.

scholarly journals Uncovering temporal structure in hippocampal output patterns

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Kourosh Maboudi ◽  
Etienne Ackermann ◽  
Laurel Watkins de Jong ◽  
Brad E Pfeiffer ◽  
David Foster ◽  
...  
Keyword(s):  
Spatial Memory ◽  
Hidden Markov Models ◽  
Animal Behavior ◽  
Markov Models ◽  
Hidden Markov ◽  
Temporal Structure ◽  
Cell Activity ◽  
Pyramidal Cells ◽  
Place Cell ◽  
Population Burst

Place cell activity of hippocampal pyramidal cells has been described as the cognitive substrate of spatial memory. Replay is observed during hippocampal sharp-wave-ripple-associated population burst events (PBEs) and is critical for consolidation and recall-guided behaviors. PBE activity has historically been analyzed as a phenomenon subordinate to the place code. Here, we use hidden Markov models to study PBEs observed in rats during exploration of both linear mazes and open fields. We demonstrate that estimated models are consistent with a spatial map of the environment, and can even decode animals’ positions during behavior. Moreover, we demonstrate the model can be used to identify hippocampal replay without recourse to the place code, using only PBE model congruence. These results suggest that downstream regions may rely on PBEs to provide a substrate for memory. Additionally, by forming models independent of animal behavior, we lay the groundwork for studies of non-spatial memory.

scholarly journals Representation of Objects in Space by Two Classes of Hippocampal Pyramidal Cells

2004 ◽  
Vol 124 (1) ◽  
pp. 9-25 ◽  
Author(s):  
Bruno Rivard ◽  
Yu Li ◽  
Pierre-Pascal Lenck-Santini ◽  
Bruno Poucet ◽  
Robert U. Muller
Keyword(s):  
Cell Activity ◽  
Pyramidal Cells ◽  
Place Cell ◽  
Place Cells ◽  
Rat Hippocampus ◽  
Ca1 Pyramidal Cells ◽  
New Class ◽  
Familiar Environment ◽  
Current Arrangement ◽  
Number Of Cells

Humans can recognize and navigate in a room when its contents have been rearranged. Rats also adapt rapidly to movements of objects in a familiar environment. We therefore set out to investigate the neural machinery that underlies this capacity by further investigating the place cell–based map of the surroundings found in the rat hippocampus. We recorded from single CA1 pyramidal cells as rats foraged for food in a cylindrical arena (the room) containing a tall barrier (the furniture). Our main finding is a new class of cells that signal proximity to the barrier. If the barrier is fixed in position, these cells appear to be ordinary place cells. When, however, the barrier is moved, their activity moves equally and thereby conveys information about the barrier's position relative to the arena. When the barrier is removed, such cells stop firing, further suggesting they represent the barrier. Finally, if the barrier is put into a different arena where place cell activity is changed beyond recognition (“remapping”), these cells continue to discharge at the barrier. We also saw, in addition to barrier cells and place cells, a small number of cells whose activity seemed to require the barrier to be in a specific place in the environment. We conclude that barrier cells represent the location of the barrier in an environment-specific, place cell framework. The combined place + barrier cell activity thus mimics the current arrangement of the environment in an unexpectedly realistic fashion.

Spatial memory: Place cell activity is causally related to behavior

2021 ◽  
Vol 31 (7) ◽  
pp. R335-R337
Author(s):  
Mark H. Plitt ◽  
Lisa M. Giocomo
Keyword(s):  
Spatial Memory ◽  
Cell Activity ◽  
Place Cell

scholarly journals Theta-modulation drives the emergence of network-wide connectivity patterns underlying replay in a model of hippocampal place cells

2017 ◽  
Author(s):  
Panagiota Theodoni ◽  
Bernat Rovira ◽  
Yingxue Wang ◽  
Alex Roxin
Keyword(s):  
Cell Activity ◽  
Pyramidal Cells ◽  
Spatial Location ◽  
Medial Septum ◽  
Place Cell ◽  
Place Cells ◽  
Synaptic Connectivity ◽  
Pairwise Correlation ◽  
Ca1 Pyramidal Cells ◽  
Spatial Environment

SummaryPlace cells of the rodent hippocampus fire action potentials when the animal traverses a particular spatial location in a given environment. Therefore, for any given trajectory one will observe a repeatable sequence of place cell activations as the animal explores. Interestingly, when the animal is quiescent or sleeping, one can observe similar sequences of activation, although at a highly compressed rate, known as “replays”. It is hypothesized that this replay underlies the process of memory consolidation whereby memories are “transferred” from hippocampus to cortex. However, it remains unclear how the memory of a particular environment is actually encoded in the place cell activity and what the mechanism for replay is. Here we study how plasticity during spatial exploration shapes the patterns of synaptic connectivity in model networks of place cells. Specifically, we show how plasticity leads to the emergence of patterns of activity which represent the spatial environment learned. These states become spontaneously active when the animal is quiescent, reproducing the phenomenology of replays. Interestingly, replay emerges most rapidly when place cell activity is modulated by an ongoing oscillation. The optimal oscillation frequency can be calculated analytically, is directly related to the plasticity rule, and for experimentally determined values of the plasticity window in rodent slices gives values in the theta range. A major prediction of this model is that the pairwise correlation of place cells which encode for neighboring locations should increase during initial exploration, leading up to the critical transition. We find such an increase in a population of simultaneously recorded CA1 pyramidal cells from a rat exploring a novel track. Furthermore, in a rat in which hippocampal theta is reduced through inactivation of the medial septum we find no such increase. Our model is the first to show how theta-modulation can speed up learning by facilitating the emergence of environment-specific network-wide patterns of synaptic connectivity in hippocampal circuits.

Estimating Personality Impression from Speech Record Using Hidden Markov Models

2015 ◽  
Vol 135 (12) ◽  
pp. 1517-1523 ◽  
Author(s):  
Yicheng Jin ◽  
Takuto Sakuma ◽  
Shohei Kato ◽  
Tsutomu Kunitachi
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov

Hidden Markov Processes

2014 ◽  
Author(s):  
M. Vidyasagar
Keyword(s):  
Hidden Markov Models ◽  
Markov Processes ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Local Alignment ◽  
Biological Applications ◽  
Standard Material ◽  
Hidden Markov Processes ◽  
Genomics And Proteomics

This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. It starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron–Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum–Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. It also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored.

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