200 research outputs found
ν΄λ§ νμ μμ CA1κ³Ό CA3μ μ₯λ©΄ μκ·Ήμ κΈ°λ°ν μ₯μ νμ νμ± μ°κ΅¬
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Όλ¬Έ (λ°μ¬) -- μμΈλνκ΅ λνμ : μμ°κ³Όνλν λμΈμ§κ³Όνκ³Ό, 2021. 2. μ΄μΈμ.When we recall the past experiences, we usually think of a scene which is a combination of what we saw, the sounds we hear, and the feeling we felt at that moment. Since the scene is an essential component of episodic memory, studying how scene stimuli are represented and stored in the brain is important in understanding the processes of formation, storage, and retrieval of our memories. One of the brain regions important for episodic memory is the hippocampus. It has been reported that patients or animals with damage to the hippocampus have trouble with retrieving past experiences or forming new memories. The hippocampus is involved not only in episodic memory but also in the formation of a cognitive map. In particular, the place cells observed in the rodent hippocampus play a key role in these functions. However, research on place cells has mainly focused on the firing patterns of cells during foraging in a space, and it has not been clear how hippocampal cells represent and make use of visual scenes for behavior.
To find how scene stimuli are represented in place cells, I measured spiking activities of single neurons in the CA1, one of the subregions of hippocampus, and the subiculum, a major output of the hippocampus. Neuronal spiking activity was monitored when the rat performed a task of selecting right or left associated to the scene stimulus presented on monitors. As a result, I found that the place cells in the CA1 and subiculum showed rate modulation according to the scene stimulus. In addition, I also conducted an experiment using a virtual reality system to investigate the neural mechanisms of the formation of a place field based on visual scenes. In this experiment, the rat ran on a virtual linear track as visual cues were added one by one to make a scene-like environment. Neuronal activities of place cells were recorded in the CA1 and CA3 simultaneously to study the neural mechanisms of the development of a place field on the basis of external visual stimuli. Place fields appeared in the CA1 even with a single visual cue, whereas in the CA3, place fields only emerged when a sufficient number of visual cues were collectively arranged in a scene-like fashion. The results suggest that that scene is one of the key stimulus that effectively recruits the hippocampus.μ°λ¦¬λ κ³Όκ±°μ κ²½νμ λ μ¬λ¦΄ λ κ·Έ λλ₯Ό λ¬μ¬νλ λ¬Έμ₯μ λ μ¬λ¦¬λ κ²μ΄ μλλΌ κ²½ν ν μκ°μ 보μλ κ², λ€λ Έλ μ리, λκΌλ κ°μ λ±μ΄ 볡ν©μ μΌλ‘ μ΄μ°λ¬μ§ μ₯λ©΄μ λ μ¬λ¦¬κ² λλ€. μ΄λ κ² μ₯λ©΄μ μΌν κΈ°μ΅μ ꡬμ±νλ μ€μν μμλΌ ν μ μκΈ°μ μ₯λ©΄ μκ·Ήμ΄ λμμ μ΄λ»κ² νμλλ©° μ μ₯λλμ§λ₯Ό μ°κ΅¬νλ κ²μ μ°λ¦¬ κΈ°μ΅μ νμ±κ³Ό μ μ₯, μ¬μΈ κ³Όμ μ μ΄ν΄νλλ° μμ΄ λ§€μ° μ€μνλ€κ³ λ³Ό μ μλ€. λμμ μΌν κΈ°μ΅μ λ΄λΉνλ€κ³ μλ €μ§ μμμ ν΄λ§λ‘μ¨, ν΄λ§μ μμμ μ
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μ΄ λ
Όλ¬Έμμ λλ μ₯λ©΄ μκ·Ήμ΄ ν΄λ§μ μ₯μ μΈν¬μμ μ΄λ»κ² νμλλμ§λ₯Ό μμλ³΄κ³ μ μ₯κ° λͺ¨λν°μ μ μ λ μ₯λ©΄ μκ·Ήμ λ³΄κ³ μ€λ₯Έμͺ½μ΄λ μΌμͺ½μ μ νν΄μΌ νλ κ³Όμ λ₯Ό μν ν λ ν΄λ§μ νμ μμμΈ CA1κ³Ό ν΄λ§μ μ 보λ₯Ό μ λ¬ λ°μ λμ λ€λ₯Έ μμμΌλ‘ μ 보λ₯Ό μ λ¬νλ ν΄λ§μ΄νλΆμ λ¨μΌ μΈν¬ νλμ μΈ‘μ νμλ€. κ·Έ κ²°κ³Ό CA1κ³Ό ν΄λ§μ΄νλΆμμ κ΄μ°° λ μ₯μ μΈν¬λ€μ΄ μ₯λ©΄ μκ·Ήμ λ°λ₯Έ λ°νμ¨ λ³νλ₯Ό 보μΈλ€λ κ²μ νμΈ ν μ μμλ€. μ΄μ λνμ¬ λλ ν΄λ§μ μ₯μ μΈν¬λ€μ΄ μ₯μμ₯μ νμ±νκΈ° μν΄μ νμν μκ° μκ·Ήμ΄ λ¬΄μμ΄λ©°, μ΄μ μ₯λ©΄ μκ·Ήμ΄ μ΄λ€ μν μ νλμ§ νμΈνκΈ° μν΄ κ°μ νκ²½μ μ΄μ©ν μ€νμ μννμλ€. μ΄ μ€νμμλ μ₯κ° μ ν νΈλμ λ¬λ¦΄ λ, λΉ κ³΅κ°μμ μμνμ¬ μ₯λ©΄ μκ·Ήμ νμ± ν λκΉμ§ μκ° μκ·Ήμ νλμ© μΆκ°νλ©΄μ ν΄λ§μ νμ μμμΈ CA1κ³Ό CA3μ μ₯μ μΈν¬ νλμ μΈ‘μ νλ κ³Όμ μ ν΅ν΄ μ΄λ€ μκ° μκ·Ήμ΄ μ₯μ μΈν¬μ μ₯μμ₯ νμ±μ κ°μ₯ ν° μν₯μ λ―ΈμΉλ κ²μΈμ§ μμ보μλ€. κ·Έ κ²°κ³Ό CA1μ μ₯μ μΈν¬λ κ°λ¨ν μκ° μκ·Ήμ μΆκ°μλ μ₯μμ₯μ μ νμ±νλ λͺ¨μ΅μ λ³΄μΈ λ°λ©΄ CA3μ μ₯μ μΈν¬λ€μ μΆ©λΆν μκ° μκ·Ήμ΄ λͺ¨μ¬μ μ₯λ©΄ μκ·Ήμ νμ± ν κ²½μ°μ μ₯μμ₯μ νμ±νλ κ²μ΄ κ΄μ°°λμλ€. μ΄λ¬ν μΌλ ¨μ μ€νμ ν΅νμ¬ λλ μ₯λ©΄ μκ·Ήμ΄ ν΄λ§μ μ₯μ μΈν¬ λ°νλ₯Ό ν΅ν΄ νμλλ©°, ν΄λ§μ νμ μμμ΄ λͺ¨λ μ₯λ©΄ μκ·Ή μ²λ¦¬μ κ΄μ¬νμ§λ§ κ·Έ μ€μμλ νΉν CA3κ° μ₯λ©΄ μκ·Ήμ μ²λ¦¬ ν λμ ννμ¬ ν° νμ±μ 보μΈλ€λ κ²μ λ°νλ€.Abstract i
Table of Contents iii
List of Figures iv
Background 1
Scene processing in the hippocampus 2
Anatomical connections of CA1 and CA3 4
Properties of place cell activity 7
Chapter 1. Visual scene representation of CA1 and subiculum in the visual scene memory task 10
Introduction 11
Materials and methods 14
Results 31
Discussion 60
Chapter 2. Role of the visual scene stimulus for place field formation in CA1 and CA3 65
Introduction 66
Materials and methods 68
Results 80
Discussion 107
General Discussion 118
Bibliography 124
κ΅λ¬Έμ΄λ‘ 140Docto
Two photon interrogation of hippocampal subregions CA1 and CA3 during spatial behaviour
The hippocampus is crucial for spatial navigation and episodic memory formation. Hippocampal place cells exhibit spatially selective activity within an environment and form the neural basis of a cognitive map of space which supports these mnemonic functions. Hebbβs (1949) postulate regarding the creation of cell assemblies is seen as the pre-eminent model of learning in neural systems. Investigating changes to the hippocampal representation of space during an animalβs exploration of its environment provides an opportunity to observe Hebbian learning at the population and single cell level. When exploring new environments animals form spatial memories that are updated with experience and retrieved upon re-exposure to the same environment, but how this is achieved by different subnetworks in hippocampal CA1 and CA3, and how these circuits encode distinct memories of similar objects and events remains unclear. To test these ideas, we developed an experimental strategy and detailed protocols for simultaneously recording from CA1 and CA3 populations with 2P imaging. We also developed a novel all-optical protocol to simultaneously activate and record from ensembles of CA3 neurons. We used these approaches to show that targeted activation of CA3 neurons results in an increasing excitatory amplification seen only in CA3 cells when stimulating other CA3 cells, and not in CA1, perhaps reflecting the greater number of recurrent connections in CA3. To probe hippocampal spatial representations, we titrated input to the network by morphing VR environments during spatial navigation to assess the local CA3 as well as downstream CA1 responses. To this end, we found CA1 and CA3 neural population responses behave nonlinearly, consistent with attractor dynamics associated with the two stored representations. We interpret our findings as supporting classic theories of Hebbian learning and as the beginning of uncovering the relationship between hippocampal neural circuit activity and the computations implemented by their dynamics. Establishing this relationship is paramount to demystifying the neural underpinnings of cognition
Memory capacity in the hippocampus
Neural assemblies in hippocampus encode positions. During rest, the hippocam-
pus replays sequences of neural activity seen during awake behavior. This replay
is linked to memory consolidation and mental exploration of the environment. Re-
current networks can be used to model the replay of sequential activity. Multiple
sequences can be stored in the synaptic connections. To achieve a high mem-
ory capacity, recurrent networks require a pattern separation mechanism. Such a
mechanism is global remapping, observed in place cell populations. A place cell
fires at a particular position of an environment and is silent elsewhere. Multiple
place cells usually cover an environment with their firing fields. Small changes in
the environment or context of a behavioral task can cause global remapping, i.e.
profound changes in place cell firing fields. Global remapping causes some cells to
cease firing, other silent cells to gain a place field, and other place cells to move
their firing field and change their peak firing rate. The effect is strong enough to
make global remapping a viable pattern separation mechanism.
We model two mechanisms that improve the memory capacity of recurrent net-
works. The effect of inhibition on replay in a recurrent network is modeled using
binary neurons and binary synapses. A mean field approximation is used to de-
termine the optimal parameters for the inhibitory neuron population. Numerical
simulations of the full model were carried out to verify the predictions of the mean
field model. A second model analyzes a hypothesized global remapping mecha-
nism, in which grid cell firing is used as feed forward input to place cells. Grid
cells have multiple firing fields in the same environment, arranged in a hexagonal
grid. Grid cells can be used in a model as feed forward inputs to place cells to produce place fields. In these grid-to-place cell models, shifts in the grid cell firing
patterns cause remapping in the place cell population. We analyze the capacity of
such a system to create sets of separated patterns, i.e. how many different spatial
codes can be generated. The limiting factor are the synapses connecting grid cells
to place cells. To assess their capacity, we produce different place codes in place
and grid cell populations, by shuffling place field positions and shifting grid fields
of grid cells. Then we use Hebbian learning to increase the synaptic weights be-
tween grid and place cells for each set of grid and place code. The capacity limit
is reached when synaptic interference makes it impossible to produce a place code
with sufficient spatial acuity from grid cell firing. Additionally, it is desired to
also maintain the place fields compact, or sparse if seen from a coding standpoint.
Of course, as more environments are stored, the sparseness is lost. Interestingly,
place cells lose the sparseness of their firing fields much earlier than their spatial
acuity.
For the sequence replay model we are able to increase capacity in a simulated
recurrent network by including an inhibitory population. We show that even
in this more complicated case, capacity is improved. We observe oscillations in
the average activity of both excitatory and inhibitory neuron populations. The
oscillations get stronger at the capacity limit. In addition, at the capacity limit,
rather than observing a sudden failure of replay, we find sequences are replayed
transiently for a couple of time steps before failing. Analyzing the remapping
model, we find that, as we store more spatial codes in the synapses, first the
sparseness of place fields is lost. Only later do we observe a decay in spatial
acuity of the code. We found two ways to maintain sparse place fields while
achieving a high capacity: inhibition between place cells, and partitioning the
place cell population so that learning affects only a small fraction of them in
each environment. We present scaling predictions that suggest that hundreds of
thousands of spatial codes can be produced by this pattern separation mechanism.
The effect inhibition has on the replay model is two-fold. Capacity is increased, and
the graceful transition from full replay to failure allows for higher capacities when
using short sequences. Additional mechanisms not explored in this model could
be at work to concatenate these short sequences, or could perform more complex operations on them. The interplay of excitatory and inhibitory populations gives
rise to oscillations, which are strongest at the capacity limit. The oscillation
draws a picture of how a memory mechanism can cause hippocampal oscillations
as observed in experiments. In the remapping model we showed that sparseness of
place cell firing is constraining the capacity of this pattern separation mechanism.
Grid codes outperform place codes regarding spatial acuity, as shown in Mathis et
al. (2012). Our model shows that the grid-to-place transformation is not harnessing
the full spatial information from the grid code in order to maintain sparse place
fields. This suggests that the two codes are independent, and communication
between the areas might be mostly for synchronization. High spatial acuity seems
to be a specialization of the grid code, while the place code is more suitable for
memory tasks.
In a detailed model of hippocampal replay we show that feedback inhibition can
increase the number of sequences that can be replayed. The effect of inhibition
on capacity is determined using a meanfield model, and the results are verified
with numerical simulations of the full network. Transient replay is found at the
capacity limit, accompanied by oscillations that resemble sharp wave ripples in
hippocampus. In a second model
Hippocampal replay of neuronal activity is linked to memory consolidation and
mental exploration. Furthermore, replay is a potential neural correlate of episodic
memory. To model hippocampal sequence replay, recurrent neural networks are
used. Memory capacity of such networks is of great interest to determine their
biological feasibility. And additionally, any mechanism that improves capacity has
explanatory power. We investigate two such mechanisms.
The first mechanism to improve capacity is global, unspecific feedback inhibition
for the recurrent network. In a simplified meanfield model we show that capacity
is indeed improved.
The second mechanism that increases memory capacity is pattern separation. In
the spatial context of hippocampal place cell firing, global remapping is one way
to achieve pattern separation. Changes in the environment or context of a task
cause global remapping. During global remapping, place cell firing changes in unpredictable ways: cells shift their place fields, or fully cease firing, and formerly
silent cells acquire place fields. Global remapping can be triggered by subtle
changes in grid cells that give feed-forward inputs to hippocampal place cells.
We investigate the capacity of the underlying synaptic connections, defined as the
number of different environments that can be represented at a given spatial acuity.
We find two essential conditions to achieve a high capacity and sparse place fields:
inhibition between place cells, and partitioning the place cell population so that
learning affects only a small fraction of them in each environments. We also find
that sparsity of place fields is the constraining factor of the model rather than
spatial acuity. Since the hippocampal place code is sparse, we conclude that the
hippocampus does not fully harness the spatial information available in the grid
code. The two codes of space might thus serve different purposes
ν΄λ§ νμ μμ CA1κ³Ό CA3μ μκ° μκ·Ή λ³νμ λ°λ₯Έ μ₯μ νμ ν¨ν΄ μ°κ΅¬
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Όλ¬Έ(λ°μ¬) -- μμΈλνκ΅λνμ : μμ°κ³Όνλν λμΈμ§κ³Όνκ³Ό, 2023. 2. μ΄μΈμ.μ°λ¦¬κ° μΌμμμ κ²½ννλ μ¬κ±΄λ€μ νλμ μ€ν λ¦¬λ‘ κ΅¬μ±λμ΄ μΌν κΈ°μ΅μΌλ‘ νμ±λλ€. ν΄λ§λ κ³Όκ±°μ κ²½νν μΌ λ€ λΏλ§ μλλΌ νμ¬ κ²½ννκ³ μλ μ¬κ±΄λ€μ λν μΌν κΈ°μ΅μ μ²λ¦¬ν λ νμμ μΈ λ μμμ΄λΌκ³ μλ €μ Έ μλ€. μ€μΉλ₯μ ν΄λ§μμ κ΄μ°°λλ μ₯μ μΈν¬λ ν΄λ§κ° λλ¬Όμ΄ μΈμ§νκ³ μλ 곡κ°μ λν μ§λλ₯Ό νμ±νλ ν΅μ¬μ μΈ μν μ νλ κ²μΌλ‘ μλ €μ Έ μλ€. νΉν νΉμ ν 곡κ°μμλ§ μ λ³μ μΌλ‘ λ°ννλ μ₯μ μΈν¬λ νκ²½μ λ³νκ° μ£Όμ΄μ‘μ λ remappingμ΄λΌλ νμμΌλ‘ νκ²½μ λ³νλ₯Ό λ°μνλ€κ³ μλ €μ Έ μλ€. νκ²½μ λ³νκ° μμ λ, μ₯μ μΈν¬κ° λμΌν μμΉμμ νλνλ©° λ°ν λΉλλ₯Ό μ‘°μ νκ±°λ μ ν λ€λ₯Έ μ₯μμμ νλνλ ν¨ν΄μΌλ‘ κ΄μ°°λλ€. μ΄λ¬ν μ₯μ μΈν¬μ λ³νλ i) κΈ°μ‘΄μ κΈ°μ΅μ μ‘°κΈ λ³ννκ±°λ, ii) μλ‘μ΄ κΈ°μ΅μ νμ±νλ μΌν κΈ°μ΅μ ννλ₯Ό κ°μ§κ³ μλ€. νμ§λ§ μ₯μ μΈν¬κ° λΆκ·μΉμ μΈ ν¨ν΄μΌλ‘ 곡κ°μ λ³νλ₯Ό νμν¨μ λ°λΌ μ΄λ€μ νλμ΄ κ°λ μλ―Έλ λΆλΆλͺ
νκ² λ¨μμλ€. λν μ₯μ μΈν¬κ° 볡ν©μ μΈ κ°κ° μ 보λ€μ λ°μνλ€λ νΉμ§μ, μ΄λ€μ΄ μ΄λ€ μΈμ§μ μλ―Έλ₯Ό κ°μ§λ©° νλμ νλ κ²μΈμ§μ λν λμ λ₯Ό λ¨κ²Όλ€. λ³ΈμΈμ ν΄λ§μ μ₯μ μΈν¬κ° μΌν κΈ°μ΅μ μ΄λ€ κΈ°μ¬λ₯Ό ν κ²μΈμ§, νΉν λ³νλ νκ²½μμ 무μμ μλ‘ κΈ°μ΅νκ³ κΈ°μ‘΄μ μκ³ μλ μ 보λ μ΄λ»κ² μ²λ¦¬ν κ²μΈμ§ μ°μ°νλ κ³Όμ μ ν΄λ§μ νμ μμμΈ CA1κ³Ό CA3μμ κ°κ° μ΄λ»κ² νμνλμ§ μμλ³΄κ³ μ νμλ€. μ΄μ λν λ΅μ μ°ΎκΈ° μν΄ λ³ΈμΈμ λλ¬Όμ΄ μνΈμμ©νλ©° κ²½νν μ μλ κ°μ νμ€ (VR) μμ€ν
μ μ μνμ¬ κ°μ νκ²½μ μκ° μκ·Ήμ μ λμ μΌλ‘ μ‘°μνμλ€. μ΄ κ³Όμ μμ λ³ΈμΈμ λλ¬Όμ΄ κ²½ννλ μκ° μκ·Ήμ λ³νμ (i.e., input) ν΄λ§ μ₯μμΈν¬μ μ κΈ°μ νλ (i.e., output) κ°μ κ΄κ³λ₯Ό μ‘°μ¬νμλ€. 첫 λ²μ§Έ μ§λ¬ΈμΌλ‘λ λ³ΈμΈμ΄ ꡬμΆν κ°μ νμ€ μμ€ν
μμ μ₯μ μΈν¬κ° λ°νλλμ§λ₯Ό νμΈνμλ€. κ·Έ κ²°κ³Όλ‘ κΈ°μ‘΄ λ¬Ένμμ λ³΄κ³ λμλ κ²°κ³Όμ λΉμ·ν μμ€μ μ₯μ μΈν¬λ€μ κ²μ¦ν μ μμλ€. λ³ΈμΈμ΄ ꡬμΆν κ°μ νμ€ μμ€ν
μμ μ₯μ μΈν¬κ° κ΄μ°°λλ€λ κ²μ νμΈν μ΄νμλ, κΈ°μ‘΄ νκ²½μ μ λμ μΈ μκ°μ λ³νλ₯Ό μ£Όμ΄ μ₯μ μΈν¬κ° ν΄λΉ λ³νλ₯Ό μ΄λ»κ² λ°μνλμ§ μ§λ¬Ένμλ€. κ·Έ κ²°κ³Όλ‘, ν΄λ§μ νμ μμ CA1μμ κΈ°μ‘΄ νκ²½μ λν νμμ μ μ§νλ μ§λ¨κ³Ό, νΉμ νκ²½μ λ³νκ° κ°ν΄μ§ μ¬κ±΄μ μν΄ μλ‘μ΄ νμμ μ μ§νλ μ§λ¨μ΄ λμλ€λ°μ μΌλ‘ λλλ€λ νμμ κ΄μ°°νμλ€. λ°λ©΄, ν΄λ§ νμ μμμΈ CA3μμλ νκ²½μ λ³νκ° μ΄λ£¨μ΄μ‘μμλ λΆκ΅¬νκ³ λλΆλΆμ μ₯μ μΈν¬λ€μ΄ κΈ°μ‘΄ νκ²½μ λν νμμ μ μ§νμλ€. μ΄λ¬ν κ²°κ³Όλ₯Ό ν λλ‘ ν΄λ§ νμ μμμΈ CA3μ κΈ°μ‘΄μ μκ³ μλ νκ²½μ λν κΈ°μ΅μ μμ μ μΌλ‘ μ μ§νλ μν μ μννλ λ°λ©΄, ν΄λ§ νμ μμμΈ CA1μ λ³ννλ νκ²½ λ΄μμλ μ΄μ μ κΈ°μ΅κ³Ό μλ‘μ΄ κΈ°μ΅μ λ
립μ μΌλ‘ ꡬλΆνμ¬ μλ‘μ΄ μ 보λ₯Ό μ μ°νκ² νμ΅νλλ‘ νλ κ°λ₯μ±μ μ μνκ³ μ νλ€.Any events or experiences in the given space and time are stitched together as an episode. The hippocampus has been widely acknowledged for its role in episodic memory for decades. At the same time, the rodent hippocampus exhibits the salient feature where its principal neurons are active in a spatially selective pattern (i.e., place cell). The place cells change their firing patterns as there are changes in the environments. Until now, we have been interpreting these firing changes, also known as "remapping," to have a functional significance in episodic memory by i) slightly modifying the old map to retrieve subtle changes from the previous memory or ii) forming the new map to reflect any major changes. In the real world, place cells receive complex sensory information from multiple sources, including multimodal sensory inputs and idiothetic information, making it even more challenging to interpret place cell activity from the intermingled sensory inputs fed into the hippocampal system. Taking advantage of the virtual reality (VR) system, I investigated how the hippocampal subregions CA1 and CA3 networks reflect environmental change. Thereby, I parametrically manipulated the environment by adding visual noise (i.e., virtual fog) in the VR environment and examined how hippocampal place cells in the CA1 and CA3 responded as visual noises were added to the environment in a quantified manner. Prior studies have suggested that CA3 forms a discrete map of the modified environments, presumably by performing either pattern separation or pattern completion. However, place cells in CA1 exhibit less coherent responses to environmental changes compared to CA3. This discrepancy between the CA1 and CA3 subregions is puzzling because CA3 output must pass through the CA1 area before reaching cortical areas. Furthermore, the functional roles of the CA1 in processing the environmental changes still need to be investigated due to the heterogeneous neural outputs with mixed yet conflicting findings. I first questioned whether our VR system reliably induced the place cells from both hippocampal subregions CA1 and CA3. As a result, I observed that the firing properties of hippocampal place cells are equivalent to that reported in the previous studies. Once I confirmed that visual environments in our VR system dominantly controlled the place cells, I examined how place cells in the CA1 and CA3 subregions responded to various levels of changes made to the visual environment. As visual noise was introduced to the familiar environment, I found that place cells in CA1 split simultaneously into two subpopulations: In one, place cells with old maps while changing their firing rate to reflect noise levels (i.e., rate remapping); in another, place cells with new maps to differentiate the dynamically changing environment from an old stable environment (i.e., global remapping). The place cells in CA3 mainly sustained the old map and reflected noise levels by rate remapping. Suppose one considers the rate remapping class of place cells as pattern-completing cells and the global remapping class as pattern-separating cells. In that case, the CA1 can manifest both pattern separation and pattern completion classes of neurons at the environmental change. My dissertation suggests that CA1 can simultaneously form an orthogonal map of the same environment to remember new episodes without interfering with the old memory.Background 1
Anatomical structures of the Hippocampal system and their proposed roles 2
The remapping properties of Hippocampal place cell 7
The usage of the virtual reality (VR) system for rodents in studying the hippocampus 16
Chapter 1. Visual scene stimulus exerts dominant control over the place fields 19
Introduction 20
Materials and methods 22
Results 37
Discussion 53
Chapter 2. The functional role of the CA1 and CA3 in processing the visually modified environment 56
Introduction 57
Materials and methods 59
Results 63
Discussion 94
General Discussion 98
Bibliography 111
κ΅λ¬Έμ΄λ‘ 137λ°
The spatial representations acquired in CA3 by self-organizing recurrent connections.
Neural computation models have hypothesized that the dentate gyrus drives the storage in the CA3 network of new memories including, e.g. in rodents, spatial memories. Can recurrent CA3 connections self-organize, during storage, and form what have been called continuous attractors, or charts - so that they express spatial information later, when aside from a partial cue the information may not be available in the inputs? We use a simplified mathematical network model to contrast the properties of spatial representations self-organized through simulated Hebbian plasticity with those of charts pre-wired in the synaptic matrix, a control case closer to the ideal notion of continuous attractors. Both models form granular quasi-attractors, characterized by drift, which approach continuous ones only in the limit of an infinitely large network. The two models are comparable in terms of precision, but not of accuracy: with self-organized connections, the metric of space remains distorted, ill-adequate for accurate path integration, even when scaled up to the real hippocampus. While prolonged selforganization makes charts somewhat more informative about position in the environment, some positional information is surprisingly present also about environments never learned, borrowed, as it were, from unrelated charts. In contrast, context discrimination decreases with more learning, as different charts tend to collapse onto each other. These observations challenge the feasibility of the idealized CA3 continuous chart concept, and are consistent with a CA3 specialization for episodic memory rather than path integration. \ua9 2013 Cifelli, Palma, Roseti, Verlengia and Simonato
Linking Anatomical and Physiological Properties of Hippocampal Pyramidal Neurons
The hippocampus is of interest to a broad range of neuroscientists, who examine its structure, function, and dysfunction in various pathologies and disorders. Great effort has been put into classifying hippocampal neurons according to their morphological, molecular, and functional characteristics; however, the question of whether and how in vivo neural activity relates to principal cell heterogeneity during natural behaviour has remained unresolved. This study aimed at resolving structure-function relationships in the mouse dorsal hippocampus by investigating three dimensions of principal cell heterogeneity. First, we juxtacellularly recorded and labelled CA2/CA3 pyramidal neurons in freely-moving mice, thus linking quantitative features of dendritic architecture and anatomical position to in vivo activities. We found that a higher proportion of distal dendritic length correlated with higher burst propensity, indicating that entorhinal inputs may determine the burst-firing properties of CA2/CA3 pyramidal neurons. Second, we investigated CA1 principal cell diversity within the deep-superficial axis. We combined the juxtacellular recording technique with optogenetics in freely-moving animals. With restricted Channelrhodopsin (ChR2) expression in Calbindin-positive (Calb1+) neurons, we achieved online readout of cell identity via photostimulation, thus improving the sampling efficacy of superficial layer neurons. We found that Calb1+ CA1 pyramidal cells had weaker spatial modulation and contained less spatial information than Calbindin-negative (Calb1-) neurons, pointing to cell identity as a critical determinant for recruitment into the hippocampal spatial map. Lastly, we explored the anatomical determinants for the recruitment of pyramidal neurons by hippocampal sharp-wave ripple events. The axon initial segment location determined two distinct pyramidal cell types. Neurons with axons initiating from dendrites were more recruited into sharp-wave ripples, indicating that excitatory inputs onto axon-carrying dendrites can escape perisomatic inhibition during hippocampal ripples. Collectively, these results indicate that the recruitment of pyramidal neurons into hippocampal neural ensembles critically depends on cell identity and single cell morphological features
Place Cell Networks in Pre-weanling Rats Show Associative Memory Properties from the Onset of Exploratory Behavior
Place cells are hippocampal pyramidal cells that are active when an animal visits a restricted area of the environment, and collectively their activity constitutes a neural representation of space. Place cell populations in the adult rat hippocampus display fundamental properties consistent with an associative memory network: the ability to 1) generate new and distinct spatial firing patterns when encountering novel spatial contexts or changes in sensory input ("remapping") and 2) reinstate previously stored firing patterns when encountering a familiar context, including on the basis of an incomplete/degraded set of sensory cues ("pattern completion"). To date, it is unknown when these spatial memory responses emerge during brain development. Here, we show that, from the age of first exploration (postnatal day 16) onwards, place cell populations already exhibit these key features: they generate new representations upon exposure to a novel context and can reactivate familiar representations on the basis of an incomplete set of sensory cues. These results demonstrate that, as early as exploratory behaviors emerge, and despite the absence of an adult-like grid cell network, the developing hippocampus processes incoming sensory information as an associative memory network
Spatial Representations in the Entorhino-Hippocampal Circuit
After a general introduction and a brief review of the available experimental data on spatial representations (chapter 2), this thesis is divided into two main parts. The first part, comprising the chapters from 3 to 6, is dedicated to grid cells. In chapter 3 we present and discuss the various models proposed for explaining grid cells formation. In chapter 4 and 5 we study our model of grid cells generation based on adaptation in the case of non-planar environments, namely in the case of a spherical environment and of three-dimensional space. In chapter 6 we propose a variant of the model where the alignment of the grid axes is induced through reciprocal inhibition, and we suggest that that the inhibitory connections obtained during this learning process can be used to implement a continuous attractor in mEC.
The second part, comprising chapters from 7 to 10 is instead focused on place cell representations. In chapter 7 we analyze the differences between place cells and grid cells in terms on information content, in chapter 8 we describe the properties of attractor dynamics in our model of the Ca3 net- work, and in the following chapter we study the effects of theta oscillations on network dynamics. Finally, in Chapter 10 we analyze to what extent the learning of a new representation, can preserve the topology and the exact metric of physical space
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