scholarly journals Bidirectional synaptic plasticity rapidly modifies hippocampal representations

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Aaron D Milstein ◽  
Yiding Li ◽  
Katie C Bittner ◽  
Christine Grienberger ◽  
Ivan Soltesz ◽  
...  
Keyword(s):  
Synaptic Plasticity ◽  
Synaptic Weight ◽  
Standard Form ◽  
Learning Rule ◽  
Weight Changes ◽  
Place Field ◽  
Plateau Potentials ◽  
Population Activity ◽  
Neural Adaptations ◽  
Place Fields

Learning requires neural adaptations thought to be mediated by activity-dependent synaptic plasticity. A relatively non-standard form of synaptic plasticity driven by dendritic calcium spikes, or plateau potentials, has been reported to underlie place field formation in rodent hippocampal CA1 neurons. Here we found that this behavioral timescale synaptic plasticity (BTSP) can also reshape existing place fields via bidirectional synaptic weight changes that depend on the temporal proximity of plateau potentials to pre-existing place fields. When evoked near an existing place field, plateau potentials induced less synaptic potentiation and more depression, suggesting BTSP might depend inversely on postsynaptic activation. However, manipulations of place cell membrane potential and computational modeling indicated that this anti-correlation actually results from a dependence on current synaptic weight such that weak inputs potentiate and strong inputs depress. A network model implementing this bidirectional synaptic learning rule suggested that BTSP enables population activity, rather than pairwise neuronal correlations, to drive neural adaptations to experience.

scholarly journals Entorhinal cortex directs learning-related changes in CA1 representations

2021 ◽  
Author(s):  
Christine Grienberger ◽  
Jeffrey C Magee
Keyword(s):  
Synaptic Plasticity ◽  
Entorhinal Cortex ◽  
Activity Pattern ◽  
Brain Activity ◽  
Adaptive Behaviors ◽  
Place Field ◽  
Population Activity ◽  
Field Density ◽  
Hippocampal Ca1

Learning-related changes in brain activity are thought to underlie adaptive behaviors. For instance, the learning of a reward site by rodents requires the development of an over-representation of that location in the hippocampus. However, how this learning-related change occurs remains unknown. Here we recorded hippocampal CA1 population activity as mice learned a reward location on a linear treadmill. Physiological and pharmacological evidence suggests that the adaptive over-representation required behavioral timescale synaptic plasticity (BTSP). BTSP is known to be driven by dendritic voltage signals that we hypothesized were initiated by input from entorhinal cortex layer 3 (EC3). Accordingly, the CA1 over-representation was largely removed by optogenetic inhibition of EC3 activity. Recordings from EC3 neurons revealed an activity pattern that could provide an instructive signal directing BTSP to generate the over-representation. Consistent with this function, exposure to a second environment possessing a prominent reward-predictive cue resulted in both EC3 activity and CA1 place field density that were more elevated at the cue than the reward. These data indicate that learning-related changes in the hippocampus are produced by synaptic plasticity directed by an instructive signal from the EC3 that appears to be specifically adapted to the behaviorally relevant features of the environment.

scholarly journals A Biophysical Model of Synaptic Plasticity and Metaplasticity Can Account for the Dynamics of the Backward Shift of Hippocampal Place Fields

2008 ◽  
Vol 100 (2) ◽  
pp. 983-992 ◽  
Author(s):  
Xintian Yu ◽  
Harel Z. Shouval ◽  
James J. Knierim
Keyword(s):  
Synaptic Plasticity ◽  
Cortical Plasticity ◽  
Learning Rule ◽  
Spike Timing ◽  
Biophysical Model ◽  
Place Field ◽  
Dependent Plasticity ◽  
Backward Shift ◽  
Field Shift

Hippocampal place cells in the rat undergo experience-dependent changes when the rat runs stereotyped routes. One such change, the backward shift of the place field center of mass, has been linked by previous modeling efforts to spike-timing–dependent plasticity (STDP). However, these models did not account for the termination of the place field shift and they were based on an abstract implementation of STDP that ignores many of the features found in cortical plasticity. Here, instead of the abstract STDP model, we use a calcium-dependent plasticity (CaDP) learning rule that can account for many of the observed properties of cortical plasticity. We use the CaDP learning rule in combination with a model of metaplasticity to simulate place field dynamics. Without any major changes to the parameters of the original model, the present simulations account both for the initial rapid place field shift and for the subsequent slowing down of this shift. These results suggest that the CaDP model captures the essence of a general cortical mechanism of synaptic plasticity, which may underlie numerous forms of synaptic plasticity observed both in vivo and in vitro.

Inheritance of Hippocampal Place Fields Through Hebbian Learning: Effects of Theta Modulation and Phase Precession on Structure Formation

2015 ◽  
Vol 27 (8) ◽  
pp. 1624-1672 ◽  
Author(s):  
Tiziano D’Albis ◽  
Jorge Jaramillo ◽  
Henning Sprekeler ◽  
Richard Kempter
Keyword(s):  
Hebbian Learning ◽  
Learning Rule ◽  
Place Cells ◽  
Spike Timing ◽  
Upstream Region ◽  
Place Field ◽  
Hebbian Learning Rule ◽  
Phase Precession ◽  
Place Fields ◽  
Field Formation

A place cell is a neuron that fires whenever the animal traverses a particular location of the environment—the place field of the cell. Place cells are found in two regions of the rodent hippocampus: CA3 and CA1. Motivated by the anatomical connectivity between these two regions and by the evidence for synaptic plasticity at these connections, we study how a place field in CA1 can be inherited from an upstream region such as CA3 through a Hebbian learning rule, in particular, through spike-timing-dependent plasticity (STDP). To this end, we model a population of CA3 place cells projecting to a single CA1 cell, and we assume that the CA1 input synapses are plastic according to STDP. With both numerical and analytical methods, we show that in the case of overlapping CA3 input place fields, the STDP learning rule leads to the formation of a place field in CA1. We then investigate the roles of the hippocampal theta modulation and phase precession on the inheritance process. We find that theta modulation favors the inheritance and leads to faster place field formation whereas phase precession changes the drift of CA1 place fields over time.

scholarly journals Calcium Imaging Reveals Fast Tuning Dynamics of Hippocampal Place Cells and CA1 Population Activity during Free Exploration Task in Mice

2022 ◽  
Vol 23 (2) ◽  
pp. 638
Author(s):  
Vladimir P. Sotskov ◽  
Nikita A. Pospelov ◽  
Viktor V. Plusnin ◽  
Konstantin V. Anokhin
Keyword(s):  
Neuronal Activity ◽  
Calcium Imaging ◽  
Viral Vector ◽  
Place Cells ◽  
Place Field ◽  
Population Activity ◽  
Novel Environment ◽  
Mouse Hippocampus ◽  
Place Fields

Hippocampal place cells are a well-known object in neuroscience, but their place field formation in the first moments of navigating in a novel environment remains an ill-defined process. To address these dynamics, we performed in vivo imaging of neuronal activity in the CA1 field of the mouse hippocampus using genetically encoded green calcium indicators, including the novel NCaMP7 and FGCaMP7, designed specifically for in vivo calcium imaging. Mice were injected with a viral vector encoding calcium sensor, head-mounted with an NVista HD miniscope, and allowed to explore a completely novel environment (circular track surrounded by visual cues) without any reinforcement stimuli, in order to avoid potential interference from reward-related behavior. First, we calculated the average time required for each CA1 cell to acquire its place field. We found that 25% of CA1 place fields were formed at the first arrival in the corresponding place, while the average tuning latency for all place fields in a novel environment equaled 247 s. After 24 h, when the environment was familiar to the animals, place fields formed faster, independent of retention of cognitive maps during this session. No cumulation of selectivity score was observed between these two sessions. Using dimensionality reduction, we demonstrated that the population activity of rapidly tuned CA1 place cells allowed the reconstruction of the geometry of the navigated circular maze; the distribution of reconstruction error between the mice was consistent with the distribution of the average place field selectivity score in them. Our data thus show that neuronal activity recorded with genetically encoded calcium sensors revealed fast behavior-dependent plasticity in the mouse hippocampus, resulting in the rapid formation of place fields and population activity that allowed the reconstruction of the geometry of the navigated maze.

scholarly journals Graphene-Based Artificial Synapses with Tunable Plasticity

2021 ◽  
Vol 17 (4) ◽  
pp. 1-21
Author(s):  
He Wang ◽  
Nicoleta Cucu Laurenciu ◽  
Yande Jiang ◽  
Sorin Cotofana
Keyword(s):  
Synaptic Plasticity ◽  
Weight Change ◽  
Time Scale ◽  
Large Scale ◽  
Synaptic Weight ◽  
Low Voltage ◽  
Logic Gates ◽  
Full Potential ◽  
Back Gate ◽  
Input Spike

Design and implementation of artificial neuromorphic systems able to provide brain akin computation and/or bio-compatible interfacing ability are crucial for understanding the human brain’s complex functionality and unleashing brain-inspired computation’s full potential. To this end, the realization of energy-efficient, low-area, and bio-compatible artificial synapses, which sustain the signal transmission between neurons, is of particular interest for any large-scale neuromorphic system. Graphene is a prime candidate material with excellent electronic properties, atomic dimensions, and low-energy envelope perspectives, which was already proven effective for logic gates implementations. Furthermore, distinct from any other materials used in current artificial synapse implementations, graphene is biocompatible, which offers perspectives for neural interfaces. In view of this, we investigate the feasibility of graphene-based synapses to emulate various synaptic plasticity behaviors and look into their potential area and energy consumption for large-scale implementations. In this article, we propose a generic graphene-based synapse structure, which can emulate the fundamental synaptic functionalities, i.e., Spike-Timing-Dependent Plasticity (STDP) and Long-Term Plasticity . Additionally, the graphene synapse is programable by means of back-gate bias voltage and can exhibit both excitatory or inhibitory behavior. We investigate its capability to obtain different potentiation/depression time scale for STDP with identical synaptic weight change amplitude when the input spike duration varies. Our simulation results, for various synaptic plasticities, indicate that a maximum 30% synaptic weight change and potentiation/depression time scale range from [-1.5 ms, 1.1 ms to [-32.2 ms, 24.1 ms] are achievable. We further explore the effect of our proposal at the Spiking Neural Network (SNN) level by performing NEST-based simulations of a small SNN implemented with 5 leaky-integrate-and-fire neurons connected via graphene-based synapses. Our experiments indicate that the number of SNN firing events exhibits a strong connection with the synaptic plasticity type, and monotonously varies with respect to the input spike frequency. Moreover, for graphene-based Hebbian STDP and spike duration of 20ms we obtain an SNN behavior relatively similar with the one provided by the same SNN with biological STDP. The proposed graphene-based synapse requires a small area (max. 30 nm 2 ), operates at low voltage (200 mV), and can emulate various plasticity types, which makes it an outstanding candidate for implementing large-scale brain-inspired computation systems.

scholarly journals Effects of memristive synapse radiation interactions on learning in spiking neural networks

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
Sumedha Gandharava Dahl ◽  
Robert C. Ivans ◽  
Kurtis D. Cantley
Keyword(s):  
Action Potentials ◽  
Radiation Effects ◽  
Synaptic Weight ◽  
Learning Rule ◽  
Spike Timing ◽  
Spiking Neural Network ◽  
Synaptic Action ◽  
Drift Model ◽  
Spatio Temporal ◽  
Post Synaptic Neuron

AbstractThis study uses advanced modeling and simulation to explore the effects of external events such as radiation interactions on the synaptic devices in an electronic spiking neural network. Specifically, the networks are trained using the spike-timing-dependent plasticity (STDP) learning rule to recognize spatio-temporal patterns (STPs) representing 25 and 100-pixel characters. Memristive synapses based on a TiO2 non-linear drift model designed in Verilog-A are utilized, with STDP learning behavior achieved through bi-phasic pre- and post-synaptic action potentials. The models are modified to include experimentally observed state-altering and ionizing radiation effects on the device. It is found that radiation interactions tend to make the connection between afferents stronger by increasing the conductance of synapses overall, subsequently distorting the STDP learning curve. In the absence of consistent STPs, these effects accumulate over time and make the synaptic weight evolutions unstable. With STPs at lower flux intensities, the network can recover and relearn with constant training. However, higher flux can overwhelm the leaky integrate-and-fire post-synaptic neuron circuits and reduce stability of the network.

Task-Dependent Representations in Rat Hippocampal Place Neurons

1997 ◽  
Vol 78 (2) ◽  
pp. 597-613 ◽  
Author(s):  
Tsuneyuki Kobayashi ◽  
Hisao Nishijo ◽  
Masaji Fukuda ◽  
Jan Bures ◽  
Taketoshi Ono
Keyword(s):  
Open Field ◽  
Hippocampal Neurons ◽  
Hippocampal Formation ◽  
Movement Speed ◽  
Neuron Activity ◽  
Place Field ◽  
Complex Spike ◽  
Place Fields ◽  
Sequential Trials ◽  
Self Stimulation

Kobayashi, Tsuneyuki, Hisao Nishijo, Masaji Fukuda, Jan Bures, and Taketoshi Ono. Task-dependent representations in rat hippocampal place neurons. J. Neurophysiol. 78: 597–613, 1997. It is suggested that the hippocampal formation is essential to spatial representations by flexible encoding of diverse information during navigation, which includes not only externally generated sensory information such as visual and auditory sensation but also ideothetic information concerning locomotion (i.e., internally generated information such as proprioceptive and vestibular sensation) as well as information concerning reward. In the present study, we investigated how various types of information are represented in the hippocampal formation, by recording hippocampal complex-spike cells from rats that performed three types of place learning tasks in a circular open field with the use of intracranial self-stimulation as reward. The intracranial self-stimulation reward was delivered in the following three contexts: if the rat 1) entered an experimenter-determined reward place within the open field, and this place was randomly varied in sequential trials; 2) entered two specific places, one within and one outside the place field (an area identified by change in activity of a place neuron); or 3) entered an experimenter-specified place outside the place field. Because the behavioral trails during navigation were more constant in the second task than in the first task, ideothetic information concerning locomotion was more relevant to acquiring reward in the second task than in the first task. Of 43 complex-spike cells recorded, 37 displayed place fields under the first task. Of these 37 place neurons, 34 also had significant reward correlates only inside the place field. Although reward and place correlates of the place neuron activity did not change between the first and second tasks, neuronal correlates to behavioral variables for locomotion such as movement speed, direction, and turning angle significantly increased in the second task. Furthermore, 6 of 31 place neurons tested with the third task, in which the reward place was located outside the original place field, shifted place fields. The results indicated that neuronal correlates of most place neurons flexibly increased their sensitivity to relevant information in a given context and environment, and some place neurons changed the place field per se with place reward association. These results suggest two strategies for how hippocampal neurons incorporate an incredible variety of perceptions into a unified representation of the environment: through flexible use of information and the creation of new representations.

scholarly journals Behavioral Time Scale Plasticity of Place Fields: Mathematical Analysis

2021 ◽  
Vol 15 ◽  
Author(s):  
Ian Cone ◽  
Harel Z. Shouval
Keyword(s):  
Synaptic Plasticity ◽  
Fixed Points ◽  
Time Scales ◽  
Convergence Time ◽  
Simple Mathematical Model ◽  
System Parameters ◽  
Temporal Correlations ◽  
Postsynaptic Activity ◽  
Naturally Occurring ◽  
Place Fields

Traditional synaptic plasticity experiments and models depend on tight temporal correlations between pre- and postsynaptic activity. These tight temporal correlations, on the order of tens of milliseconds, are incompatible with significantly longer behavioral time scales, and as such might not be able to account for plasticity induced by behavior. Indeed, recent findings in hippocampus suggest that rapid, bidirectional synaptic plasticity which modifies place fields in CA1 operates at behavioral time scales. These experimental results suggest that presynaptic activity generates synaptic eligibility traces both for potentiation and depression, which last on the order of seconds. These traces can be converted to changes in synaptic efficacies by the activation of an instructive signal that depends on naturally occurring or experimentally induced plateau potentials. We have developed a simple mathematical model that is consistent with these observations. This model can be fully analyzed to find the fixed points of induced place fields and how these fixed points depend on system parameters such as the size and shape of presynaptic place fields, the animal's velocity during induction, and the parameters of the plasticity rule. We also make predictions about the convergence time to these fixed points, both for induced and pre-existing place fields.

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