scholarly journals Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits

2020 ◽  
Author(s):  
Alexandre Payeur ◽  
Jordan Guerguiev ◽  
Friedemann Zenke ◽  
Blake A. Richards ◽  
Richard Naud
Keyword(s):  
Synaptic Plasticity ◽  
Physiological Mechanism ◽  
Learning Rule ◽  
Credit Assignment ◽  
Hierarchical Networks ◽  
Postsynaptic Activity ◽  
Challenging Tasks ◽  
Synaptic Dynamics ◽  
Mathematical Analyses ◽  
Dependent Learning

AbstractSynaptic plasticity is believed to be a key physiological mechanism for learning. It is well-established that it depends on pre and postsynaptic activity. However, models that rely solely on pre and postsynaptic activity for synaptic changes have, to date, not been able to account for learning complex tasks that demand credit assignment in hierarchical networks. Here, we show that if synaptic plasticity is regulated by high-frequency bursts of spikes, then neurons higher in a hierarchical circuit can coordinate the plasticity of lower-level connections. Using simulations and mathematical analyses, we demonstrate that, when paired with short-term synaptic dynamics, regenerative activity in the apical dendrites, and synaptic plasticity in feedback pathways, a burst-dependent learning rule can solve challenging tasks that require deep network architectures. Our results demonstrate that well-known properties of dendrites, synapses, and synaptic plasticity are sufficient to enable sophisticated learning in hierarchical circuits.

Associative synaptic plasticity in hippocampal CA1 neurons is not sensitive to unpaired presynaptic activity

1996 ◽  
Vol 76 (1) ◽  
pp. 631-636 ◽  
Author(s):  
D. V. Buonomano ◽  
M. M. Merzenich
Keyword(s):  
Synaptic Plasticity ◽  
Temporal Structure ◽  
Learning Rule ◽  
Inverse Correlation ◽  
Pyramidal Cells ◽  
Long Term Potentiation ◽  
Time Varying ◽  
Postsynaptic Activity ◽  
Term Potentiation

1. Hebbian or associative synaptic plasticity has been proposed to play an important role in learning and memory. Whereas many behaviorally relevant stimuli are time-varying, most experimental and theoretical work on synaptic plasticity has focused on stimuli or induction protocols without temporal structure. Recent theoretical studies have suggested that associative plasticity sensitive to only the conjunction of pre- and postsynaptic activity is not an effective learning rule for networks required to learn time-varying stimuli. Our goal in the current experiment was to determine whether associative long-term potentiation (LTP) is sensitive to temporal structure. We examined whether the presentation of unpaired presynaptic pulses in addition to paired pre- and postsynaptic activity altered the induction of associative LTP. 2. By using intracellular recordings from CA1 pyramidal cells, associative long-term potentiation (LTP) was induced in a control pathway by pairing a single presynaptic pulse with postsynaptic depolarization every 5 s (50-70 x). The experimental pathway received the same training, with additional unpaired presynaptic pulses delivered in close temporal proximity, either after or before associative pairing. Five separate sets of experiments were performed with intervals of -200, -50, +50, +200, or +800 ms. Negative intervals indicate that the unpaired presynaptic pulse was presented before the depolarizing pulse. Our results showed that the presence of unpaired presynaptic pulses, occurring either before or after pairing, did not significantly alter the magnitude of LTP. 3. The experimental design permitted an analysis of whether changes in paired-pulse facilitation (PPF) occur as a result of associative LTP. The average degree of PPF was the same before and after LTP. However, there was a significant inverse correlation between the initial degree of PPF and the degree of PPF after LTP. There was no relationship between the change in PPF, and whether the first or second pulse had been paired with depolarization. 4. These results indicate that the presence of unpaired presynaptic pulses does not alter the induction of synaptic plasticity, suggesting that plasticity of the Schaffer collateral-CA1 synapse is primarily conjunctive rather than correlative.

Stable Competitive Dynamics Emerge from Multispike Interactions in a Stochastic Model of Spike-Timing-Dependent Plasticity

2006 ◽  
Vol 18 (10) ◽  
pp. 2414-2464 ◽  
Author(s):  
Peter A. Appleby ◽  
Terry Elliott
Keyword(s):  
Synaptic Plasticity ◽  
Stochastic Model ◽  
Higher Order ◽  
Spike Timing ◽  
Competitive Dynamics ◽  
Spike Timing Dependent Plasticity ◽  
Interaction Function ◽  
Dependent Plasticity ◽  
Order Interaction ◽  
Synaptic Dynamics

In earlier work we presented a stochastic model of spike-timing-dependent plasticity (STDP) in which STDP emerges only at the level of temporal or spatial synaptic ensembles. We derived the two-spike interaction function from this model and showed that it exhibits an STDP-like form. Here, we extend this work by examining the general n-spike interaction functions that may be derived from the model. A comparison between the two-spike interaction function and the higher-order interaction functions reveals profound differences. In particular, we show that the two-spike interaction function cannot support stable, competitive synaptic plasticity, such as that seen during neuronal development, without including modifications designed specifically to stabilize its behavior. In contrast, we show that all the higher-order interaction functions exhibit a fixed-point structure consistent with the presence of competitive synaptic dynamics. This difference originates in the unification of our proposed “switch” mechanism for synaptic plasticity, coupling synaptic depression and synaptic potentiation processes together. While three or more spikes are required to probe this coupling, two spikes can never do so. We conclude that this coupling is critical to the presence of competitive dynamics and that multispike interactions are therefore vital to understanding synaptic competition.

scholarly journals Event-Driven Simulation Scheme for Spiking Neural Networks Using Lookup Tables to Characterize Neuronal Dynamics

2006 ◽  
Vol 18 (12) ◽  
pp. 2959-2993 ◽  
Author(s):  
Eduardo Ros ◽  
Richard Carrillo ◽  
Eva M. Ortigosa ◽  
Boris Barbour ◽  
Rodrigo Agís
Keyword(s):  
Synaptic Plasticity ◽  
High Speed ◽  
Large Scale ◽  
Computational Cost ◽  
Brain Structures ◽  
Spike Timing ◽  
Neuronal Dynamics ◽  
Event Driven ◽  
Synaptic Dynamics

Nearly all neuronal information processing and interneuronal communication in the brain involves action potentials, or spikes, which drive the short-term synaptic dynamics of neurons, but also their long-term dynamics, via synaptic plasticity. In many brain structures, action potential activity is considered to be sparse. This sparseness of activity has been exploited to reduce the computational cost of large-scale network simulations, through the development of event-driven simulation schemes. However, existing event-driven simulations schemes use extremely simplified neuronal models. Here, we implement and evaluate critically an event-driven algorithm (ED-LUT) that uses precalculated look-up tables to characterize synaptic and neuronal dynamics. This approach enables the use of more complex (and realistic) neuronal models or data in representing the neurons, while retaining the advantage of high-speed simulation. We demonstrate the method's application for neurons containing exponential synaptic conductances, thereby implementing shunting inhibition, a phenomenon that is critical to cellular computation. We also introduce an improved two-stage event-queue algorithm, which allows the simulations to scale efficiently to highly connected networks with arbitrary propagation delays. Finally, the scheme readily accommodates implementation of synaptic plasticity mechanisms that depend on spike timing, enabling future simulations to explore issues of long-term learning and adaptation in large-scale networks.

scholarly journals The brain-tumor related protein podoplanin regulates synaptic plasticity and hippocampus-dependent learning and memory

2016 ◽  
Vol 48 (8) ◽  
pp. 652-668 ◽  
Author(s):  
Ana Cicvaric ◽  
Jiaye Yang ◽  
Sigurd Krieger ◽  
Deeba Khan ◽  
Eun-Jung Kim ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Synaptic Plasticity ◽  
Learning And Memory ◽  
Related Protein ◽  
Dependent Learning ◽  
The Brain

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.

scholarly journals An approximate backpropagation learning rule for memristor based neural networks using synaptic plasticity

2017 ◽  
Vol 237 ◽  
pp. 193-199 ◽  
Author(s):  
D. Negrov ◽  
I. Karandashev ◽  
V. Shakirov ◽  
Yu. Matveyev ◽  
W. Dunin-Barkowski ◽  
...  
Keyword(s):  
Neural Networks ◽  
Synaptic Plasticity ◽  
Learning Rule ◽  
Backpropagation Learning

Modeling the short-term dynamics of in vivo excitatory spike transmission

2018 ◽  
Author(s):  
Abed Ghanbari ◽  
Naixin Ren ◽  
Christian Keine ◽  
Carl Stoelzel ◽  
Bernhard Englitz ◽  
...  
Keyword(s):  
Synaptic Plasticity ◽  
Large Scale ◽  
Auditory Brainstem ◽  
Postsynaptic Neuron ◽  
Intracellular Recordings ◽  
Short Term ◽  
Functional Connection ◽  
Synaptic Dynamics ◽  
Synaptic Effects

AbstractInformation transmission in neural networks is influenced by both short-term synaptic plasticity (STP) as well as non-synaptic factors, such as after-hyperpolarization currents and changes in excitability. Although these effects have been widely characterized in vitro using intracellular recordings, how they interact in vivo is unclear. Here we develop a statistical model of the short-term dynamics of spike transmission that aims to disentangle the contributions of synaptic and non-synaptic effects based only on observed pre- and postsynaptic spiking. The model includes a dynamic functional connection with short-term plasticity as well as effects due to the recent history of postsynaptic spiking and slow changes in postsynaptic excitability. Using paired spike recordings, we find that the model accurately describes the short-term dynamics of in vivo spike transmission at a diverse set of identified and putative excitatory synapses, including a thalamothalamic connection in mouse, a thalamocortical connection in a female rabbit, and an auditory brainstem synapse in a female gerbil. We illustrate the utility of this modeling approach by showing how the spike transmission patterns captured by the model may be sufficient to account for stimulus-dependent differences in spike transmission in the auditory brainstem (endbulb of Held). Finally, we apply this model to large-scale multi-electrode recordings to illustrate how such an approach has the potential to reveal cell-type specific differences in spike transmission in vivo. Although short-term synaptic plasticity parameters estimated from ongoing pre- and postsynaptic spiking are highly uncertain, our results are partially consistent with previous intracellular observations in these synapses.Significance StatementAlthough synaptic dynamics have been extensively studied and modeled using intracellular recordings of post-synaptic currents and potentials, inferring synaptic effects from extracellular spiking is challenging. Whether or not a synaptic current contributes to postsynaptic spiking depends not only on the amplitude of the current, but also on many other factors, including the activity of other, typically unobserved, synapses, the overall excitability of the postsynaptic neuron, and how recently the postsynaptic neuron has spiked. Here we developed a model that, using only observations of pre- and postsynaptic spiking, aims to describe the dynamics of in vivo spike transmission by modeling both short-term synaptic plasticity and non-synaptic effects. This approach may provide a novel description of fast, structured changes in spike transmission.

scholarly journals Factors Influencing Short-term Synaptic Plasticity in the Avian Cochlear Nucleus Magnocellularis

2015 ◽  
Vol 9s2 ◽  
pp. JEN.S25472 ◽  
Author(s):  
Jason Tait Sanchez Quinones ◽  
Quinones Karla ◽  
Otto-Meyer Sebastian
Keyword(s):  
Synaptic Plasticity ◽  
Cochlear Nucleus ◽  
Time Course ◽  
Synaptic Depression ◽  
Auditory Brainstem ◽  
Short Term ◽  
Nucleus Magnocellularis ◽  
Developmental Shift ◽  
Acid Type ◽  
Synaptic Dynamics

Defined as reduced neural responses during high rates of activity, synaptic depression is a form of short-term plasticity important for the temporal filtering of sound. In the avian cochlear nucleus magnocellularis (NM), an auditory brainstem structure, mechanisms regulating short-term synaptic depression include pre-, post-, and extrasynaptic factors. Using varied paired-pulse stimulus intervals, we found that the time course of synaptic depression lasts up to four seconds at late-developing NM synapses. Synaptic depression was largely reliant on exogenous Ca2+-dependent probability of presynaptic neurotransmitter release, and to a lesser extent, on the desensitization of postsynaptic α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid-type glutamate receptor (AMPA-R). Interestingly, although extrasynaptic glutamate clearance did not play a significant role in regulating synaptic depression, blocking glutamate clearance at early-developing synapses altered synaptic dynamics, changing responses from depression to facilitation. These results suggest a developmental shift in the relative reliance on pre-, post-, and extrasynaptic factors in regulating short-term synaptic plasticity in NM.

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