Multiplicative Synaptic Normalization and a Nonlinear Hebb Rule Underlie a Neurotrophic Model of Competitive Synaptic Plasticity

2002 ◽  
Vol 14 (6) ◽  
pp. 1311-1322 ◽  
Author(s):  
T. Elliott ◽  
N. R. Shadbolt
Keyword(s):  
Synaptic Plasticity ◽  
Activity Patterns ◽  
Competitive Dynamics ◽  
Hebb Rule

Synaptic normalization is used to enforce competitive dynamics in many models of developmental synaptic plasticity. In linear and semilinear Hebbian models, multiplicative synaptic normalization fails to segregate afferents whose activity patterns are positively correlated. To achieve this, the biologically problematic device of subtractive synaptic normalization must be used instead. Our own model of competition for neurotrophic support, which can segregate positively correlated afferents, was developed in part in an attempt to overcome these problems by removing the need for synaptic normalization altogether. However, we now show that the dynamics of our model decompose into two decoupled subspaces, with competitive dynamics being implemented in one of them through a nonlinear Hebb rule and multiplicative synaptic normalization. This normalization is “emergent” rather than imposed. We argue that these observations permit biologically plausible forms of synaptic normalization to be viewed as abstract and general descriptions of the underlying biology in certain scaleless models of synaptic plasticity.

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 Coupling an aVLSI Neuromorphic Vision Chip to a Neurotrophic Model of Synaptic Plasticity: The Development of Topography

2002 ◽  
Vol 14 (10) ◽  
pp. 2353-2370 ◽  
Author(s):  
Terry Elliott ◽  
Jörg Kramer
Keyword(s):  
Synaptic Plasticity ◽  
Neuronal Development ◽  
Ganglion Cells ◽  
Activity Patterns ◽  
Retinal Ganglion ◽  
Biologically Inspired ◽  
Silicon Neurons ◽  
Retina Chip ◽  
Developing System ◽  
Activity Dependent

We couple a previously studied, biologically inspired neurotrophic model of activity-dependent competitive synaptic plasticity and neuronal development to a neuromorphic retina chip. Using this system, we examine the development and refinement of a topographic mapping between an array of afferent neurons (the retinal ganglion cells) and an array of target neurons. We find that the plasticity model can indeed drive topographic refinement in the presence of afferent activity patterns generated by a real-world device. We examine the resilience of the developing system to the presence of high levels of noise by adjusting the spontaneous firing rate of the silicon neurons.

Biological Learning: Synaptic Plasticity, Hebb Rule and Spike TimingDependent Plasticity

2011 ◽  
pp. 111-132
Author(s):  
Paul Munro ◽  
Hannu Toivonen ◽  
Geoffrey I. Webb ◽  
Wray Buntine ◽  
Peter Orbanz ◽  
...  
Keyword(s):  
Synaptic Plasticity ◽  
Hebb Rule ◽  
Biological Learning

scholarly journals Bidirectional Regulation of eEF2 Phosphorylation Controls Synaptic Plasticity by Decoding Neuronal Activity Patterns

2015 ◽  
Vol 35 (10) ◽  
pp. 4403-4417 ◽  
Author(s):  
P. K. McCamphill ◽  
C. A. Farah ◽  
M. N. Anadolu ◽  
S. Hoque ◽  
W. S. Sossin
Keyword(s):  
Synaptic Plasticity ◽  
Neuronal Activity ◽  
Activity Patterns ◽  
Bidirectional Regulation

scholarly journals Pre- and postsynaptically expressed spike-timing-dependent plasticity contribute differentially to neuronal learning

2021 ◽  
Author(s):  
Beatriz Eymi Pimentel Mizusaki ◽  
Sally Si Ying Li ◽  
Rui Ponte Costa ◽  
Jesper Sjöström
Keyword(s):  
Synaptic Plasticity ◽  
Activity Patterns ◽  
Experimental Studies ◽  
Spike Timing ◽  
Response Amplitude ◽  
Synaptic Response ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Response Range

A plethora of experimental studies have shown that long-term synaptic plasticity can be expressed pre- or postsynaptically depending on a range of factors such as developmental stage, synapse type, and activity patterns. The functional consequences of this diversity are not clear, although it is understood that whereas postsynaptic expression of plasticity predominantly affects synaptic response amplitude, presynaptic expression alters both synaptic response amplitude and short-term dynamics. In most models of neuronal learning, long-term synaptic plasticity is implemented as changes in connective weights. The consideration of long-term plasticity as a fixed change in amplitude corresponds more closely to post- than to presynaptic expression, which means theoretical outcomes based on this choice of implementation may have a postsynaptic bias. To explore the functional implications of the diversity of expression of long-term synaptic plasticity, we adapted a model of long-term plasticity, more specifically spike-timing-dependent plasticity (STDP), such that it was expressed either independently pre- or postsynaptically, or in a mixture of both ways. We compared pair-based standard STDP models and a biologically tuned triplet STDP model, and investigated the outcomes in a minimal setting, using two different learning schemes: in the first, inputs were triggered at different latencies, and in the second a subset of inputs were temporally correlated. We found that presynaptic changes adjusted the speed of learning, while postsynaptic expression was more efficient at regulating spike timing and frequency. When combining both expression loci, postsynaptic changes amplified the response range, while presynaptic plasticity allowed control over postsynaptic firing rates, potentially providing a form of activity homeostasis. Our findings highlight how the seemingly innocuous choice of implementing synaptic plasticity by single weight modification may unwittingly introduce a postsynaptic bias in modelling outcomes. We conclude that pre- and postsynaptically expressed plasticity are not interchangeable, but enable complimentary functions.

scholarly journals Unsupervised Learning of Persistent and Sequential Activity

2018 ◽  
Author(s):  
Ulises Pereira ◽  
Nicolas Brunel
Keyword(s):  
Synaptic Plasticity ◽  
Firing Rate ◽  
Bifurcation Diagram ◽  
Activity Patterns ◽  
Learning Rule ◽  
Brain Structures ◽  
Homeostatic Plasticity ◽  
Delayed Response ◽  
Plasticity Rule ◽  
Recurrent Architecture

AbstractTwo strikingly distinct types of activity have been observed in various brain structures during delay periods of delayed response tasks: Persistent activity (PA), in which a sub-population of neurons maintains an elevated firing rate throughout an entire delay period; and Sequential activity (SA), in which sub-populations of neurons are activated sequentially in time. It has been hypothesized that both types of dynamics can be ‘learned’ by the relevant networks from the statistics of their inputs, thanks to mechanisms of synaptic plasticity. However, the necessary conditions for a synaptic plasticity rule and input statistics to learn these two types of dynamics in a stable fashion are still unclear. In particular, it is unclear whether a single learning rule is able to learn both types of activity patterns, depending on the statistics of the inputs driving the network. Here, we first characterize the complete bifurcation diagram of a firing rate model of multiple excitatory populations with an inhibitory mechanism, as a function of the parameters characterizing its connectivity. We then investigate how an unsupervised temporally asymmetric Hebbian plasticity rule shapes the dynamics of the network. Consistent with previous studies, we find that for stable learning of PA and SA, an additional stabilization mechanism, such as multiplicative homeostatic plasticity, is necessary. Using the bifurcation diagram derived for fixed connectivity, we study analytically the temporal evolution and the steady state of the learned recurrent architecture as a function of parameters characterizing the external inputs. Slow changing stimuli lead to PA, while fast changing stimuli lead to SA. Our network model shows how a network with plastic synapses can stably and flexibly learn PA and SA in an unsupervised manner.

scholarly journals Epigenetic regulation of the circadian genePer1in the hippocampus mediates age-related changes in memory and synaptic plasticity

2018 ◽  
Author(s):  
Janine L. Kwapis ◽  
Yasaman Alaghband ◽  
Enikö A. Kramár ◽  
Alberto J. López ◽  
Annie Vogel Ciernia ◽  
...  
Keyword(s):  
Synaptic Plasticity ◽  
Circadian Clock ◽  
Clock Gene ◽  
Memory Performance ◽  
Activity Patterns ◽  
Memory Formation ◽  
Long Term Memory ◽  
Term Memory ◽  
Age Related

AbstractAging is accompanied by impairments in both circadian rhythmicity and long-term memory. Although it is clear that memory performance is affected by circadian cycling, it is unknown whether age-related disruption of the circadian clock causes impaired hippocampal memory. Here, we show that the repressive histone deacetylase HDAC3 restricts long-term memory, synaptic plasticity, and learning-induced expression of the circadian genePer1in the aging hippocampus without affecting rhythmic circadian activity patterns. We also demonstrate that hippocampalPer1is critical for long-term memory formation. Together, our data challenge the traditional idea that alterations in the core circadian clock drive circadian-related changes in memory formation and instead argue for a more autonomous role for circadian clock gene function in hippocampal cells to gate the likelihood of long-term memory formation.

An Analysis of Synaptic Normalization in a General Class of Hebbian Models

2003 ◽  
Vol 15 (4) ◽  
pp. 937-963 ◽  
Author(s):  
Terry Elliott
Keyword(s):  
Fixed Point ◽  
Synaptic Plasticity ◽  
Electrical Activity ◽  
General Class ◽  
Competitive Dynamics ◽  
Synaptic Growth ◽  
Activity Dependent ◽  
Multiplicative Normalization

In standard Hebbian models of developmental synaptic plasticity, synaptic normalization must be introduced in order to constrain synaptic growth and ensure the presence of activity-dependent, competitive dynamics. In such models, multiplicative normalization cannot segregate afferents whose patterns of electrical activity are positively correlated, while subtractive normalization can. It is now widely believed that multiplicative normalization cannot segregate positively correlated afferents in any Hebbian model. However, we recently provided a counterexample to this belief by demonstrating that our own neurotrophic model of synaptic plasticity, which can segregate positively correlated afferents, can be reformulated as a nonlinear Hebbian model with competition implemented through multiplicative normalization. We now perform an analysis of a general class of Hebbian models under general forms of synaptic normalization. In particular, we extract conditions on the forms of these rules that guarantee that such models possess a fixed-point structure permitting the segregation of all but perfectly correlated afferents. We find that the failure of multiplicative normalization to segregate positively correlated afferents in a standard Hebbian model is quite atypical.

Sign in / Sign up

Export Citation Format

Share Document