Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector

By Friedemann Zenke, Guillaume Hennequin, Wulfram Gerstner. 2013

167 citations recorded by the source corpus.

Hebbian changes of excitatory synapses are driven by and further enhance correlations between pre- and postsynaptic activities. Hence, Hebbian plasticity forms a positive feedback loop that can lead to instability in simulated neural networks. To keep activity at healthy, low levels, plasticity must therefore incorporate homeostatic control mechanisms. We find in numerical simulations of recurrent networks with a realistic triplet-based spike-timing-dependent plasticity rule (triplet STDP) that homeostasis has to detect rate changes on a timescale of seconds to minutes to keep the activity stable. We confirm this result in a generic mean-field formulation of network activity and homeostatic plasticity. Our results strongly suggest the existence of a homeostatic regulatory mechanism that reacts to firing rate changes on the order of seconds to minutes.

Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering, Statistical and Nonlinear Physics

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Citations in the corpus

The temporal paradox of Hebbian learning and homeostatic plasticity (2017, methodology): “In practice, such compensatory mechanisms must act on similar or even faster timescales than Hebbian plasticity itself [31 ,32 ,36,37 ,54,55 ,56].”
Emergent Dynamical Properties of the BCM Learning Rule (2017, background): “While homeostasis of synapses found in experiments is slow [12, 37], homeostasis of synapses in most theoretical models needs to be rapid and sometimes even instantaneous to achieve stability [33, 38, 39].”
Network-timing-dependent plasticity (2015, background): “…firing rates become excessive (Turrigiano et al., 1998; Trasande and Ramirez, 2007), such mechanisms have been shown to be insufficient to maintain network stability in simulations of networks of neurons incorporating empirically constrained STDP models at excitatory synapses (Zenke et al., 2013).”
Partial Breakdown of Input Specificity of STDP at Individual Synapses Promotes New Learning (2016, methodology): “In model simulations, stabilization of activity and synaptic competition can be attained by normalization implemented directly into the equations for synaptic weight changes (von der Malsburg, 1973; Oja, 1982; K. M. Miller, 1994; K. D. Miller, 1996; Fiete et al., 2010; Zenke et al., 2013).”
Models of Metaplasticity: A Review of Concepts (2015, methodology): “For modelers, this STDP protocol leads to the simplified view of the time-difference window in Figure 1B, where a single pre spike followed by a post spike will trigger LTP, whereas post followed by pre causes LTD.”
Modeling the dynamic interaction of Hebbian and homeostatic plasticity. (2014, background): “Instability occurs when the threshold change is so slow that the threshold never catches up (Cooper et al., 2004; Zenke et al., 2013) (when tq=tw>1=ðx2y0Þ for Equations 1 and 2).”
A Voltage-Based STDP Rule Combined with Fast BCM-Like Metaplasticity Accounts for LTP and Concurrent “Heterosynaptic” LTD in the Dentate Gyrus In Vivo (2015, unknown): “Recent theoretical results indicate that stable activity in neural networks requires a homeostatic rule with a fast detection (seconds to minutes) of firing rate changes [14].”
Electronic supplementary material: Hebbian plasticity requires compensatory processes on multiple timescales (2016, methodology): “that the presynaptic and postsynaptic neurons are mutually connected, of the same type, and fire at the same rate [9].”
A Diffusive Homeostatic Signal Maintains Neural Heterogeneity and Responsiveness in Cortical Networks (2015, background): “Narrow firing rate distributions are an obvious consequence of local homeostatic processes, as for instance shown recently with homeostasis implemented as local synaptic metaplasticity [14].”