Spike-Timing-Dependent Plasticity in Balanced Random Networks

Abstract

The balanced random network model attracts considerable interest because it explains the irregular spiking activity at low rates and large membrane potential fluctuations exhibited by cortical neurons in vivo. In this article, we investigate to what extent this model is also compatible with the experimentally observed phenomenon of spike-timing-dependent plasticity (STDP). Confronted with the plethora of theoretical models for STDP available, we reexamine the experimental data. On this basis, we propose a novel STDP update rule, with a multiplicative dependence on the synaptic weight for depression, and a power law dependence for potentiation. We show that this rule, when implemented in large, balanced networks of realistic connectivity and sparseness, is compatible with the asynchronous irregular activity regime. The resultant equilibrium weight distribution is unimodal with fluctuating individual weight trajectories and does not exhibit development of structure. We investigate the robustness of our results with respect to the relative strength of depression. We introduce synchronous stimulation to a group of neurons and demonstrate that the decoupling of this group from the rest of the network is so severe that it cannot effectively control the spiking of other neurons, even those with the highest convergence from this group.

Formation and maintenance of neuronal assemblies through synaptic plasticity

“…1b). However, as has been reported in previous studies of balanced networks with STDP 23,34 , after a few tens of stimulus applications, the network firing rate increased abruptly (Fig. 1b) and exhibited high frequency oscillations (Fig.…”

“…These dynamics were due to runaway positive feedback of Hebbian STDP during training 35 . Indeed, such instability has precluded the development of realistic models of stimulus-evoked plasticity in such systems 23 . Our study investigates homeostatic mechanisms for synaptic plasticity 34,35 , which regulate network activity, preventing this instability and producing spontaneous dynamics consistent with experimental data from cortex.…”

“…Conversely, work in balanced attractor circuits has lacked realistic plasticity mechanisms 21,22 . This is in part due to the fact that previous attempts to embed spike timing-dependent plasticity (STDP) in balanced networks have led to pathological behaviour 23 . Recently, detailed STDP models have been proposed [24][25][26] , but studies so far have been restricted to small or homogeneous networks.…”

Simulation of networks of spiking neurons: A review of tools and strategies

“…The scaling is shown for the case that all the synapses are static, and for the case that the excitatory-excitatory synapses implement multiplicative spike-timing dependent plasticity with an all-to-all spike pairing scheme (Rubin et al 2001). For implementation details of the STDP, see Morrison et al (2007b), for further network parameters, see the supplementary material. The network activity is in the asynchronous irregular regime at 10 Hz.…”

The state of MIIND

“…The BlueBrain project (e.g., Markram, 2006) aims to model an entire cortical column in realistic detail. The effects of synaptic plasticity rules can now be investigated in great detail both on the neuronal (e.g., Gerstner & Kistler, 2002) and the network level (Morrison, Aertsen, & Diesmann, 2007). But where experimental (cognitive) neuroscience has reached a breakthrough at the system level, and allows us to observe the global flow of neural activity in the active brain, computational modelling has yet to follow suit here.…”