Connectivity reflects coding: a model of voltage-based STDP with homeostasis

Electrophysiological connectivity patterns in cortex often have a few strong connections, which are sometimes bidirectional, among a lot of weak connections. To explain these connectivity patterns, we created a model of spike timing–dependent plasticity (STDP) in which synaptic changes depend on presynaptic spike arrival and the postsynaptic membrane potential, filtered with two different time constants. Our model describes several nonlinear effects that are observed in STDP experiments, as well as the voltage dependence of plasticity. We found that, in a simulated recurrent network of spiking neurons, our plasticity rule led not only to development of localized receptive fields but also to connectivity patterns that reflect the neural code. For temporal coding procedures with spatio-temporal input correlations, strong connections were predominantly unidirectional, whereas they were bidirectional under rate-coded input with spatial correlations only. Thus, variable connectivity patterns in the brain could reflect different coding principles across brain areas; moreover, our simulations suggested that plasticity is fast.

Towards a unified understanding of synaptic plasticity

Giuseppe Chindemi

Understanding how learning and memory formation work in the brain is a major challenge in neuroscience, with important implications for many other fields, including medicine and industry. It is nowadays widely accepted that synaptic plasticity is the biological foundation of these higher order brain functions. So far, many different plastic behaviors have been intensively studied and characterized, leading to the definition of several forms of plasticity: structural, functional, homeostatic, inhibitory, and many others. Unfortunately, despite all the interest and efforts of the scientific community, a complete and consistent understanding of synaptic plasticity is still lacking. The main goal of this study is to unify data and theories on synaptic plasticity in a comprehensive model, suitable for studying learning and memory down to the synapse level. To reach our objective, we identified a minimal set of biological mechanisms responsible for plastic dynamics and integrated them into a single synapse model, relying whenever possible on well accepted sub-models from literature. We designed a data-driven fitting and generalization strategy to parameterize all excitatory-to-excitatory synapses in a large scale reconstruction of neocortical tissue [Markram et al., 2015]. Finally, we tested the effects of functional synaptic plasticity on neural circuits in simulations. Our model was able to capture not only the outcome of Spike Timing Dependent Plasticity (STDP) protocols used in the training phase, but also the one of all others available in the same experimental dataset [Markram et al., 1997b]. Moreover, it correctly reproduced results on distance-dependent synaptic plasticity [Sjöström and Häusser, 2006], even though this dataset was never used during fitting. In network simulations, we observed the emergence of a self-regulatory homeostatic mechanism, preventing runaway excitation. Furthermore, we noticed the strengthening of connections between neurons that are similarly innervated, as previously shown in vitro [Perin et al., 2011].

EPFL2018

Spike-timing dependent plasticity

Wulfram Gerstner

Spike Timing Dependent Plasticity (STDP) is a temporally asymmetric form of Hebbian learning induced by tight temporal correlations between the spikes of pre- and postsynaptic neurons. As with other forms of synaptic plasticity, it is widely believed that it underlies learning and information storage in the brain, as well as the development and refinement of neuronal circuits during brain development (e.g. Bi and Poo, 2001; Sjöström et al., 2008). With STDP, repeated presynaptic spike arrival a few milliseconds before postsynaptic action potentials leads in many synapse types to long-term potentiation (LTP) of the synapses, whereas repeated spike arrival after postsynaptic spikes leads to long-term depression (LTD) of the same synapse. The change of the synapse plotted as a function of the relative timing of pre- and postsynaptic action potentials is called the STDP function or learning window and varies between synapse types. The rapid change of the STDP function with the relative timing of spikes suggests the possibility of temporal coding schemes on a millisecond time scale.

2010

Towards a unified understanding of synaptic plasticity

Giuseppe Chindemi

EPFL2018