Linear-Nonlinear Cascades Capture Synaptic Dynamics

Abstract

Synaptic dynamics differ markedly across connections and strongly regulate how action potentials are being communicated. To model the range of synaptic dynamics observed in experiments, we develop a flexible mathematical framework based on a linear-nonlinear operation. This model can capture various experimentally observed features of synaptic dynamics and different types of heteroskedasticity. Despite its conceptual simplicity, we show it is more adaptable than previous models. Combined with a standard maximum likelihood approach, synaptic dynamics can be accurately and efficiently characterized using naturalistic stimulation patterns. These results make explicit that synaptic processing bears algorithmic similarities with information processing in convolutional neural networks. Author summaryUnderstanding how information is transmitted relies heavily on knowledge of the underlying regulatory synaptic dynamics. Existing computational models for capturing such dynamics are often either very complex or too restrictive. As a result, effectively capturing the different types of dynamics observed experimentally remains a challenging problem. Here, we propose a mathematically flexible linear-nonlinear model that is capable of efficiently characterizing synaptic dynamics. We demonstrate the ability of this model to capture different features of experimentally observed data.The nervous system has evolved a communication system largely based on temporal 2 sequences of action potentials. A central feature of this communication is that action 3 potentials are communicated with variable efficacy on short (10 ms -10 s) time 4 scales [1-6]. The dynamics of synaptic efficacy at short time scales, or short-term 5 plasticity (STP), can be a powerful determinant of the flow of information, allowing the 6 same axon to communicate independent messages to different post-synaptic 7 targets [7, 8]. Properties of STP vary markedly across projections [9-11], leading to the 8idea that connections can be conceived as belonging to distinct classes [12,13] and that 9 these distinct classes shape information transmission in vivo [14][15][16]. Thus, to 10 May 29, 2020 1/25 understand the flow of information in neuronal networks, the connectome must be 11 indexed with an accurate description of STP properties. 12One approach to characterizing synaptic dynamics is to perform targeted 13 experiments and extract a summary feature, most commonly the paired-pulse 14 ratio [5,[17][18][19], whereby a synapse can be classified as short-term depressing (STD) or 15 short-term facilitating (STF). However, a single summary feature is insufficient to 16 capture the full extent of STP diversity. Longer or more complex stimulation patterns 17 are required to describe delayed facilitation onset [6], biphasic STP [20, 21] or the 18 distinction between supra-and sub-linear facilitation [22]. Such atypical STP dynamics 19 challenge the traditional dichotomy of STF and STD and suggest that more complex 20 phenotypes can exist and contribute to network function in...

Burst coding despite unimodal interval distributions

“…5.Ai). Such a dependence between the number of intra-burst spikes and the changes in synaptic efficacy is observed in experiments (36). Since the probability of observing N subsequent events with short ISIs decreases as N increases this allows post-synaptic cells to better distinguish events from bursts, regardless of the overlap between IEI and IBI interval distributions.…”

“…For the decoding cells, we adopted the model described in Ref. (36), that defines STP weight functions as a composition of a linear filtering and nonlinear function. This can be formalized as…”

Inferring Deep Brain Stimulation Induced Short-term Synaptic Plasticity Using Novel Dual Optimization Algorithm

“…Therefore, these factors influence the interaction between the pre-and postsynaptic neurons which cannot be captured by the dynamics of STP solely. It was suggested that alternative models of plasticity with less biophysical constraints like generalized bilinear model ( 16) and linear-nonlinear cascade model (18) could provide more flexible representation of STP dynamics compared to the TM model. Despite such limitations, the conventional three-parameter TM model is sufficient to describe STP in many cases (13).…”

“…Ghanbari et al (16,17) also used a different GLM to directly estimate the STP dynamics, a model that can be considered as an alternative to the TM model. Rossbroich et al (18) introduced a new synaptic model which represents short-term dynamics by combining an exponential kernel with a non-linear readout function. The simplicity of this model enabled applying the concepts of STP in artificial neural networks to examine its role in learning.…”

Optimizing interneuron circuits for compartment-specific feedback inhibition

“…A wrong short-term plasticity model will therefore lead to a wrong connectivity in the circuit. Here, it will be interesting to see how a more expressive model of short-term plasticity [62] influences the optimal circuit structure. Finally, of course, our optimality assumption could be wrong to different degrees.…”