Girardi-Schappo, M. | bioRxiv Science

bioRxiv2022

Dentate gyrus mossy cells exhibit sparse coding via adaptive spike threshold dynamics

Hilar mossy cells (hMCs) are glutamatergic neurons in the dentate gyrus (DG) that receive inputs primarily from DG granule cells (GCs), CA3 pyramidal cells and local inhibitory interneurons. The hMCs then provide direct excitatory and disynaptic inhibitory feedback input to GCs. Behavioral and in vivo single unit recording experiments have implicated hMCs in pattern separation as well as is in spatial navigation and learning. It has, however, been difficult to mechanistically link the in vivo physiological behavior of hMCs with their intrinsic excitability properties that convert their synaptic inputs into spiking output. Here, we carried out electrophysiological recordings from the main cell types in the DG and found that hMCs displayed a highly adaptive threshold acting over a remarkably protracted time-scale. The hMC spike threshold increased linearly with increasing current stimulation and saturated at high current intensities. This threshold also increased in response to spiking and this effect also decayed over a long timescale, allowing for activity-dependent summation that limited hMC firing rates. This mechanism operates in parallel with a prominent medium after-hyperpolarizing potential (AHP) generated by the small conductance K+ channel. Based on experimentally derived parameters, we developed a phenomenological exponential integrate-and-fire model that closely mimics the hMC adaptive threshold. This lightweight model is amenable to its incorporation into large network models of the DG that will be conducive to deepen our understanding of the neural bases of pattern separation, spatial learning and navigation in the hippocampus. Statement of significanceRecent studies on hilar mossy cells have revealed that they are implicated in spatial navigation and mnemonic functions. Yet, the basic intrinsic characterization of these hMCs is still too superficial to explain their spiking behavior in vivo. Here, we describe novel biophysical properties of hMCs, including an independent relationship between spike latency and spike threshold as well as a slowly adapting spike threshold. These findings complement several other biophysical and connectivity similarities between hMCs and CA3 pyramidal cells, while emphasizing the contrast with hilar interneurons. Additionally, our results are well captured by a phenomenological model of the hMC which provides a useful framework to study the neural substrate of spatial navigation and learning in the dentate gyrus.

neuroscience↗

bioRxiv2020

A unified theory of E/I synaptic balance, quasicritical neuronal avalanches and asynchronous irregular spiking

Neuronal avalanches and asynchronous irregular (AI) firing patterns have been thought to represent distinct frameworks to understand the brain spontaneous activity. The former is typically present in systems where there is a balance between the slow accumulation of tension and its fast dissipation, whereas the latter is accompanied by the balance between synaptic excitation and inhibition (E/I). Here, we develop a new theory of E/I balance that relies on two homeostatic adaptation mechanisms: the short-term depression of inhibition and the spike-dependent threshold increase. First, we turn off the adaptation and show that the so-called static system has a typical critical point commonly attributed to self-organized critical models. Then, we turn on the adaptation and show that the network evolves to a dynamic regime in which: (I) E/I synapses balance regardless of any parameter choice; (II) an AI firing pattern emerges; and (III) neuronal avalanches display power laws. This is the first time that these three phenomena appear simultaneously in the same network activity. Thus, we show that the once thought opposing frameworks may be unified into a single dynamics, provided that adaptation mechanisms are in place. In our model, the AI firing pattern is a direct consequence of the hovering close to the critical line where external inputs are compensated by threshold growth, creating synaptic balance for any E/I weight ratio. HighlightsO_LIAsynchronous irregular (AI) firing happens together with power-law neuronal avalanches under self-organized synaptic balance. C_LIO_LISelf-organization towards the critical and balanced state (with AI and power-law avalanches) occur via short-term inhibition depression and firing threshold adaptation. C_LIO_LIThe avalanche exponents match experimental findings. C_LIO_LIThe adaptation time scales drive the self-organized dynamics towards different firing regimes. C_LI Author summaryTwo competing frameworks are employed to understand the brain spontaneous activity, both of which are backed by computational and experimental evidence: globally asynchronous and locally irregular (AI) activity arises in excitatory/inhibitory balanced networks subjected to external stimuli, whereas avalanche activity emerge in excitable systems on the critical point between active and inactive states. Here, we develop a new theory for E/I networks and show that there is a state where synaptic balance coexists with AI firing and power-law distributed neuronal avalanches. This regime is achieved through the introducing of short-term depression of inhibitory synapses and spike-dependent threshold adaptation. Thus, the system self-organizes towards the balance point, such that its AI activity arises from quasicritical fluctuations. The need for two independent adaptive mechanisms explains why different dynamical states are observed in the brain.

neuroscience↗