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Lynn, M. B.

Publications and source records attributed to Lynn, M. B..

4 recordsLinked to original sources

Functional separation of long-range inputs by intrinsic dynamics of dorsal raphe 5-HT neurons

Monoaminergic nuclei such as the serotonergic dorsal raphe nucleus (DRN) receive synaptic inputs containing functionally distinct streams of information, yet the dimensionality of the resulting output population code and its cellular underpinning are currently unknown. By combining electrophysiological and computational approaches, here we uncover separable neural encoding of two excitatory inputs conveying disjunct information to DRN 5-HT neurons - the lateral habenula (LHb) and medial prefrontal cortex (mPFC). Dual-color opsin strategies revealed that a population of 5-HT neurons receive inputs from both mPFC and LHb. Subthreshold excitatory postsynaptic potentials triggered by both inputs were largely indistinguishable, yet suprathreshold spiking behavior exhibited input-specific latencies and dispersion statistics. A support vector machine classifier demonstrated that input identity can be accurately decoded from spike timing, but not subthreshold events, of under ten 5-HT neurons. Upon examining the intrinsic cellular mechanisms in 5-HT neurons that couple EPSPs to spiking dynamics, we uncovered two likely candidate mechanisms: a low-threshold calcium conductance that selectively boosts slow excitatory inputs, and a subthreshold, voltage-dependent membrane noise that generates variation of spike latency and jitter. Stochastic simulations suggest that these two intrinsic properties of 5-HT neurons are sufficient to transform LHb and mPFC inputs into distinct output spiking patterns. These results reveal that hub-like networks like the DRN can segregate distinct informational streams by a cell-intrinsic mechanism. The resulting emergent population spike synchrony code provides a means for the DRN to widely broadcast these streams as a multiplexed signal. Significance statementPhylogenetically old neuromodulatory systems in the brain, such as the serotonergic dorsal raphe nucleus, are compact yet richly innervated structures. Here, we use the raphe as a testbed to ask how distinct informational sources to hub-like networks are processed or integrated into a coherent neural code. Using electrophysiological and computational methods, including biophysically grounded stochastic simulations, we find that intrinsic noise mechanisms in serotonergic neurons are critical to transform approximately matched subthreshold excitation into distinct spike timing profiles. Thus, cell-intrinsic noise mechanisms can effectively synthesize a spike synchrony code that, we hypothesize, multiplexes input information to hub-like networks at the population level even in the absence of strong local circuit interactions.

neuroscience↗

A slow 5-HT1AR-mediated recurrent inhibitory network in raphe computes contextual value through synaptic facilitation

Serotonin (5-HT) neurons in the dorsal raphe nucleus (DRN) receive a constellation of long-range inputs, yet guiding principles of local circuit organization and underlying computations in this nucleus are largely unknown. Using inputs from the lateral habenula (LHb) to interrogate the processing features of the DRN, we uncovered 5-HT1A receptor-mediated recurrent connections between 5-HT neurons, refuting classical theories of autoinhibition. Cellular electrophysiology and imaging of a genetically encoded 5-HT sensor revealed that these recurrent inhibitory connections spanned the raphe, were slow, stochastic, strongly facilitating, and gated spike output. These features collectively conveyed highly non-linear dynamics to this network, generating excitation-driven inhibition and winner-take-all computations. In vivo optogenetic activation of LHb inputs to DRN, at frequencies where these computations are predicted to ignite, transiently disrupted expression of a reward-conditioned response in an auditory conditioning task. Together, these data identify a core computation supported by an unsuspected slow serotonergic recurrent inhibitory network.

neuroscience↗

Temporal derivative computation in the dorsal raphe network revealed by an experimentally-driven augmented integrate-and-fire modeling framework

By means of an expansive innervation, the serotonin (5-HT) neurons of the dorsal raphe nucleus (DRN) are positioned to enact coordinated modulation of circuits distributed across the entire brain in order to adaptively regulate behavior. Yet the network computations that emerge from the excitability and connectivity features of the DRN are still poorly understood. To gain insight into these computations, we began by carrying out a detailed electrophysiological characterization of genetically-identified mouse 5-HT and somatostatin (SOM) neurons. We next developed a single-neuron modeling framework that combines the realism of Hodgkin-Huxley models with the simplicity and predictive power of generalized integrate-and-fire (GIF) models. We found that feedforward inhibition of 5-HT neurons by heterogeneous SOM neurons implemented divisive inhibition, while endocannabinoid-mediated modulation of excitatory drive to the DRN increased the gain of 5-HT output. Our most striking finding was that the output of the DRN encodes a mixture of the intensity and temporal derivative of its input, and that the temporal derivative component dominates this mixture precisely when the input is increasing rapidly. This network computation primarily emerged from prominent adaptation mechanisms found in 5-HT neurons, including a previously undescribed dynamic threshold. By applying a bottom-up neural network modeling approach, our results suggest that the DRN is particularly apt to encode input changes over short timescales, reflecting one of the salient emerging computations that dominate its output to regulate behavior.

neuroscience↗

A synthetic likelihood solution to the silent synapse estimation problem

Functional features of populations of synapses are typically inferred from random electrophysiological sampling of small subsets of synapses. Are these samples unbiased? Here, we developed a biophysically constrained statistical framework for addressing this question and applied it to assess the performance of a widely used method based on a failure-rate analysis to quantify the occurrence of silent (AMPAR-lacking) synapses in neural networks. We simulated this method in silico and found that it is characterized by strong and systematic biases, poor reliability and weak statistical power. Key conclusions were validated by whole-cell recordings from hippocampal neurons. To address these shortcomings, we developed a simulator of the experimental protocol and used it to compute a synthetic likelihood. By maximizing the likelihood, we inferred silent synapse fraction with no bias, low variance and superior statistical power over alternatives. Together, this generalizable approach highlights how a simulator of experimental methodologies can substantially improve the estimation of physiological properties.

neuroscience↗