Caya-Bissonnette, L. | bioRxiv Science

bioRxiv2023

Cellular Substrate of Eligibility Traces

The ability of synapses to undergo associative, activity-dependent weight changes constitutes a linchpin of current cellular models of learning and memory. It is, however, unclear whether canonical forms of Hebbian plasticity, which inherently detect correlations of cellular events occurring over short time scales, can solve the temporal credit assignment problem proper to learning driven by delayed behavioral outcomes. Recent evidence supports the existence of synaptic eligibility traces, a time decaying process that renders synapses momentarily eligible for a weight update by a delayed instructive signal. While eligibility traces offer a means of retrospective credit assignment, their material nature is unknown. Here, we combined whole-cell recordings with two-photon uncaging, calcium imaging and biophysical modeling to address this question. We observed and parameterized a form of behavioral timescale synaptic plasticity (BTSP) in layer 5 pyramidal neurons of mice prefrontal areas wherein the pairing of temporally separated pre- and postsynaptic events (0.5 s - 1 s), irrespective of order, induced synaptic potentiation. By imaging calcium in apical oblique dendrites, we reveal a short-term and associative plasticity of calcium dynamics (STAPCD) whose time-dependence mirrored the induction rules of BTSP. We identified a core set of molecular players that were essential for both STAPCD and BTSP and that, together with computational simulations, support a model wherein the dynamics of intracellular handling of calcium by the endoplasmic reticulum (ER) provides a latent memory trace of neural activity that instantiates synaptic weight updates upon a delayed instructive signal. By satisfying the requirements expected of eligibility traces, this mechanism accounts for how individual neurons can conjunctively bind cellular events that are separated by behaviorally relevant temporal delays, and thus offers a cellular model of reinforced learning.

neuroscience↗

bioRxiv2021

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↗