Profile Url: saray-soldado-magraner
Researcher at Department of Neurobiology, University of California
Journal of Neurophysiology, 2019-11-13
Unlike synaptic strength, intrinsic excitability is assumed to be a stable property of neurons. For example, learning of somatic conductances is generally not incorporated into computational models, and the discharge pattern of neurons in response to test stimuli is frequently used as a basis for phenotypic classification. However, it is increasingly evident that signal processing properties of neurons are more generally plastic on the timescale of minutes. Here we demonstrate that the intrinsic firing patterns of CA3 neurons of the rat hippocampus in vitro undergo rapid long-term plasticity in response to a few minutes of only subthreshold synaptic conditioning. This plasticity on the spike-timing could also be induced by intrasomatic injection of subthreshold depolarizing pulses and was blocked by kinase inhibitors, indicating that discharge dynamics are modulated locally. Cluster analysis of firing patterns before and after conditioning revealed systematic transitions towards adapting and intrinsic burst behaviours, irrespective of the patterns initially exhibited by the cells. We used a conductance-based model to decide appropriate pharmacological blockade, and found that the observed transitions are likely due to recruitment of calcium and M-type potassium conductances. We conclude that CA3 neurons adapt their conductance profile to the subthreshold activity of their input, so that their intrinsic firing pattern is not a static signature, but rather a reflection of their history of subthreshold activity. In this way, recurrent output from CA3 neurons may collectively shape the temporal dynamics of their embedding circuits. New & Noteworthy Despite being widely conserved across the animal phyla, it is still a mystery why nerve cells present diverse discharge dynamics upon somatic step currents. Adding a new timing dimension to the intrinsic plasticity literature, here we show that CA3 neurons rapidly adapt through the space of known firing patterns in response to the subthreshold signals that they receive from their embedding circuit. This result implies that CA3 neurons collectively adjust their network processing to the temporal statistics of their circuit.
Self-sustaining dynamics maintained through recurrent connections are of fundamental importance to cortical function. We show that Up-states--an example of self-sustained network dynamics--autonomously emerge in cortical circuits across three weeks of ex vivo development, establishing the presence of unsupervised synaptic learning rules that lead to globally stable emergent dynamics. Computational models of excitatory-inhibitory networks have established that four sets of weights (WE[<-]E, WE[<-]I, WI[<-]E, WI[<-]I) cooperate to generate stable self-sustained dynamics, but have not addressed how a family of learning rules can operate in parallel at all four weight classes to achieve self-sustained inhibition-stabilized regimes. Using numerical and analytical methods we show that standard homeostatic rules cannot account for the emergence of self-sustained dynamics due to the paradoxical effect. We derived a novel family of homeostatic learning rules that operate in parallel at all four synaptic classes, which robustly lead to the emergence of Up-states and balanced excitation-inhibition.