Biology subjects

Malerba, P.

Publications and source records attributed to Malerba, P..

4 recordsLinked to original sources

bioRxiv2018

Large time step discrete-time modeling of sharp wave activity in hippocampal area CA3.

Reduced models of neuronal spiking activity simulated with a fixed integration time step are frequently used in studies of spatio-temporal dynamics of neurobiological networks. The choice of fixed time step integration provides computational simplicity and efficiency, especially in cases dealing with large number of neurons and synapses operating at a different level of activity across the population at any given time. A network model tuned to generate a particular type of oscillations or wave patterns is sensitive to the intrinsic properties of neurons and synapses and, therefore, commonly susceptible to changes in the time step of integration. In this study, we analyzed a model of sharp-wave activity in the network of hippocampal area CA3, to examine how an increase of the integration time step affects network behavior and to propose adjustments of intrinsic properties of neurons and synapses that help minimize or remove the damage caused by the time step increase.\n\nHighlightsO_LISpiking models of neural network activity are sensitive to the integration step\nC_LIO_LILarger integration time steps are preferable in simulating large networks\nC_LIO_LICase study of CA3 sharp waves shows time step increase damages network dynamics\nC_LIO_LINeuronal and synaptic parameters adjustments rescue the dynamics at large time step1\nC_LI

neuroscience↗

bioRxiv2017

Selective Recruitment of Cortical Neurons by Electrical Stimulation

Despite its critical importance in experimental and clinical neuroscience, at present there is no systematic method to predict which neural elements will be activated by a given stimulation regime. Here we develop a novel approach to model the effect of cortical stimulation on spiking probability of neurons in a volume of tissue, by applying an analytical estimate of stimulation-induced activation of different cell types across cortical layers. We utilize the morphology and properties of axonal arborization profiles obtained from publicly available anatomical reconstructions of the twelve main categories of neocortical neurons to derive the dependence of activation probability on cell type, layer and distance from the source. We then propagate this activity through the local network incorporating connectivity, synaptic and cellular properties. Our work predicts that (a) intracranial cortical stimulation induces selective activation across cell types and layers; (b) superficial anodal stimulation is more effective than cathodal at cell activation; (c) cortical surface stimulation focally activates layer I axons, and (d) an optimal stimulation intensity exists capable of eliciting cell activation lasting beyond the end of stimulation.

neuroscience↗

bioRxiv2017

Learning-induced sequence reactivation during sharp-wave ripples: a computational study

During sleep, memories formed during the day are consolidated in a dialogue between cortex and hippocampus. The reactivation of specific neural activity patterns - replay - during sleep has been observed in both structures and is hypothesized to represent a neuronal substrate of consolidation. In the hippocampus, replay happens during sharp wave - ripples (SWR), short bouts of excitatory activity in area CA3 which induce high frequency oscillations in area CA1. In particular, recordings of hippocampal cells which spike at a specific location ( place cells) show that recently learned trajectories are reactivated during SWR in the following sleep SWR. Despite the importance of sleep replay, its underlying neural mechanisms are still poorly understood.\n\nWe developed a model of SWR activity, to study the effects of learning-induced synaptic changes on spontaneous sequence reactivation during SWR. The model implemented a paradigm including three epochs: Pre-sleep, learning and Post-sleep activity. We first tested the effects of learning on the hippocampal network activity through changes in a minimal number of synapses connecting selected pyramidal cells. We then introduced an explicit trajectory-learning task to the model, to obtain behavior-induced synaptic changes. The model revealed that the recently learned trajectory reactivates during sleep more often than other trajectories in the training field. The study predicts that the gain of reactivation rate during sleep following vs sleep preceding learning for a trained sequence of pyramidal cells depends on Pre-sleep activation of the same sequence, and on the amount of trajectory repetitions included in the training phase.

neuroscience↗

bioRxiv2017

Defining the synaptic mechanisms that tune CA3-CA1 reactivation during sharp-wave ripples.

During non-REM sleep, memory consolidation is driven by a dialogue between hippocampus and cortex involving the reactivation of specific neural activity sequences ( replay). In the hippocampus, replay occurs during sharp-wave ripples (SWRs), short bouts of excitatory activity in area CA3 which induce high frequency oscillations in the inhibitory interneurons of area CA1. Despite growing evidence for the functional importance of replay, its neural mechanisms remain poorly understood. Here, we develop a novel theoretical model of hippocampal spiking during SWRs. In our model, noise-induced activation of CA3 pyramidal cells triggered an excitatory cascade capable of inducing local ripple events in CA1. Ripples occurred stochastically, with Schaffer Collaterals driving their coordination, so that localized sharp waves in CA3 produced consistently localized CA1 ripples. In agreement with experimental data, the majority of pyramidal cells in the model showed low reactivation probabilities across SWRs. We found, however, that a subpopulation of pyramidal cells had high reactivation probabilities, which derived from fine-tuning of the network connectivity. In particular, the excitatory inputs along synaptic pathway(s) converging onto cells and cell pairs controlled emergent single cell and cell pair reactivation, with inhibitory inputs and intrinsic cell excitability playing differential roles in CA3 vs. CA1. Our model predicts (1) that the hippocampal network structure driving the emergence of SWR is also able to generate and modulate reactivation, (2) inhibition plays a particularly prominent role in CA3 reactivation and (3) CA1 sequence reactivation is reliant on CA3-CA1 interactions rather than an intrinsic CA1 process.

neuroscience↗