Biology subjects

Hadjiabadi, D. H.

Publications and source records attributed to Hadjiabadi, D. H..

4 recordsLinked to original sources

bioRxiv2022

An inhibitory plasticity mechanism for world structure inference by hippocampal replay

Memory consolidation assimilates recent experiences into long-term memory. This process requires the replay of learned sequences, though the content of these sequences remains controversial. Recent work has shown that the statistics of replay deviate from those of experience: stimuli which are experientially salient may be either selected or suppressed. We find that this phenomenon can be explained parsimoniously and biologically plausibly by a Hebbian spike time-dependent plasticity rule at inhibitory synapses. Using spiking networks at three levels of abstraction-leaky integrate-and-fire, biophysically detailed, and abstract binary-we show that this rule enables efficient inference of a model of the structure of the world. We present analytical results that these replayed sequences converge to ground truth under a mathematical model of replay. Finally, we make specific predictions about the consequences of intact and perturbed inhibitory dynamics for network dynamics and cognition. Our work outlines a potential direct link between the synaptic and cognitive levels of memory consolidation, with implications for both normal learning and neurological disease.

neuroscience↗

bioRxiv2021

Towards a general framework for modeling large-scale biophysical neuronal networks: a full-scale computational model of the rat dentate gyrus

Large-scale computational models of the brain are necessary to accurately represent anatomical and functional variability in neuronal biophysics across brain regions and also to capture and study local and global interactions between neuronal populations on a behaviorally-relevant temporal scale. We present the methodology behind and an initial implementation of a novel open-source computational framework for construction, simulation, and analysis of models consisting of millions of neurons on high-performance computing systems, based on the NEURON and CoreNEURON simulators (Carnevale and Hines, 2006, Kumbhar et al., 2019). This framework uses the HDF5 data format and software library (HDF Group, 2021) and includes a data format for storing morphological, synaptic, and connectivity information of large neuronal network models, and an accompanying open-source software library that provides efficient, scalable parallel storage and MPI-based data movement capabilities. We outline our approaches for constructing detailed large-scale biophysical models with topographical connectivity and input stimuli, and present simulation results obtained with a full-scale model of the dentate gyrus constructed with our framework. The model generates sparse and spatially selective population activity that fits well with in-vivo experimental data. Moreover, our approach is fully general and can be applied to modeling other regions of the hippocampal formation in order to rapidly evaluate specific hypotheses about large-scale neural architectural features.

neuroscience↗

bioRxiv2020

Higher-order hub cells involved in feedforward motifs as critical factors in epileptic network instability

Neurological and psychiatric disorders are associated with pathological neural dynamics. The fundamental connectivity patterns of cell-cell communication networks that enable pathological dynamics to emerge remain unknown. We studied epileptic circuits using a newly developed integrated computational pipeline applied to cellular resolution functional imaging data. Control and preseizure neural dynamics in larval zebrafish and in chronically epileptic mice were captured using large-scale cellular-resolution calcium imaging. Biologically constrained effective connectivity modeling extracted the underlying cell-cell communication network. Novel analysis of the higher-order network structure revealed the existence of superhub cells that are unusually richly connected to the rest of the network through feedforward motifs. Instability in epileptic networks was causally linked to superhubs whose involvement in feedforward motifs critically enhanced downstream excitation. Disconnecting individual superhubs was significantly more effective in stabilizing epileptic networks compared to disconnecting hub cells defined traditionally by connection count. Collectively, these results predict a new, maximally selective and minimally invasive cellular target for seizure control. HighlightsO_LIHigher-order connectivity patterns of large-scale neuronal communication networks were studied in zebrafish and mice C_LIO_LIControl and epileptic networks were modeled from in vivo cellular resolution calcium imaging data C_LIO_LIRare superhub cells unusually richly connected to the rest of the network through higher-order feedforward motifs were identified C_LIO_LIDisconnecting single superhub neurons more effectively stabilized epileptic networks than targeting conventional hub cells defined by high connection count. C_LIO_LIThese data predict a maximally selective novel single cell target for minimally invasive seizure control C_LI

neuroscience↗

bioRxiv2018

Generating Realistic Morphologies of Neurons in Rodent Hippocampus with DGCAN

Dendritic size and branching patterns are important features of neural form and function. However, current computational models of neuronal networks use simplistic cylindrical geometries to mimic dendritic arborizations. One reason for this is that current methods to generate dendritic trees have rigid a priori constraints. To address this, a deep convolutional generative adversarial network (DCGAN) trained on images of rodent hippocampal granule and pyramidal dendritic trees. Image features learned by the network were used to generate realistic dendritic morphologies. Results show that DCGANs achieved greater stability* and high generalization, as quantified by kernel maximum mean discrepancy, when exposed to instance noise and/or label smoothing during training. Trained models successfully generated realistic morphologies for both neuron types, with high false positive rate reported by expert reviewers. Collectively, DCGANs offer a unique opportunity to advance the geometry of neural modeling, and, therefore, to propel our understanding of neuronal function.\n\n * A \"stable/stabilized DCGAN\", as mentioned throughout this work, is a DCGAN which was stable throughout training.

bioengineering↗