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The model includes a detailed reconstructed morphology, models of the dynamics of eleven different ionic channels and a description of the intracellular Ca2+ concentration of a high-complexity, biophysically detailed model of thick-tufted layer 5 pyramidal cell. Due to the computational complexity of the original model, consisting of 196 compartments, a reduced morphology model was used, where passive parameters, ion channel conductances and parameters describing Ca2+ dynamics were properly fitted. The inhibitory cells in the network were based on a model of fast-spiking PV+ basket cells. These two single cell models were combined into a microcircuit network model consisting of 256 excitatory and 64 inhibitory neurons. Cells were connected via AMPA and NMDA receptor-mediated synaptic currents in the case of excitatory connections and GABAA receptor-mediated synaptic currents for inhibitory connections. Additionally, model cells received two types of input, Poissonian noise to all cells representing background activity in the cortex and rhythmic input representing the sensory input during auditory entrainment. A smaller percentage of inhibitory interneurons (35%) received no sensory input drive.", + "tags": [ + { + "id": 709, + "tag": "NetPyNE" + }, + { + "id": 926, + "tag": "Schizophrenia" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 3007, + "tag": "NetPyNE examples" + } + ], + "timestamp_created": "2024-01-25 17:58:08.863699+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/ChristophMetzner/ACnet", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2238": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2238, + "name": "Macaque auditory thalamocortical circuits model", + "repository_type": "github", + "summary": "We developed a biophysically-detailed model of the macaque auditory thalamocortical circuits, including primary auditory cortex (A1), medial geniculate body (MGB) and thalamic reticular nuclei (TRN), using the NEURON simulator and NetPyNE multiscale modeling tool. We simulated A1 as a cortical column with a depth of 2000 \u03bcm and 200 \u03bcm diameter, containing over 12k neurons and 30M synapses. Neuron densities, laminar locations, classes, morphology and biophysics, and connectivity at the long-range, local and dendritic scale were derived from published experimental data. The A1 model included 6 cortical layers and multiple populations of neurons consisting of 4 excitatory and 4 inhibitory types, and was reciprocally connected to the thalamus (MGB and TRN) mimicking anatomical connectivity. MGB included core and matrix thalamocortical neurons with layer-specific projection patterns to A1, and thalamic interneurons projecting locally. Auditory stimulus related inputs were simulated using phenomenological models of the cochlear auditory nerve and the inferior colliculus, which served as input to MGB. The model generated cell type and layer-specific firing rates consistent with overall ranges observed experimentally, and accurately simulated the corresponding local field potentials (LFPs), current source density (CSD), and electroencephalogram (EEG) signals. Laminar CSD patterns during spontaneous activity and responses to speech input were similar to those recorded experimentally. Physiological oscillations emerged spontaneously across frequency bands without external rhythmic inputs and were comparable to those recorded in vivo. We used the model to unravel the contributions from distinct cell type and layer-specific neuronal populations to oscillation events detected in CSD, and explore how these relate to the population firing patterns. Overall, the computational model provides a quantitative theoretical framework to integrate and interpret a wide range of experimental data in auditory circuits. It also constitutes a powerful tool to evaluate hypotheses and make predictions about the cellular and network mechanisms underlying common experimental measurements, including MUA, LFP and EEG signals.", + "tags": [ + { + "id": 3, + "tag": "netpyne" + }, + { + "id": 3007, + "tag": "NetPyNE examples" + }, + { + "id": 173, + "tag": "Macaque" + }, + { + "id": 3009, + "tag": "Thalamocortical model" + }, + { + "id": 3010, + "tag": "Multiscale model" + } + ], + "timestamp_created": "2024-01-25 18:01:52.077602+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2239": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2239, + "name": "CA1 microcircuit exhibiting phase-amplitude coupling", + "repository_type": "github", + "summary": "Phase amplitude coupling (PAC) between slow and fast oscillations is found throughout the brain and plays important functional roles. Its neural origin remains unclear. Experimental findings are often puzzling and sometimes contradictory. Most computational models rely on pairs of pacemaker neurons or neural populations tuned at different frequencies to produce PAC. Here, using a data-driven model of a hippocampal microcircuit, we demonstrate that PAC can naturally emerge from a single feedback mechanism involving an inhibitory and excitatory neuron population, which interplay to generate theta frequency periodic bursts of higher frequency gamma. The model suggests the conditions under which a CA1 microcircuit can operate to elicit theta-gamma PAC, and highlights the modulatory role of OLM and PVBC cells, recurrent connectivity, and short term synaptic plasticity. Surprisingly, the results suggest the experimentally testable prediction that the generation of the slow population oscillation requires the fast one and cannot occur without it.", + "tags": [ + { + "id": 709, + "tag": "NetPyNE" + }, + { + "id": 328, + "tag": "CA1" + }, + { + "id": 550, + "tag": "Hippocampus" + } + ], + "timestamp_created": "2024-01-25 18:06:40.248947+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/adampdp/plosCB-PAC", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2240": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2240, + "name": "Basal ganglia-thalamus-cortex circuit for motor control, decision & learning", + "repository_type": "github", + "summary": "Behaviour selection has been an active research topic for robotics, in particular in the field of human\u2013robot interaction. Fora robot to interact autonomously and effectively with humans, the coupling between techniques for human activityrecognition and robot behaviour selection is of paramount importance. However, most approaches to date consist ofdeterministic associations between the recognised activities and the robot behaviours, neglecting the uncertainty inherent tosequential predictions in real-time applications. In this paper, we address this gap by presenting an initial neuroroboticsmodel that embeds, in a simulated robot, computational models of parts of the mammalian brain that resembles neurophysiological aspects of the basal ganglia\u2013thalamus\u2013cortex (BG\u2013T\u2013C) circuit, coupled with human activity recognitiontechniques. A robotics simulation environment was developed for assessing the model, where a mobile robot accomplishedtasks by using behaviour selection in accordance with the activity being performed by the inhabitant of an intelligent home.Initial results revealed that the initial neurorobotics model is advantageous, especially considering the coupling betweenthe most accurate activity recognition approaches and the computational models of more complex animals.", + "tags": [ + { + "id": 709, + "tag": "NetPyNE" + }, + { + "id": 3007, + "tag": "NetPyNE examples" + }, + { + "id": 731, + "tag": "Basal ganglia" + }, + { + "id": 680, + "tag": "Thalamus" + }, + { + "id": 73, + "tag": "Cortex" + }, + { + "id": 2041, + "tag": "Motor control" + } + ], + "timestamp_created": "2024-01-25 18:18:25.592690+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/cmranieri/Bioinspired-behaviour", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2241": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2241, + "name": "NetPyNE implementation of the somatosensory thalamocortical circuits model", + "repository_type": "github", + "summary": "The primary somatosensory cortex (S1) of mammals is critically important in the perception of touch and related sensorimotor behaviors. In 2015, the Blue Brain Project (BBP) developed a groundbreaking rat S1 microcircuit simulation with over 31,000 neurons with 207 morpho-electrical neuron types, and 37 million synapses, incorporating anatomical and physiological information from a wide range of experimental studies. We have implemented this highly detailed and complex S1 model in NetPyNE, using the data available in the Neocortical Microcircuit Collaboration Portal. NetPyNE provides a Python high-level interface to NEURON and allows defining complicated multiscale models using an intuitive declarative standardized language. It also facilitates running parallel simulations, automates the optimization and exploration of parameters using supercomputers, and provides a wide range of built-in analysis functions. This will make the S1 model more accessible and simpler to scale, modify and extend in order to explore research questions or interconnect to other existing models. Despite some implementation differences, the NetPyNE model preserved the original cell morphologies, electrophysiological responses and spatial distribution for all 207 cell types; and the connectivity properties of all 1941 pathways, including synaptic dynamics and short-term plasticity (STP). The NetPyNE S1 simulations produced reasonable physiological firing rates and activity patterns across all populations. When STP was included, the network generated a 1 Hz oscillation comparable to the original model in vitro-like state. By then reducing the extracellular calcium concentration, the model reproduced the original S1 in vivo-like states with asynchronous activity. These results validate the original study using a new modeling tool. Simulated local field potentials (LFPs) exhibited realistic oscillatory patterns and features, including distance- and frequency-dependent attenuation. The model was extended by adding thalamic circuits, including 6 distinct thalamic populations with intrathalamic, thalamocortical (TC) and corticothalamic connectivity derived from experimental data. The thalamic model reproduced single known cell and circuit-level dynamics, including burst and tonic firing modes and oscillatory patterns, providing a more realistic input to cortex and enabling study of TC interactions. Overall, our work provides a widely accessible, datadriven and biophysically-detailed model of the somatosensory TC circuits that can be employed as a community tool for researchers to study neural dynamics, function and disease.", + "tags": [ + { + "id": 709, + "tag": "NetPyNE" + }, + { + "id": 3011, + "tag": "Thalamocortical circuit" + }, + { + "id": 3012, + "tag": "Somatosensory cortex" + } + ], + "timestamp_created": "2024-01-25 18:28:41.910825+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/suny-downstate-medical-center/S1_Thal_NetPyNE_Frontiers_2022", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2242": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2242, + "name": "Multiscale model of primary motor cortex (M1) circuits", + "repository_type": "github", + "summary": "Understanding cortical function requires studying multiple scales: molecular, cellular, circuit, and behavioral. We develop a multiscale, biophysically detailed model of mouse primary motor cortex (M1) with over 10,000 neurons and 30 million synapses. Neuron types, densities, spatial distributions, morphologies, biophysics, connectivity, and dendritic synapse locations are constrained by experimental data. The model includes long-range inputs from seven thalamic and cortical regions and noradrenergic inputs. Connectivity depends on cell class and cortical depth at sublaminar resolution. The model accurately predicts in vivo layer- and cell-type-specific responses (firing rates and LFP) associated with behavioral states (quiet wakefulness and movement) and experimental manipulations (noradrenaline receptor blockade and thalamus inactivation). We generate mechanistic hypotheses underlying the observed activity and analyzed low-dimensional population latent dynamics. This quantitative theoretical framework can be used to integrate and interpret M1 experimental data and sheds light on the cell-type-specific multiscale dynamics associated with several experimental conditions and behaviors.\nLink to Cell Reports paper: https://www.cell.com/cell-reports/fulltext/S2211-1247(23)00585-5", + "tags": [ + { + "id": 709, + "tag": "NetPyNE" + }, + { + "id": 144, + "tag": "Motor cortex" + }, + { + "id": 3013, + "tag": "M1" + }, + { + "id": 3010, + "tag": "Multiscale model" + } + ], + "timestamp_created": "2024-01-25 18:35:16.511676+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/suny-downstate-medical-center/M1_NetPyNE_CellReports_2023", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2243": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2243, + "name": "Model of Parkinson\u2019s Disease tuned to reproduce oscillatory behavior", + "repository_type": "github", + "summary": "In this work we propose a new biophysical computational model of brain regions relevant toParkinson\u2019s Disease (PD) based on local field potential data collected from the brain of marmoset monkeys.PD is a neurodegenerative disorder, linked to the death of dopaminergic neurons at the substantia nigrapars compacta, which affects the normal dynamics of the basal ganglia-thalamus-cortex (BG-T-C) neuronal circuit of the brain. Although there are multiple mechanisms underlying the disease, a complete description of those mechanisms and molecular pathogenesis are still missing, and there is still no cure. To address this gap, computational models that resemble neurobiological aspects found in animal models have been proposed.In our model, we performed a data-driven approach in which a set of biologically constrained parameters is optimised using differential evolution. Evolved models successfully resembled spectral signatures of local field potentials and single-neuron mean firing rates from healthy and parkinsonian marmoset brain data.This is the first computational model of PD based on simultaneous electrophysiological recordings from seven brain regions of Marmoset monkeys. Results indicate that the proposed model may facilitate the investigation of the mechanisms of PD and eventually support the development of new therapies. The DE method could also be applied to other computational neuroscience problems in which biological data is used to fit multi-scale models of brain circuits.", + "tags": [ + { + "id": 709, + "tag": "NetPyNE" + }, + { + "id": 3014, + "tag": "Parkinson's Disease" + }, + { + "id": 787, + "tag": "Oscillations" + } + ], + "timestamp_created": "2024-01-25 18:38:32.589082+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/cmranieri/MarmosetModel", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2244": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 2244, + "name": "Neurorobotics model of Parkinson's Disease", + "repository_type": "github", + "summary": "In this work, we present the first steps toward the creation of a new neurorobotics model of Parkinson's Disease (PD) that embeds, for the first time in a real robot, a well-established computational model of PD. PD mostly affects the modulation of movement in humans. The number of people suffering from this neurodegenerative disease is set to double in the next 15 years and there is still no cure. With the new model we were capable to further explore the dynamics of the disease using a humanoid robot. Results show that the embedded model under both conditions, healthy and parkinsonian, was capable of performing a simple behavioural task with different levels of motor disturbance. We believe that this neurorobotics model is a stepping stone to the development of more sophisticated models that could eventually test and inform new PD therapies and help to reduce and replace animals in research.", + "tags": [ + { + "id": 709, + "tag": "NetPyNE" + }, + { + "id": 3015, + "tag": "Parkinson's disease" + }, + { + "id": 3016, + "tag": "Neurorobotics" + } + ], + "timestamp_created": "2024-01-25 18:40:33.356258+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/jhielson/Initial_Neurorobotics_Model_of_PD", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2245": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 2245, + "name": "Potjans & Diesmann cortical microcircuit model in NetPyNE", + "repository_type": "github", + "summary": "The Potjans-Diesmann cortical microcircuit model is a widely used model originally implemented in NEST. It is a full-scale spiking network model of 1 mm\u00b2 of cortex, comprising four cortical layers, each containing an excitatory and an inhibitory population, with close to 80,000 point neurons. We reimplemented the model using NetPyNE and reproduced the findings of the original publication. We also implemented a method for scaling the network size that preserves firing rates and other statistics. Our new implementation enabled the use of more detailed neuron models with multicompartmental morphologies and multiple biophysically realistic ion channels. This opens the model to new research, including the study of dendritic processing, the influence of individual channel parameters, the relation to local field potentials (LFPs), and other multiscale interactions. See publication [11].", + "tags": [ + { + "id": 709, + "tag": "NetPyNE" + } + ], + "timestamp_created": "2024-01-25 18:44:27.751936+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/suny-downstate-medical-center/PDCM_NetPyNE", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2246": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 2246, + "name": "Spiking neuronal network model of visual-motor cortex playing a virtual racket-ball game", + "repository_type": "github", + "summary": "Recent models of spiking neuronal networks have been trained to perform behaviors in static environments using a variety of learning rules, with varying degrees of biological realism. Most of these models have not been tested in dynamic visual environments where models must make predictions on future states and adjust their behavior accordingly. The models using these learning rules are often treated as black boxes, with little analysis on circuit architectures and learning mechanisms supporting optimal performance. Here we developed visual/motor spiking neuronal network models and trained them to play a virtual racket-ball game using several reinforcement learning algorithms inspired by the dopaminergic reward system. We systematically investigated how different architectures and circuit-motifs (feed-forward, recurrent, feedback) contributed to learning and performance. We also developed a new biologically-inspired learning rule that significantly enhanced performance, while reducing training time. Our models included visual areas encoding game inputs and relaying the information to motor areas, which used this information to learn to move the racket to hit the ball. Neurons in the early visual area relayed information encoding object location and motion direction across the network. Neuronal association areas encoded spatial relationships between objects in the visual scene. Motor populations received inputs from visual and association areas representing the dorsal pathway. Two populations of motor neurons generated commands to move the racket up or down. Model-generated actions updated the environment and triggered reward or punishment signals that adjusted synaptic weights so that the models could learn which actions led to reward. Here we demonstrate that our biologically-plausible learning rules were effective in training spiking neuronal network models to solve problems in dynamic environments. We used our models to dissect the circuit architectures and learning rules most effective for learning. Our model shows that learning mechanisms involving different neural circuits produce similar performance in sensory-motor tasks. In biological networks, all learning mechanisms may complement one another, accelerating the learning capabilities of animals. Furthermore, this also highlights the resilience and redundancy in biological systems.", + "tags": [ + { + "id": 709, + "tag": "NetPyNE" + } + ], + "timestamp_created": "2024-01-25 18:46:16.143537+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/NathanKlineInstitute/SMARTAgent", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2247": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 2247, + "name": "Spiking neuronal networks performing motor control", + "repository_type": "github", + "summary": "Artificial neural networks (ANNs) have been successfully trained to perform a wide range of sensory-motor behaviors. In contrast, the performance of spiking neuronal network (SNN) models trained to perform similar behaviors remains relatively suboptimal. In this work, we aimed to push the field of SNNs forward by exploring the potential of different learning mechanisms to achieve optimal performance. We trained SNNs to solve the CartPole reinforcement learning (RL) control problem using two learning mechanisms operating at different timescales: (1) spike-timing-dependent reinforcement learning (STDP-RL) and (2) evolutionary strategy (EVOL). Though the role of STDP RL in biological systems is well established, several other mechanisms, though not fully understood, work in concert during learning in vivo. Recreating accurate models that capture the interaction of STDP-RL with these diverse learning mechanisms is extremely difficult. EVOL is an alternative method and has been successfully used in many studies to fit model neural responsiveness to electrophysiological recordings and, in some cases, for classification problems. One advantage of EVOL is that it may not need to capture all interacting components of synaptic plasticity and thus provides a better alternative to STDP-RL. Here, we compared the performance of each algorithm after training, which revealed EVOL as a powerful method for training SNNs to perform sensory-motor behaviors. Our modeling opens up new capabilities for SNNs in RL and could serve as a testbed for neurobiologists aiming to understand multi-timescale learning mechanisms and dynamics in neuronal circuits.", + "tags": [ + { + "id": 709, + "tag": "NetPyNE" + } + ], + "timestamp_created": "2024-01-25 18:50:48.992153+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/NathanKlineInstitute/netpyne-STDP", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2248": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 2248, + "name": "Thalamocortical network with epilepsy", + "repository_type": "github", + "summary": "Thalamocortical computer model. NetPyNE version of a model of a thalamocortical network with epilepsy. Original model and publication: https://modeldb.yale.edu/234233 (original model by Andrew Knox). The model is comprised of cortical pyramidal, cortical inhibitory, thalamocortical relay, and thalamic reticular single-compartment neurons, implemented with Hodgkin-Huxley model ion channels and connected by AMPA, GABAA, and GABAB synapses.", + "tags": [ + { + "id": 709, + "tag": "NetPyNE" + }, + { + "id": 572, + "tag": "Epilepsy" + } + ], + "timestamp_created": "2024-01-25 18:53:08.832564+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/rtekin/myKnoxRepo", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2249": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2249, + "name": "M1-channelopthies-OSB", + "repository_type": "github", + "summary": "", + "tags": [], + "timestamp_created": "2024-01-26 15:20:50.182162+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/suny-downstate-medical-center/M1-channelopthies-OSB", + "user": { + "email": "salvadordura@gmail.com", + "first_name": "Salvador", + "id": "aab08e79-1830-4b2c-8116-7565205fd9d2", + "last_name": "Dura-Bernal", + "username": "salvadordura@gmail.com" + }, + "user_id": "aab08e79-1830-4b2c-8116-7565205fd9d2" } } } \ No newline at end of file