@article {157481, title = {Mechanisms and implications of high depolarization baseline offsets in conductance-based neuronal models.}, journal = {J Neurophysiol}, year = {2025}, month = {2025 May 19}, abstract = {

Somatic step-current injection is commonly used to characterize the electrophysiological properties of neurons. Many neuronal types show a large depolarization baseline offset (DBLO), which is defined as the positive difference between the minimum membrane potential during action potential trains and resting. We used stochastic parameter search in experimentally constrained conductance-based models to show that four key factors together account for high DBLO: Liquid Junction Potential correction, high backpropagating passive charges during the repolarization phase of an action potential, fast potassium delayed rectifier kinetics, and appropriate transient sodium current kinetics. Several plausible mechanisms for DBLO, such as Ohmic depolarization due to current input or low-pass filtering by the membrane, fail to explain the effect, and many published conductance-based models do not correctly manifest high DBLO. Finally, physiological levels of DBLO constrain ion channel levels and kinetics, and are linked to cellular processes such as bistable firing, spikelets, and calcium influx.

}, issn = {1522-1598}, doi = {10.1152/jn.00617.2024}, url = {https://journals.physiology.org/doi/abs/10.1152/jn.00617.2024?url_ver=Z39.88-2003\&rfr_id=ori:rid:crossref.org\&rfr_dat=cr_pub\%20\%200pubmed}, author = {Kumar, Anal and Shahul, Anzal K and Bhalla, Upinder Singh} } @article {136527, title = {Understanding molecular signaling cascades in neural disease using multi-resolution models.}, journal = {Curr Opin Neurobiol}, volume = {83}, year = {2023}, month = {2023 Nov 14}, pages = {102808}, abstract = {

If the genome defines the program for the operations of a cell, signaling networks execute it. These cascades of chemical, cell-biological, structural, and trafficking events span milliseconds (e.g., synaptic release) to potentially a lifetime (e.g., stabilization of dendritic spines). In principle almost every aspect of neuronal function, particularly at the synapse, depends on signaling. Thus dysfunction of these cascades, whether through mutations, local dysregulation, or infection, leads to disease. The sheer complexity of these pathways is matched by the range of diseases and the diversity of their phenotypes. In this review, we discuss how to build computational models, how these models are essential to tackle this complexity, and the benefits of using families of models at different levels of detail to understand signaling in health and disease.

}, issn = {1873-6882}, doi = {10.1016/j.conb.2023.102808}, author = {Viswan, Nisha Ann and Bhalla, Upinder Singh} } @article {115011, title = {Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows.}, journal = {Elife}, volume = {11}, year = {2022}, month = {2022 07 06}, abstract = {

Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-driven modeling brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the system works or why certain phenomena are observed. Data-driven modeling, on the other hand, follows a more unbiased approach, with model construction informed by the computationally intensive use of data. At the same time, researchers employ models at different biological scales and at different levels of abstraction. Combining these models while validating them against experimental data increases understanding of the multiscale brain. However, a lack of interoperability, transparency, and reusability of both models and the workflows used to construct them creates barriers for the integration of models representing different biological scales and built using different modeling philosophies. We argue that the same imperatives that drive resources and policy for data - such as the FAIR (Findable, Accessible, Interoperable, Reusable) principles - also support the integration of different modeling approaches. The FAIR principles require that data be shared in formats that are Findable, Accessible, Interoperable, and Reusable. Applying these principles to models and modeling workflows, as well as the data used to constrain and validate them, would allow researchers to find, reuse, question, validate, and extend published models, regardless of whether they are implemented phenomenologically or mechanistically, as a few equations or as a multiscale, hierarchical system. To illustrate these ideas, we use a classical synaptic plasticity model, the Bienenstock-Cooper-Munro rule, as an example due to its long history, different levels of abstraction, and implementation at many scales.

}, keywords = {Neurosciences, Workflow}, issn = {2050-084X}, doi = {10.7554/eLife.69013}, author = {Eriksson, Olivia and Bhalla, Upinder Singh and Blackwell, Kim T and Crook, Sharon M and Keller, Daniel and Kramer, Andrei and Linne, Marja-Leena and Saudargiene, Ausra and Wade, Rebecca C and Hellgren Kotaleski, Jeanette} } @article {99941, title = {Adult brain neurons require continual expression of the schizophrenia-risk gene Tcf4 for structural and functional integrity.}, journal = {Transl Psychiatry}, volume = {11}, year = {2021}, month = {2021 Sep 25}, pages = {494}, abstract = {

The schizophrenia-risk gene Tcf4 has been widely studied in the context of brain development using mouse models of haploinsufficiency, in utero knockdown and embryonic deletion. However, Tcf4 continues to be abundantly expressed in adult brain neurons where its functions remain unknown. Given the importance of Tcf4 in psychiatric diseases, we investigated its role in adult neurons using cell-specific deletion and genetic tracing in adult animals. Acute loss of Tcf4 in adult excitatory neurons in vivo caused hyperexcitability and increased dendritic complexity of neurons, effects that were distinct from previously observed effects in embryonic-deficiency models. Interestingly, transcriptomic analysis of genetically traced adult-deleted FACS-sorted Tcf4-knockout neurons revealed that Tcf4 targets in adult neurons are distinct from those in the embryonic brain. Meta-analysis of the adult-deleted neuronal transcriptome from our study with the existing datasets of embryonic Tcf4 deficiencies revealed plasma membrane and ciliary genes to underlie Tcf4-mediated structure-function regulation specifically in adult neurons. The profound changes both in the structure and excitability of adult neurons upon acute loss of Tcf4 indicates that proactive regulation of membrane-related processes underlies the functional and structural integrity of adult neurons. These findings not only provide insights for the functional relevance of continual expression of a psychiatric disease-risk gene in the adult brain but also identify previously unappreciated gene networks underpinning mature neuronal regulation during the adult lifespan.

}, issn = {2158-3188}, doi = {10.1038/s41398-021-01618-x}, author = {Sarkar, Dipannita and Shariq, Mohammad and Dwivedi, Deepanjali and Krishnan, Nirmal and Naumann, Ronald and Bhalla, Upinder Singh and Ghosh, HS} } @article {56659, title = {Precise excitation-inhibition balance controls gain and timing in the hippocampus.}, journal = {Elife}, volume = {8}, year = {2019}, month = {2019 Apr 25}, abstract = {

Excitation-inhibition (EI) balance controls excitability, dynamic range, and input gating in many brain circuits. Subsets of synaptic input can be selected or {\textquoteright}gated{\textquoteright} by precise modulation of finely tuned EI balance, but assessing the granularity of EI balance requires combinatorial analysis of excitatory and inhibitory inputs. Using patterned optogenetic stimulation of mouse hippocampal CA3 neurons, we show that hundreds of unique CA3 input combinations recruit excitation and inhibition with a nearly identical ratio, demonstrating precise EI balance at the hippocampus. Crucially, the delay between excitation and inhibition decreases as excitatory input increases from a few synapses to tens of synapses. This creates a dynamic millisecond-range window for postsynaptic excitation, controlling membrane depolarization amplitude and timing via subthreshold divisive normalization. We suggest that this combination of precise EI balance and dynamic EI delays forms a general mechanism for millisecond-range input gating and subthreshold gain control in feedforward networks.

}, issn = {2050-084X}, doi = {10.7554/eLife.43415}, author = {Bhatia, Aanchal and Moza, Sahil and Bhalla, Upinder Singh} } @article {46414, title = {Subunit exchange enhances information retention by CaMKII in dendritic spines.}, journal = {Elife}, volume = {7}, year = {2018}, month = {2018 Nov 12}, abstract = {

Molecular bistables are strong candidates for long-term information storage, for example, in synaptic plasticity. Calcium/calmodulin dependent protein Kinase II (CaMKII) is a highly expressed synaptic protein which has been proposed to form a molecular bistable switch capable of maintaining its state for years despite protein turnover and stochastic noise. It has recently been shown that CaMKII holoenzymes exchange subunits among themselves. Here we used computational methods to analyze the effect of subunit exchange on the CaMKII pathway in the presence of diffusion in two different micro-environments, the post synaptic density (PSD) and spine cytosol. We show that CaMKII exhibits multiple timescales of activity due to subunit exchange. Further, subunit exchange enhances information retention by CaMKII both by improving the stability of its switching in the PSD, and by slowing the decay of its activity in the spine cytosol. The existence of diverse timescales in the synapse has important theoretical implications for memory storage in networks.

}, issn = {2050-084X}, doi = {10.7554/eLife.41412}, author = {Singh, Dilawar and Bhalla, Upinder Singh} } @article {16562, title = {Synaptic input sequence discrimination on behavioral time-scales mediated by reaction-diffusion chemistry in dendrites.}, journal = {Elife}, volume = {6}, year = {2017}, month = {2017 Apr 19}, abstract = {

Sequences of events are ubiquitous in sensory, motor, and cognitive function. Key computational operations, including pattern recognition, event prediction, and plasticity, involve neural discrimination of spatio-temporal sequences. Here we show that synaptically-driven reaction-diffusion pathways on dendrites can perform sequence discrimination on behaviorally relevant time-scales. We used abstract signaling models to show that selectivity arises when inputs at successive locations are aligned with, and amplified by, propagating chemical waves triggered by previous inputs. We incorporated biological detail using sequential synaptic input onto spines in morphologically, electrically, and chemically detailed pyramidal neuronal models based on rat data. Again, sequences were recognized, and local channel modulation downstream of putative sequence-triggered signaling could elicit changes in neuronal firing. We predict that dendritic sequence-recognition zones occupy 5 to 30 microns and recognize time-intervals of 0.2 to 5s. We suggest that this mechanism provides highly parallel and selective neural computation in a functionally important time range.

}, issn = {2050-084X}, doi = {10.7554/eLife.25827}, author = {Bhalla, Upinder Singh} }