Biology subjects

Milstein, A. D.

Publications and source records attributed to Milstein, A. D..

7 recordsLinked to original sources

bioRxiv2026

GIRK Channel Loss of Function Increases Dendritic Excitability in a Mouse Model of GNB1 Encephalopathy

GNB1 encephalopathy (GNB1-E) is a rare neurodevelopmental disorder associated with motor dysfunction, epilepsy and learning disability caused by mutations in the gene encoding the G protein subunit G{beta}1. Previous work has shown that altered G{beta}1 can disrupt activation of G-protein-coupled inwardly rectifying potassium (GIRK) channels, dysregulate neuronal excitability and cause seizures. However, the relevant upstream regulators of G{beta}1 and the consequences of GIRK dysfunction for neuronal synaptic, cellular and circuit function have not been characterized. Here we report that mice of both sexes carrying the deleterious p.I80T mutation in Gnb1 present features consistent with GNB1-E, including developmental delay, decreased locomotion and increased anxiety. Using histology, whole-cell patch-clamp electrophysiology and pharmacology in ex vivo brain slices, we find that hippocampal neurons in heterozygous Gnb1I80T/+ mice exhibit simplified dendritic morphologies, decreased synaptic inhibition mediated by metabotropic GABAB receptors and increased dendritic excitability. These phenotypes result in longer duration dendritic calcium spikes in response to synaptic afferent stimulation, an effect that is reversed by a specific activator of GIRK channels, ML297. Given the known roles of dendritic calcium spikes in driving burst firing and inducing synaptic plasticity, these findings suggest that targeting dendritic excitability has therapeutic potential to address both the seizure susceptibility and learning deficits associated with GNB1-E. Significance StatementGNB1 encephalopathy (GNB1-E) is a rare neurodevelopmental disorder associated with motor dysfunction, epilepsy and learning disability for which there are currently no mechanism-based treatments. Here we show that a pathogenic variant of the G protein subunit G{beta}1 impairs activation of neuronal G-protein-coupled inwardly rectifying potassium (GIRK) channels by inhibitory synaptic GABAB receptors. This leads to increased dendritic excitability and longer duration dendritic calcium spikes in mouse hippocampal neurons in response to stimulation of synaptic inputs. We find that this phenotype is reversed by a drug that activates GIRK channels, opening pathways to develop therapies for GNB1-E that specifically target dendritic excitability.

neuroscience↗

bioRxiv2026

The Importance of Synchrony in the Neural Control of Movement

Throughout the mammalian cortex, populations of neurons must work together to enact behaviors. While population recordings in motor cortex have revealed many aspects of when neurons fire during behaviors, limitations in causal experiments have made it difficult to identify which features of neural activity directly drive movements and which do not. Here, we explicitly test the principles of neural coding using high temporal precision multiphoton holographic optogenetics in the motor cortex. We show that activation of a small number (50-75) of Layer 2/3 excitatory neurons in the motor cortex is sufficient to drive movements. The efficacy of stimulation-driven movement depends on the state of the local circuit, and, to a lesser extent, the identity of which neurons are stimulated. We test whether evoked activity is acting through a rate code or a timing code by holding the firing rate and cell identities constant while varying the millisecond precise timing of activation. We find an unexpected and strong dependence on inter-cell synchrony when evoking movements. This neural synchrony recruits distinct patterns of recurrent excitation and inhibition. These findings provide evidence that the timing code, more so than the rate code, drives motor output.

neuroscience↗

bioRxiv2026

Hippocampal Place Cells with NMDARs Do Not Require Excitation and Inhibition to Be Reciprocally Tuned

In mouse hippocampal area CA1, excitatory pyramidal neurons referred to as "place cells" fire at specific locations in spatial environments during navigation. Many of the excitatory inputs to place cells are themselves spatially tuned, and prior work has shown that synaptic plasticity at those inputs strongly contributes to a selective increase in excitatory synaptic conductance when an animal is inside a cells "place field." It is less clear whether inhibitory inputs to place cells vary with spatial position. Recent studies have investigated whether place cells receive spatially tuned inhibitory conductances by recording place cell activity in vivo and using computational models to help interpret experimental perturbations. One prior study used inhibitory optogenetics to suppress inhibitory neuron firing rates and observed a uniform depolarization of place cells across spatial locations, supporting a model with spatially uniform synaptic inhibition. In apparent conflict, other studies used excitatory optogenetics to depolarize place cells and observed a selective increase in excitability within place fields, supporting a model with a spatially localized decrease in inhibition. However, the latter studies overlooked the contribution of voltage-gated NMDA-type glutamate receptors (NMDARs) to synaptic integration, which are expected to contribute to the balance of excitatory and inhibitory synaptic currents. Here we show that when NMDARs are included at excitatory synapses in simple CA1 place cell models, all experimentally-observed properties of place cells can be recapitulated regardless of whether inhibition increases, decreases, or remains constant inside a place field. Significance StatementThe hippocampus is a brain region required for the formation of new spatial and episodic memories (what happened where and when). Investigating the cellular and circuit mechanisms of memory recall could identify targets for therapies to combat memory decline associated with aging or neurodegeneration. Here we compare the results of computational models of the hippocampus to experimental recordings from mice to better understand the contribution of inhibitory neurons to the expression of spatial memories. We find that a special type of glutamate receptor, the NMDA receptor, helps to maintain the spatial selectivity of excitatory neurons in the hippocampus by counter-balancing fluctuations in the magnitude of inhibitory synaptic currents.

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bioRxiv2025

Cellular and subcellular specialization enables biology-constrained deep learning

Learning and memory in the brain depend on changes in the strengths of synaptic connections between neurons. While the molecular and cellular mechanisms of synaptic plasticity have been extensively studied experimentally, much of our understanding of how plasticity is organized across populations of neurons during task learning comes from training artificial neural networks (ANNs) using computational methods. However, the architectures of modern ANNs and the algorithms used to train them are not compatible with fundamental principles of neuroscience, leaving a gap in understanding how the brain coordinates learning across multiple layers of neural circuitry. Here we leverage recent experimental evidence to test an emergent theory that biological learning depends on specialization of distinct neuronal cell types and compartmentalized signaling within neuronal dendrites. We demonstrate that multilayer ANNs comprised of separate recurrently connected excitatory and inhibitory cell types, and neuronal units with separate soma and dendrite compartments, can be trained to accurately classify images using a fully biology-compatible deep learning algorithm called dendritic target propagation. By adhering to strict biological constraints, this model is able to provide unique insight into the biological mechanisms of learning and to make experimentally testable predictions regarding the roles of specific neuronal cell types in coordinating learning across different brain regions.

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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.

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bioRxiv2021

Offline memory replay in recurrent neuronal networks emerges from constraints on online dynamics

During spatial exploration, neural circuits in the hippocampus store memories of sequences of sensory events encountered in the environment. When sensory information is absent during "offline" resting periods, brief neuronal population bursts can "replay" sequences of activity that resemble bouts of sensory experience. These sequences can occur in either forward or reverse order, and can even include spatial trajectories that have not been experienced, but are consistent with the topology of the environment. The neural circuit mechanisms underlying this variable and flexible sequence generation are unknown. Here we demonstrate in a recurrent spiking network model of hippocampal area CA3 that experimental constraints on network dynamics such as population sparsity, stimulus selectivity, rhythmicity, and spike rate adaptation enable additional emergent properties, including variable offline memory replay. In an online stimulus-driven state, we observed the emergence of neuronal sequences that swept from representations of past to future stimuli on the timescale of the theta rhythm. In an offline state driven only by noise, the network generated both forward and reverse neuronal sequences, and recapitulated the experimental observation that offline memory replay events tend to include salient locations like the site of a reward. These results demonstrate that biological constraints on the dynamics of recurrent neural circuits are sufficient to enable memories of sensory events stored in the strengths of synaptic connections to be flexibly read out during rest and sleep, which is thought to be important for memory consolidation and planning of future behavior.

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bioRxiv2020

Bidirectional synaptic plasticity rapidly modifies hippocampal representations independent of correlated activity

Learning requires neural adaptations thought to be mediated by activity-dependent synaptic plasticity. A relatively non-standard form of synaptic plasticity driven by dendritic plateau potentials has been reported to underlie place field formation in hippocampal CA1 neurons. Here we found that this behavioral timescale synaptic plasticity (BTSP) can also reshape existing place fields via bidirectional synaptic weight changes that depend on the temporal proximity of plateau potentials to pre-existing place fields. When evoked near an existing place field, plateau potentials induced less synaptic potentiation and more depression, suggesting BTSP might depend inversely on postsynaptic activation. However, manipulations of place cell membrane potential and computational modeling indicated that this anti-correlation actually results from a dependence on current synaptic weight such that weak inputs potentiate and strong inputs depress. A network model implementing this bidirectional synaptic learning rule suggested that BTSP enables population activity, rather than pairwise neuronal correlations, to drive neural adaptations to experience.

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