Biology subjects

Byrne, J. H.

Publications and source records attributed to Byrne, J. H..

8 recordsLinked to original sources

bioRxiv2026

Graph neural network approach for inferring neuronal connectivity

Understanding the connectivity of neuronal networks is essential for understanding brain function. Current anatomical mapping techniques, such as serial section microscopy and viral tracing, are resource-intensive and cannot capture the efficacy of connectivity. A complementary approach is to infer effective connectivity from single-neuron-resolution population activity. We developed a graph neural network (GNN)-based tool, CircuitGNN, that accurately infers neural effective connectivity from population activity. CircuitGNN accurately inferred the connectivity of several small circuits and detected both the strengthening and weakening of connections, such as those that occur during learning. CircuitGNN outperformed other methods when applied to simulated population activity generated by an invertebrate central pattern-generating circuit and a cortical microcircuit. CircuitGNN was evaluated on networks of varying size, recording duration, and proportion of hidden neurons. Although performance improved with longer recordings, CircuitGNN achieved high precision with recordings as short as 4 min, scaled to large circuits of up to ~750 neurons, and remained robust when 20% or 40% of neurons were hidden. These results establish CircuitGNN as an accurate and robust technique for inferring neural connectivity from activity.

neuroscience↗

bioRxiv2022

Enhancing associative learning in rats with a computationally designed training protocol

BackgroundLearning requires the activation of protein kinases with distinct temporal dynamics. In Aplysia, nonassociative learning can be enhanced by a computationally designed learning protocol with intertrial intervals (ITIs) that maximize the interaction between fast-activated protein kinase A (PKA) and slow-activated extracellular signal-regulated kinase (ERK). Whether a similar strategy can enhance associative learning in mammals is unknown. MethodsWe simulated 1,000 training protocols with varying ITIs to predict an optimal protocol based on empirical data for PKA and ERK dynamics in rat hippocampus. Adult male rats received the optimal protocol or control protocols in auditory fear conditioning and fear extinction experiments. Immunohistochemistry was performed to evaluate phosphorylated cAMP responsive element binding (pCREB) protein levels in brain regions implicated in fear acquisition. ResultsRats exposed to the optimal conditioning protocol with irregular ITIs exhibited impaired extinction memory acquisition within the session using a standard footshock intensity, and stronger fear memory retrieval and spontaneous recovery with a weaker footshock intensity, compared to rats that received massed or spaced conditioning protocols with fixed ITIs. Rats exposed to the optimal extinction protocol displayed improved extinction of contextual fear memory and reduced spontaneous recovery compared to rats that received standard extinction protocols. Moreover, the optimal conditioning protocol increased pCREB levels in the dentate gyrus (DG) of the dorsal hippocampus, suggesting enhanced induction of long-term potentiation. ConclusionThese findings demonstrate that a computational model-driven behavioral intervention can enhance associative learning in mammals, and may provide insight into strategies to improve cognition in humans.

neuroscience↗

bioRxiv2022

Defective synaptic plasticity in a model of Coffin-Lowry Syndrome is rescued by simultaneously targeting PKA and MAPK pathways

Empirical and computational methods were combined to examine whether individual or dual-drug treatments can restore the deficit in long-term synaptic facilitation (LTF) of the Aplysia sensorimotor synapse observed in a molecular model of Coffin-Lowry Syndrome (CLS). The model was produced by pharmacological inhibition of p90 ribosomal S6 kinase (RSK) activity. Simultaneous treatment with an activator of the mitogen-activated protein kinase (MAPK) isoform ERK and an activator of protein kinase A (PKA) resulted in enhanced phosphorylation of RSK and LTF to a greater extent than either drug alone and greater than their additive effects. Indeed, the combined drugs exerted synergistic effects on both RSK activation and LTF, fully restoring RSK phosphorylation and LTF. The extent of synergism appeared to depend on another MAPK isoform, p38 MAPK. Inhibition of p38 MAPK facilitated serotonin (5-HT)-induced RSK phosphorylation, indicating that p38 MAPK inhibits activation of RSK. Inhibition of p38 MAPK combined with activation of PKA synergistically activated RSK.

neuroscience↗

bioRxiv2022

Genesis of bursting activity: Role of inhibitory constraints on persistent Na+ currents

Some neurons express a plateau of spike activity greatly outlasting a stimulus, whereas others do not. This difference could be due to differential expression of ion channels (e.g., persistent Na+, and Ca2+) or similar expression of channels that differ in their properties. We compared three Aplysia neurons with varying capacity to generate plateau potentials: B51 with self-terminating plateaus, B64 with plateau potentials that do not self-terminate, and the regular spiking neuron B8. Our results indicate the three neurons expressed outward currents IA and ID, voltage-gated Ca2+ currents ICaL and ICaR, and persistent inward INaP. The most notable difference observed was a larger IA, ID, and IKCa currents in B8, and the plateau generating B64 did not express IKCa. Computational models suggest outward currents suppress and temporally constrain the plateau potential and that inward Ca2+ currents suppress plateau potentials when coupled with IKCa.

neuroscience↗

bioRxiv2021

Unsupervised functional neurocartography of the Aplysia buccal ganglion

A major limitation of large-scale neuronal recordings is the difficulty in locating homologous neurons in different subjects, referred to as the "correspondence" issue. This issue stems, at least in part, from the lack of a unique feature that unequivocally identifies each neuron. One promising approach to this problem is the functional neurocartography framework developed by Frady et al. (2016), in which neurons are identified by a semisupervised machine learning algorithm using a combination of multiple selected features. Here, the framework was adapted to the buccal ganglia of Aplysia. Multiple features were derived from neuronal activity during motor pattern generation, responses to peripheral nerve stimulation, and the spatial properties of each cell. The feature set was optimized based on its potential usefulness in discriminating neurons from each other, and then used to match putatively homologous neurons across subjects with the functional neurocartography software. An alternative matching method based on a cyclic matching algorithm was also developed, which allows for unsupervised extraction of groups of neurons and automated selection of high-quality matches. This improvement enabled unsupervised implementation of the machine learning algorithm, thereby enhancing scalability of the analysis. This study paves the way for investigating the roles of both well-characterized and previously uncharacterized neurons in Aplysia, and helps to adapt the neurocartography framework to other systems.

neuroscience↗

bioRxiv2021

Neuronal population activity dynamics reveal a low-dimensional signature of operant learning

Learning engages a high-dimensional neuronal population space spanning multiple brain regions. We identified a low-dimensional signature associated with operant conditioning, a ubiquitous form of learning in which animals learn from the consequences of behavior. Using single-neuron resolution voltage imaging, we identified two low-dimensional motor modules in the neuronal population underlying Aplysia feeding. Our findings point to a temporal shift in module recruitment as the primary signature of operant learning.

neuroscience↗

bioRxiv2021

Specific plasticity loci and their synergism mediate operant conditioning

Despite numerous studies examining the mechanisms of operant conditioning (OC), the diversity of plasticity loci and their synergism have not been examined sufficiently. In the well-characterized feeding neural circuit of Aplysia, appetitive OC increases neuronal excitability and electrical coupling among several neurons. Here we found OC decreased the intrinsic excitability of B4 and the strength of its inhibitory connection to a key decision-making neuron, B51. The OC-induced changes were specific without affecting the B4-to-B8 inhibitory connection or excitability of another neuron critical for feeding behavior, B8. A conductance-based circuit model indicated certain sites of plasticity mediated the OC phenotype more effectively and that plasticity loci acted synergistically. This synergy was specific in that only certain combinations of loci synergistically enhanced feeding. Taken together, these results suggest modifications of diverse loci work synergistically to mediate OC. Significance StatementThe diversity and synergism of plasticity loci mediating operant conditioning (OC) is poorly understood. Here we found that OC decreased the intrinsic excitability of a critical neuron mediating Aplysia feeding behavior and specifically reduced the strength of one of its inhibitory connections to a key decision-making neuron. A conductance-based computational model indicated that the known plasticity loci showed a surprising level of synergism to mediate the behavioral changes associated with OC. These results highlight the importance of understanding the diversity, specificity and synergy among different types of plasticity that encode memory. Also, because OC in Aplysia is mediated by dopamine (DA), the present study provides insights into specific and synergistic mechanisms of DA-mediated reinforcement of behaviors.

neuroscience↗

bioRxiv2020

Comparing Theories for the Maintenance of Late LTP and Long-Term Memory: Computational Analysis of the Roles of Kinase Feedback Pathways and Synaptic Reactivation

A fundamental problem in neuroscience is how memories are maintained from days to a lifetime, given turnover of proteins that underlie expression of long-term synaptic potentiation (LTP) or tag synapses as eligible for LTP. One likely solution relies on synaptic positive feedback loops, prominently including persistent activation of Ca2+/calmodulin kinase II (CaMKII) and self-activated synthesis of protein kinase M {zeta} (PKM{zeta}). Recent studies also suggest positive feedback based on recurrent synaptic reactivation within neuron assemblies, or engrams, is necessary to maintain memories. The relative importance of these feedback mechanisms is controversial. To explore the likelihood that each mechanism is necessary or sufficient to maintain memory, we simulated maintenance of LTP with a simplified model incorporating persistent kinase activation, synaptic tagging, and preferential reactivation of strong synapses, and analyzed implications of recent data. We simulated three model variants, each maintaining LTP with one feedback loop: autonomous, self-activated PKM{zeta} synthesis (model variant I); self-activated CamKII (model variant II); and recurrent reactivation of strengthened synapses (model variant III). Variant I requires and predicts that, for successful maintenance, PKM{zeta} must contribute to synaptic tagging. Variant II maintains LTP and suggests persistent CaMKII activation could maintain PKM{zeta} activity, a feedforward interaction not previously considered. However we note data challenging this feedback loop. In Variant III synaptic reactivation drives, and thus predicts, recurrent or persistent activity elevations of CamKII and other necessary kinases, plausibly contributing to empirically persistent elevation of PKM{zeta} levels. Reactivation is thus predicted to sustain recurrent rounds of synaptic tagging and incorporation of plasticity-related proteins. We also suggest (model variant IV) that synaptic reactivation and autonomous kinase activation could synergistically maintain LTP. We propose experiments that could discriminate these maintenance mechanisms.

neuroscience↗