Biology subjects

Libedinsky, C.

Publications and source records attributed to Libedinsky, C..

13 recordsLinked to original sources

bioRxiv2026

Primate Hippocampus Reveals Distinct Rules for Associative Synaptic Plasticity

Long-term potentiation (LTP) is a key cellular mechanism underlying learning and memory, but its conservation across species remains unclear. Using nonhuman primates (NHPs), we examined hippocampal synaptic plasticity at Schaffer collateral- CA1 synapses. Theta-burst stimulation (TBS) reliably induced LTP in NHPs, comparable to rodents. However, unlike rodents, TBS in NHPs readily engaged synaptic tagging and capture (STC), indicating a lower threshold for associative plasticity. This was accompanied by increased expression of plasticity-related proteins, including PKM{zeta} and BDNF, suggesting enhanced recruitment of protein synthesisdependent stabilization mechanisms. These findings reveal a species-specific divergence in the molecular regulation of persistent synaptic plasticity and identify an evolutionary specialization in mechanisms supporting associative memory. Together, our results highlight limitations of rodent models in fully capturing human-relevant memory processes and underscore the importance of primate systems for translational neuroscience.

neuroscience↗

bioRxiv2025

Graphene nanoedge electronics for monolithic and chronic recording of local microcircuits at neuronal density

High-density single-unit recording among closely spaced neurons over long durations is crucial for understanding the cellular-level functional architecture of the brain. Existing brain probes, however, either laterally sample neurons far more sparsely than neuronal density in the same cortical layer or suffer from long-term instability due to electrode modification, preventing precise neuron-to-neuron interrogation in local microcircuits. Here, we report a monolithic graphene-edge probe (NeuroEdge) that achieves single-unit recording at neuronal density (16 electrodes within 100 {micro}m diameter). We fabricate NeuroEdge using self-assembled reduced graphene oxide nanoflakes to form an electrochemically active tip consisting of exposed graphene nanoedges and electrolyte-filling nanotunnels, achieving an ultralow specific impedance of 20 M{Omega} {micro}m2. In vivo experiments over 5 months demonstrate recording at a high signal-to-noise ratio (>20 dB) and reliable interrogation of neighboring neurons. We also show that NeuroEdge can record from a single auditory cortical layer and reveal heterogeneities in the acoustic frequency response and dynamic connectivity among neighboring neurons. NeuroEdge provides a tool for precisely interrogating local microcircuits at the density of neurons in the brain.

bioengineering↗

bioRxiv2025

Inferring Latent Behavioral Strategy from the Representational Geometry of Prefrontal Cortex Activity

Behavioral tasks can be solved employing various strategies. Sometimes, different strategies result in the same observable behavior, making them latent. In this study, we infer the latent behavioral strategy used by monkeys in a working memory updating task by comparing the representational geometry of two prefrontal regions --the lateral prefrontal cortex (LPFC) and the prearcuate cortex (PAC)--with that of recurrent neural network (RNN) models trained to solve the task using different strategies. We found that neural activity patterns in both LPFC and PAC align with only one of the proposed strategies, suggesting that monkeys employ this latent strategy to perform the task. These findings open avenues for investigating the processes that lead to strategy learning and the decision-making mechanisms that determine which strategies are chosen when multiple options are available.

neuroscience↗

bioRxiv2024

Mixed recurrent connectivity architecture in primate prefrontal cortex

The functional properties of a network depend on its connectivity, which includes the strength of its inputs and the strength of the connections between its units, or recurrent connectivity. Because we lack a detailed description of the recurrent connectivity in the lateral prefrontal cortex of primates, we developed an indirect method to estimate it. This method leverages the elevated noise correlation of mutually-connected units. To estimate the connectivity of prefrontal regions, we trained recurrent neural network models with varying percentages of bump attractor architecture and noise levels to match the noise correlation properties observed in two specific prefrontal regions: the dorsolateral prefrontal cortex and the frontal eye field. We found that models initialized with approximately 20% and 7.5% bump attractor architecture closely matched the noise correlation properties of the frontal eye field and dorsolateral prefrontal cortex, respectively. These findings suggest that the different percentages of bump attractor architecture may reflect distinct functional roles of these brain regions. Specifically, lower percentages of bump attractor units, associated with higher-dimensional representations, likely support more abstract neural representations in more anterior regions.

neuroscience↗

bioRxiv2024

Evidence of Activity-Silent Working Memory in Prefrontal Cortex

The lateral prefrontal cortex encodes working memory and motor preparation information, but the underlying neural mechanisms are debated. Recurrent neural network models relying on persistent neural activity have been challenged by the observation of periods of absent activity and information during memory maintenance, implying the existence of activity-silent mechanisms. To assess whether activity-silent mechanisms are needed for working memory maintenance, we recorded neural activity in macaque prefrontal cortex during a delayed-saccade task. We replicated the observation of periods of absent activity and decreased information between bursts of gamma power, but we show that these results are consistent with models that rely exclusively on persistent activity. However, an assessment of the length of periods with absent selective activity across the population revealed that activity-silent mechanisms are indeed needed to maintain memory information, although this is only evidenced in a small fraction of trials.

neuroscience↗

bioRxiv2023

Prefrontal Manifold Geometry Explains Reaction Time Variability

The stochastic drift-diffusion model proposes that the variability in reaction time is due to randomness during the accumulation of evidence until a decision threshold is reached. However, the neural mechanisms that explain both the randomness and implementation of the decision threshold in the model remain unclear. Here we address these questions using the dynamical systems approach to analyze primate frontal eye field activity and using microstimulation for causal manipulations. We built a mechanistic model in which signals associated with motor plans are bumped out of their attractor state by go-cue signals that emerge [~]60 ms after the go cue. The network then travels through a transition subspace towards a movement-initation subspace that emerges [~]35 ms before movement onset and implements the decision threshold. We postulate that the randomness in evidence accumulation, and hence in reaction times, is explained by the amplification of noise during movement preparation by the geometry of the frontal eye field manifold.

neuroscience↗

bioRxiv2020

Distinct lateral prefrontal regions are organized in an anterior-posterior functional gradient

The dorsolateral prefrontal cortex (DLPFC) is composed of multiple anatomically-defined regions involved in higher-order cognitive processes, including working memory and selective attention. It is organized in an anterior-posterior global gradient, where posterior regions track changes in the environment, while anterior regions support abstract neural representations. However, whether the global gradient results from a smooth gradient that spans regions, or an overall trend emerging from the organized arrangement of functionally distinct regions is unknown. Here, we provide evidence to support the latter, by analyzing single-neuron activity along the DLPFC of non-human primates trained to perform a memory-guided saccade task with an interfering distractor. Additionally, we show that the posterior DLPFC plays a particularly important role in working memory, in sharp contrast with the lack of task-related responses in the anterior DLPFC. Our results validate the functional boundaries between anatomically-defined DLPFC regions and highlight the heterogeneity of functional properties across regions.

neuroscience↗

bioRxiv2020

Lightweight Reinforcement Algorithms for autonomous, scalable intra-cortical Brain Machine Interfaces

Intra-cortical Brain Machine Interfaces (iBMIs) with wireless capability could scale the number of recording channels by integrating an intention decoder to reduce data rates. However, the need for frequent retraining due to neural signal non-stationarity is a big impediment. This paper presents an alternate paradigm of online reinforcement learning (RL) with a binary evaluative feedback in iBMIs to tackle this issue. This paradigm eliminates time-consuming calibration procedures. Instead, it relies on updating the model on a sequential sample-by-sample basis based on an instantaneous evaluative binary feedback signal. However, batch updates of weight in popular deep networks is very resource consuming and incompatible with constraints of an implant. In this work, using offline open-loop analysis on pre-recorded data, we show application of a simple RL algorithm - Banditron -in discrete-state iBMIs and compare it against previously reported state of the art RL algorithms - Hebbian RL, Attention gated RL, deep Q-learning. Owing to its simplistic single-layer architecture, Banditron is found to yield at least two orders of magnitude of reduction in power dissipation compared to state of the art RL algorithms. At the same time, post-hoc analysis performed on four pre-recorded experimental datasets procured from the motor cortex of two non-human primates performing joystick-based movement-related tasks indicate Banditron performing significantly better than state of the art RL algorithms by at least 5%, 10%, 7% and 7% in experiments 1, 2, 3 and 4 respectively. Furthermore, we propose a non-linear variant of Banditron, Banditron-RP, which gives an average improvement of 6%, 2% in decoding accuracy in experiments 2,4 respectively with only a moderate increase in power consumption.

bioengineering↗

bioRxiv2020

Towards Autonomous Intra-cortical Brain Machine Interfaces: Applying Bandit Algorithms for Online Reinforcement Learning

This paper presents application of Banditron - an online reinforcement learning algorithm (RL) in a discrete state intra-cortical Brain Machine Interface (iBMI) setting. We have analyzed two datasets from non-human primates (NHPs) - NHP A and NHP B each performing a 4-option discrete control task over a total of 8 days. Results show average improvements of {approx} 15%, 6% in NHP A and 15%, 21% in NHP B over state of the art algorithms - Hebbian Reinforcement Learning (HRL) and Attention Gated Reinforcement Learning (AGREL) respectively. Apart from yielding a superior decoding performance, Banditron is also the most computationally friendly as it requires two orders of magnitude less multiply-and-accumulate operations than HRL and AGREL. Furthermore, Banditron provides average improvements of at least 40%, 15% in NHPs A, B respectively compared to popularly employed supervised methods - LDA, SVM across test days. These results pave the way towards an alternate paradigm of temporally robust hardware friendly reinforcement learning based iBMIs.

bioengineering↗

bioRxiv2019

Sparse Ensemble Machine Learning to improve robustness of long-term decoding in iBMIs

This paper presents a novel sparse ensemble based machine learning approach to enhance robustness of intracortical Brain Machine Interfaces (iBMIs) in the face of non-stationary distribution of input neural data across time. Each classifier in the ensemble is trained on a randomly sampled (with replacement) set of input channels. These sparse connections ensure that with a high chance, few of the base classifiers should be less affected by the variations in some of the recording channels. We have tested the generality of this technique on different base classifiers - linear discriminant analysis (LDA), support vector machine (SVM), extreme learning machine (ELM) and multilayer perceptron (MLP). Results show decoding accuracy improvements of up to {approx} 21%, 13%, 19%, 10% in non-human primate (NHP) A and 7%, 9%, 7%, 9% in NHP B across test days while using the sparse ensemble approach over a single classifier model for LDA, SVM, ELM and MLP algorithms respectively. The technique also holds ground when the most informative electrode on the test day is dropped. Accordingly, improvements of up to {approx} 24%, 11%, 22%, 9% in NHP A and 14%, 19%, 7%, 28% in NHP B are obtained for LDA, SVM, ELM and MLP respectively.

bioengineering↗

bioRxiv2019

Towards Intelligent Intra-cortical BMI (i2BMI):Low-power Neuromorphic Decoders that outperform Kalman Filters

Fully implantable wireless intra-cortical Brain Machine Interfaces (iBMI) is one of the most promising next frontiers in the nascent field of neurotechnology. However, scaling the number of channels in such systems by another 10X is difficult due to power and bandwidth requirements of the wireless transmitter. One promising solution for that is to include more processing, up to the decoder, in the implant so that transmission data rate is reduced drastically. Earlier work on neuromorphic decoders only showed classification of discrete states. We present results for continuous state decoding using a low power neuromorphic decoder chip termed Spike-input Extreme Learning Machine (SELMA). We compared SELMA against state of the art Steady State Kalman Filter (SSKF) across two different datasets involving a total of 4 non-human primates (NHPs). Results show at least a 10% or more increase in the fraction of variance accounted for by SELMA over SSKF across the datasets. Furthermore, estimated energy consumption comparison shows SELMA consuming {approx} 9 nJ/update against SSKFs {approx} 7.4 nJ/update for an iBMI with a 10 degree of freedom control. Thus, SELMA yields better performance against SSKF with a marginal increase in energy consumption paving the way for reducing transmission data rates in future scaled BMI systems.

bioengineering↗

bioRxiv2019

Independent Activity Subspaces for Working Memory and Motor Preparation in the Lateral Prefrontal Cortex

The lateral prefrontal cortex is involved in the integration of multiple types of information, including working memory and motor preparation. However, it is not known how downstream regions can extract one type of information without interference from the others present in the network. Here we show that the lateral prefrontal cortex contains two independent low-dimensional subspaces: one that encodes working memory information, and another that encodes motor preparation information. These subspaces capture all the information about the target in the delay periods, and the information in both subspaces is reduced in error trials. A single population of neurons with mixed selectivity forms both subspaces, but the information is kept largely independent from each other. A bump attractor model with divisive normalization replicates the properties of the neural data. These results have implications for the neural mechanisms of cognitive flexibility and capacity limitations.

neuroscience↗

bioRxiv2019

Time-Invariant Working Memory Representations in the Presence of Code-Morphing in the Lateral Prefrontal Cortex

Endogenous processes allow the maintenance of working memories. These processes presumably involve prefrontal networks with strong recurrent connections. Distractors evoke a morphing of the population code, even when memories are stable. But it is unclear whether these dynamic population responses contain stable memory information. Here we show that dynamic prefrontal activity contains stable memory information, and the stability depends on parallel movement of trajectories associated with different memories in state space. We used an optimization algorithm to find a subspace with stable memory information. In correct trials the stability extended to periods that were not used to find the subspace, but in error trials the information and the stability were reduced. A bump attractor model was able to replicate these behaviors. The model provided predictions that could be confirmed with the neural data. We conclude that downstream regions could read memory information from a stable subspace.

neuroscience↗