Biology subjects

Herikstad, R.

Publications and source records attributed to Herikstad, R..

6 recordsLinked to original sources

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↗

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↗

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↗