Biology subjects

Levenstein, D.

Publications and source records attributed to Levenstein, D..

9 recordsLinked to original sources

bioRxiv2026

Environmental complexity reveals memory-guided search as the locus of learning in prey capture

Ethological behavior requires the integration of perception, memory, and decision-making, yet standard laboratory tasks are often too simplified to recruit these processes jointly. Here, mice that were expert hunters in a bare arena were challenged to capture live insect prey among objects that occlude vision and afford opportunities to hide. This revealed an axis of learning absent in an empty arena: mice improved not by refining pursuit but by reorganizing how they searched for concealed prey. An agent-based model showed that short-term memory and learning of object-specific values are sufficient to account for the behavior demonstrated by mice. A compact ethogram of object-directed actions revealed that animals selectively acquired the interactions most likely to expose prey. These results show that environmental complexity recruits memory- and value-guided search strategies. Reproducible with inexpensive materials, the paradigm and its open-source analyses offer a sensitive read-out of cognitive processes largely unobservable in conventional assays.

neuroscience↗

bioRxiv2024

Topography of putative bidirectional interaction between hippocampal sharp wave ripples and neocortical slow oscillations

Systems consolidation relies on coordination between hippocampal sharp-wave ripples (SWRs) and neocortical UP/DOWN states during sleep. However, whether this coupling exists across neocortex and the mechanisms enabling it remain unknown. By combining electrophysiology in mouse hippocampus (HPC) and retrosplenial cortex (RSC) with widefield imaging of dorsal neocortex, we found spatially and temporally precise bidirectional hippocampo-neocortical interaction. HPC multi-unit activity and SWR probability was correlated with UP/DOWN states in mouse default mode network, with highest modulation by RSC in deep sleep. Further, some SWRs were preceded by the high rebound excitation accompanying DMN DOWN[->]UP transitions, while large-amplitude SWRs were often followed by DOWN states originating in RSC. We explain these electrophysiological results with a model in which HPC and RSC are weakly coupled excitable systems capable of bi-directional perturbation and suggest RSC may act as a gateway through which SWRs can perturb downstream cortical regions via cortico-cortical propagation of DOWN states.

neuroscience↗

bioRxiv2024

Sequential predictive learning is a unifying theory for hippocampal representation and replay

The mammalian hippocampus contains a cognitive map that represents an animals position in the environment 1 and generates offline "replay" 2,3 for the purposes of recall 4, planning 5,6, and forming long term memories 7. Recently, its been found that artificial neural networks trained to predict sensory inputs develop spatially tuned cells 8, aligning with predictive theories of hippocampal function 9-11. However, whether predictive learning can also account for the ability to produce offline replay is unknown. Here, we find that spatially-tuned cells, which robustly emerge from all forms of predictive learning, do not guarantee the presence of a cognitive map with the ability to generate replay. Offline simulations only emerged in networks that used recurrent connections and head-direction information to predict multi-step observation sequences, which promoted the formation of a continuous attractor reflecting the geometry of the environment. These offline trajectories were able to show wake-like statistics, autonomously replay recently experienced locations, and could be directed by a virtual head direction signal. Further, we found that networks trained to make cyclical predictions of future observation sequences were able to rapidly learn a cognitive map and produced sweeping representations of future positions reminiscent of hippocampal theta sweeps 12. These results demonstrate how hippocampal-like representation and replay can emerge in neural networks engaged in predictive learning, and suggest that hippocampal theta sequences reflect a circuit that implements a data-efficient algorithm for sequential predictive learning. Together, this framework provides a unifying theory for hippocampal functions and hippocampal-inspired approaches to artificial intelligence.

neuroscience↗

bioRxiv2023

Hyperpolarization-Activated Currents Drive Neuronal Activation Sequences in Sleep

Sequential neuronal patterns are believed to support information processing in the cortex, yet their origin is still a matter of debate. We report that neuronal activity in the mouse head-direction cortex (HDC, i.e., the post-subiculum) was sequentially activated along the dorso-ventral axis during sleep at the transition from hyperpolarized "DOWN" to activated "UP" states, while representing a stable direction. Computational modelling suggested that these dynamics could be attributed to a spatial gradient of hyperpolarization-activated current (Ih), which we confirmed in ex vivo slice experiments and corroborated in other cortical structures. These findings open up the possibility that varying amounts of Ih across cortical neurons could result in sequential neuronal patterns, and that travelling activity upstream of the entorhinal-hippocampal circuit organises large-scale neuronal activity supporting learning and memory during sleep. HighlightsO_LINeuronal Activation Sequence in HDC: neuronal activity was sequentially reinstated along the dorsoventral axis of the HDC at UP state but not DOWN state onset. C_LIO_LIRole of Ih in Sequence Generation: Incorporating the hyperpolarization-activated current (Ih) into computational models, we identified its pivotal role in UP/DOWN dynamics and neuronal activity sequences. C_LIO_LIEx Vivo Verification: slice physiology revealed a dorsoventral gradient of Ih in the HDC. C_LIO_LIImplications Beyond HDC: the gradient of Ih could account for the sequential organization of neuronal activity across various cortical areas. C_LI

neuroscience↗

bioRxiv2022

Pynapple: a toolbox for data analysis in neuroscience.

Datasets collected in neuroscientific studies are of ever-growing complexity, often combining high dimensional time series data from multiple data acquisition modalities. Handling and manipulating these various data streams in an adequate programming environment is crucial to ensure reliable analysis, and to facilitate sharing of reproducible analysis pipelines. Here, we present Pynapple, the PYthon Neural Analysis Package, a lightweight python package designed to process a broad range of time-resolved data in systems neuroscience. The core feature of this package is a small number of versatile objects that support the manipulation of any data streams and task parameters. The package includes a set of methods to read common data formats and allows users to easily write their own. The resulting code is easy to read and write, avoids low-level data processing and other error-prone steps, and is open source. Libraries for higher-level analyses are developed within the Pynapple framework but are contained within in a collaborative repository of specialized and continuously updated analysis routines. This provides flexibility while ensuring long-term stability of the core package. In conclusion, Pynapple provides a common framework for data analysis in neuroscience. HighlightsO_LIAn open-source framework for data analysis in systems neuroscience. C_LIO_LIEasy-to-use object-oriented programming for data manipulation. C_LIO_LIA lightweight and standalone package ensuring long-term backward compatibility. C_LI

neuroscience↗

bioRxiv2022

Synaptic homeostasis transiently leverages Hebbian mechanisms for a multiphasic response to inactivity

Neurons use various forms of negative feedback to maintain their synaptic strengths within an operationally useful range. While this homeostatic plasticity is thought to distinctly counteract the destabilizing positive feedback of Hebbian plasticity, there is considerable overlap in the molecular components mediating both forms of plasticity. The varying kinetics of these components spurs additional inquiry into the dynamics of synaptic homeostasis. We discovered that upscaling of synaptic weights in response to prolonged inactivity is nonmonotonic. Surprisingly, this seemingly oscillatory adaptation involved transient appropriation of molecular effectors associated with Hebbian plasticity, namely CaMKII, L-type Ca2+ channels, and Ca2+-permeable AMPARs, and homeostatic elements such as calcineurin. We created a dynamic model that shows how traditionally "Hebbian" and "homeostatic" mechanisms can cooperate to autoregulate postsynaptic Ca2+ levels. We propose that this combination of mechanisms allows excitatory synapses to adapt to prolonged activity changes and safeguard the capability to undergo future strengthening on demand.

neuroscience↗

bioRxiv2021

Distinct ground state and activated state modes of spiking in forebrain neurons

Neuronal firing patterns have significant spatiotemporal variability with no agreed upon theoretical framework. Using a combined experimental and modeling approach, we found that spike interval statistics can be described by discrete modes of activity. Of these, a "ground state" (GS) mode of low-rate spiking is universal among forebrain excitatory neurons and characterized by irregular spiking at neuron-specific rates. In contrast, "activated state" (AS) modes consist of spiking at characteristic timescales and regularity that are specific to neuron populations in a given region and brain state. The majority of spiking is contributed by GS mode, while neurons can transiently switch to AS spiking in response to stimuli or in coordination with population activity patterns. We hypothesize that GS spiking serves to maintain a persistent backbone of neuronal activity while AS modes support communication functions.

neuroscience↗

bioRxiv2018

Excitable dynamics of NREM sleep: a unifying model for neocortex and hippocampus

During non-rapid eye movement (NREM) sleep, the neocortex and hippocampus alternate between periods of neuronal spiking and inactivity. By directly comparing experimental observations with a mean field model of an adapting, recurrent neuronal population, we find that the neocortical alternations reflect a dynamical regime in which a stable active state is interrupted by transient inactive states (slow waves) while the hippocampal alternations reflect a stable inactive state interrupted by transient active states (sharp waves). We propose that during NREM sleep, hippocampal and neocortical populations are excitable: each in a stable state from which internal fluctuations or external perturbation can evoke the stereotyped population events that mediate NREM functions.

neuroscience↗

bioRxiv2016

Sleep regulation of the distribution of cortical firing rates

Sleep is thought to mediate mnemonic and homeostatic functions. However, the mechanism by which this brain state can implement both the \"selective\" plasticity needed to consolidate novel memory traces as well as the \"general\" plasticity necessary to maintain a well-functioning neuronal system is unclear. Recent findings show that both of these functions differentially affect neurons based on their intrinsic firing rate, a ubiquitous neuronal heterogeneity. Furthermore, they are both implemented by the NREM slow oscillation, which also distinguishes neurons based on firing rate during sequential activity at the DOWN->UP transition. These findings suggest a mechanism by which spiking activity during the slow oscillation acts to maintain network statistics that promote a skewed distribution of neuronal firing rates, and \"perturbation\" of that activity by hippocampal replay acts to integrate new memory traces into the existing cortical network.

neuroscience↗