Biology subjects

Derdikman, D.

Publications and source records attributed to Derdikman, D..

12 recordsLinked to original sources

bioRxiv2026

Reward changes the use and structure of predictive spatial maps

Reward-related changes in hippocampal place-cell activity may reflect either different use of an existing spatial map or plasticity that reshapes the map. We separated these contributions in a two-pathway model: a recurrent network learned to predict sensory input and developed a spatial representation, while a reinforcement-learning controller combined this representation with an explicit goal signal to guide behavior. Replaying identical trajectories before and after learning isolated representational plasticity. Goal-directed behavior strongly concentrated place fields near reward, whereas changes to the representation were smaller and more variable. A reward-proximity teaching signal made these changes larger and more consistent, and the change was strongly attenuated when recurrent plasticity was restricted. After reward relocation, goal-related directional tuning adapted more fully than place-field organization, while withdrawing reward and proximity teaching reversed the learned field concentration. Thus, reward affects predictive spatial maps through separable mechanisms: goal information changes how an existing map is used, whereas reward-related teaching signals reshape the map itself. Our results provide a concrete framework for future experiments to disentangle how reward information, plasticity and behavior jointly shape hippocampal activity.

neuroscience↗

bioRxiv2026

A simple model of the co-emergence of grid and place fields

Grid cells in the medial entorhinal cortex and place cells in the hippocampus together support spatial navigation. The two regions are reciprocally connected, and there is a chicken-and-egg problem for how both arise and reinforce each other during development. Current computational accounts either derive one type from the other or use network dynamics to model the emergence of one type in isolation. We introduce a unified recurrent network model that instantiates Dales Law (every neuron is either excitatory or inhibitory), and is trained to predict the next sensory observation from masked previous sensory observations and egocentric motion. To our knowledge, this is the first single-objective model in which grid and place cells co-emerge without supervision of either type, or reliance on pre-existing spatial-cell representations. The two kinds of spatial codes coexist across 1,000 different training configurations, with their balance set by the amount of sensory noise and masking. Without retraining, the network qualitatively reproduces experimentally observed grid fragmentation in hairpin mazes, grid merging after wall removal, lattice alignment across connected rooms, locally ordered 3D fields observed in freely flying bats, as well as the developmental order in which place cells precede grid cells. We interpret these results in terms of two complementary encoding pressures within a single sensory-prediction objective: (1) correcting errors or reconstructing missing components of sensory observations, and (2) prediction of the next sensory state during navigation. Our results suggest a circuit-level account of the co-emergence of grid and place cells, and experimentally testable predictions for the two kinds of spatial codes. Codes for experiments are available at 1,2.

neuroscience↗

bioRxiv2026

Spatial remapping in the subicular complex and entorhinal cortex follows 1 low-dimensional geometric principles

Neurons in the hippocampal formation are part of the brains cognitive map, representing the spatial structure of the environment through coordinated activity across place cells, grid cells, border cells, head-direction cells and others1-5. Although remapping between environments has been extensively documented6-9, it remains unknown whether transitions between maps reflect unconstrained reorganization or obey a systematic transformation principle. To address this question, we recorded large neuronal populations from the subicular complex and entorhinal cortex in awake, behaving mice navigating environments spanning a wide range of geometries. We asked whether spatial representations across rooms could be related through a shared class of coordinate transformations. Despite pronounced heterogeneity and apparent randomness in single-cell remapping, population-level decoding across environments demonstrated a consistent low-dimensional affine transformation of coordinates, comprising rotation, scaling, shear, reflection, and translation. Thus, what appears as complex remapping at the level of individual neurons reduces to a compact geometric rule at the level of neural assemblies. These results indicate that the hippocampal formation maintains a structured internal coordinate template that is flexibly tailored to environmental geometry. This may serve as the organisms internal model of space.

neuroscience↗

bioRxiv2025

Auditory network persistence of stimulus representation in awake and naturally sleeping mice

Persistent neural activity often outlasts sensory stimulation, bridging perception and action. While commonly linked to working memory and decision making, its existence during passive states and sleep remains unclear. Using chronic high-density electrophysiology in freely behaving mice, we show that population spiking activity across the auditory cortical hierarchy enables decoding of past stimuli long after their offset, during both wakefulness and sleep. Time-resolved decoding revealed that in wakefulness, persistent representations decay uniformly across sensory and association cortices, whereas during sleep, persistence is prolonged in association cortex but remains brief in early auditory regions. Recurrent neural network modeling showed that higher internal noise during wakefulness reproduces this pattern, suggesting that reduced interference during sleep stabilizes sensory traces in associative areas. Our results demonstrate that persistent representation is a passive, state-dependent feature of sensory processing, supporting sensory maintenance even in the absence of active engagement.

neuroscience↗

bioRxiv2025

REMI: Reconstructing Episodic Memory During Intrinsic Path Planning

Grid cells in the medial entorhinal cortex (MEC) and place cells in the hippocampus (HC) both form spatial representations. Grid cells fire in triangular grid patterns, while place cells fire at specific locations and respond to contextual cues. How do these interacting systems support not only spatial encoding but also internally driven path planning, such as navigating to locations recalled from cues? Here, we propose a system-level theory of MEC-HC wiring that explains how grid and place cell patterns could be connected to enable cue-triggered goal retrieval, path planning, and reconstruction of sensory experience along planned routes. We suggest that place cells autoassociate sensory inputs with grid cell patterns, allowing sensory cues to trigger recall of goal-location grid patterns. We show analytically that grid-based planning permits shortcuts through unvisited locations and generalizes local transitions to long-range paths. During planning, intermediate grid states trigger place cell pattern completion, reconstructing sensory experiences along the route. Using a single-layer RNN modeling the HC-MEC loop with a planning subnetwork, we demonstrate these effects in both biologically grounded navigation simulations using RatatouGym and visually realistic navigation tasks using Habitat Sim. Codes for experiments, simulations, and vision encoder are available at 1,2,3.

neuroscience↗

bioRxiv2023

Representational drift as a result of implicit regularization

Recent studies show that, even in constant environments, the tuning of single neurons changes over time in a variety of brain regions. This representational drift has been suggested to be a consequence of continuous learning under noise, but its properties are still not fully understood. To investigate the underlying mechanism, we trained an artificial network on a simplified navigational task. The network quickly reached a state of high performance, and many units exhibited spatial tuning. We then continued training the network and noticed that the activity became sparser with time. Initial learning was orders of magnitude faster than ensuing sparsification. This sparsification is consistent with recent results in machine learning, in which networks slowly move within their solution space until they reach a flat area of the loss function. We analyzed four datasets from different labs, all demonstrating that CA1 neurons become sparser and more spatially informative with exposure to the same environment. We conclude that learning is divided into three overlapping phases: (i) Fast familiarity with the environment; (ii) slow implicit regularization; (iii) a steady state of null drift. The variability in drift dynamics opens the possibility of inferring learning algorithms from observations of drift statistics.

neuroscience↗

bioRxiv2022

Time or distance: predictive coding of Hippocampal cells

The discovery of place cells within the hippocampus has pointed to the importance of the hippocampus for navigation. The more recent discovery of hippocampal time cells has broadened the perspective of encoding in the hippocampus. An alternative hypothesis to the existence of time cells is based on the notion that hippocampal cells deduce location by integrating travelled distance ("path integration"). According to this alternate hypothesis, time cells, which fire at particular times when animals are running on a treadmill without changing location, actually encode accumulated distance on the treadmill. To examine this hypothesis, Kraus et al.1 performed treadmill experiments in which animals either ran for a fixed time or a fixed distance with varying velocities. Two distinct coding modes of hippocampal principal cells were found. Some cells encoded travelled distance and others elapsed time, thus refuting the notion that all hippocampal cells were performing path integration. Using the data from these experiments, we asked whether the two populations depended on the type of task the rats were engaged in. We show that the type of experiment determined the cells encoding, such that in fixed-distance experiments distance-encoding cells dominated, while on fixed-time experiments time-encoding cells dominated. These results suggest that the cells encoding contains a predictive element, dependent on the important variables of the experiment.

neuroscience↗

bioRxiv2022

Experience, not time, determines representational drift in the hippocampus

Memories of past events can be recalled long after the event, indicating stability. But new experiences are also integrated into existing memories, indicating plasticity. In the hippocampus, spatial representations are known to remain stable, but have also been shown to drift over long periods of time. We hypothesized that experience, more than the passage of time, is the driving force behind memory plasticity. We compared the stability of place cells in the hippocampus of mice traversing two similar, familiar tracks for different durations. We found that the more time spent in an environment, the greater the representational drift, regardless of the total elapsed time. Our results suggest that spatial representation is a dynamic process, related to the ongoing experiences within a specific context, and is related to the accumulation of new memories rather than to passive forgetting. HighlightsO_LIRepresentational drift is related to experience within an environment. C_LIO_LIRepresentational drift is a dynamic context-wide process. C_LIO_LIPlace cell number decreases with experience, spatial information content increases. C_LI

neuroscience↗

bioRxiv2021

Spatial coding in the hippocampus of flying owls

The elucidation of spatial coding in the hippocampus requires exploring diverse animal species. While robust place-cells are found in the mammalian hippocampus, much less is known about spatial coding in the hippocampus of birds - and nothing is known about avian spatial representation during flight. Here we used a wireless-electrophysiology system to record single neurons in the hippocampus and related pallial structures from freely flying barn owls (Tyto alba) - a central-place nocturnal predator species with excellent navigational abilities. The owls 3D position was monitored while it flew back and forth between two perches. We found place cells - neurons that robustly represented the owls location during flight, and its flight-direction - as well as neurons that coded the owls perching position between flights. Spatial coding was invariant to changes in lighting conditions and to the position of a salient object in the room. Place cells were found in the anterior hippocampus and in the adjacent posterior hyperpallium apicale, and to a much lesser extent in the visual Wulst (visual-cortex homologue). The finding of place-cells in flying owls suggests commonalities in spatial coding across a variety of species - including rodents, bats and owls.

neuroscience↗

bioRxiv2020

Head-Direction Coding in the Hippocampal Formation of Birds

Birds strongly rely on spatial memory and navigation. Therefore, it is of utmost interest to reveal how space is represented in the avian brain. Here we used tetrodes to record neurons from the hippocampal formation of Japanese quails - a ground-dwelling species - while the quails roamed a 1x1-meter arena (>2,100 neurons from 23 birds). Whereas spatially-modulated cells (place-cells, border-cells, etc.) were generally not encountered, the firing-rate of 12% of the neurons was unimodally and significantly modulated by the head-azimuth - i.e. these were head-direction cells (HD cells). Typically, HD cells were maximally active at one preferred-direction and minimally at the opposite null-direction, with preferred-directions spanning all 360{degrees}. The HD tuning was relatively broad (mean width [~]130{degrees}), independent of the animals position and speed, and was stable during the recording-session. Similarly to findings in rodents, the HD tuning usually rotated with the rotation of a salient visual cue in the arena. These findings support the existence of an allocentric head-direction representation in the quail hippocampal formation, and provide the first demonstration of head-direction cells in birds.

neuroscience↗

bioRxiv2019

During hippocampal inactivation, grid cells maintain their synchrony, even when the grid pattern is lost

The grid cell network in the MEC has been subject to thorough testing and analysis, and many theories for their formation have been suggested. To test some of these theories we re-analyzed data from Bonnevie et al. (2013), in which the hippocampus was inactivated and grid cells were recorded in the MEC, to investigate whether the firing associations of grid cells depend on hippocampal inputs. Specifically, we examined temporal and spatial correlations in the firing times of simultaneously recorded grid cells before and during hippocampal inactivation. Our analysis revealed evidence of network coherence in grid cells even in the absence of hippocampal input to the MEC, both in regular grid cells and in those that became head-direction cells after hippocampal inactivation. This favors models which suggest that phase relations between grid cells in the MEC are dependent on intrinsic connectivity within the MEC.

neuroscience↗

bioRxiv2017

Head-Direction drift in rat pups is consistent with an angular path-integration process

The sense of direction is a vital computation, whose neural basis is considered to be carried out by head-direction cells. One way to estimate head-direction is by integrating head angular-velocity over time. However, this process results in error accumulation resembling a random walk, proportional to [Formula], which constitutes a mark for a path integration process. In the present study we analyzed previously recorded data to quantify the drift in head-direction cells of rat pups before and after eye-opening. We found that in rat pups before eye-opening the drift propagated as a random walk, while in rats after eye-opening the drift was lower. This suggests that a path-integration process underlies the estimation of head-direction, such that before eye-opening the head-direction system runs in an open-loop manner and accumulates error. After eye-opening, visual-input, such as arena shape, helps to correct errors and thus compute the sense of direction accurately.

bioinformatics↗