Biology subjects

Chettih, S. N.

Publications and source records attributed to Chettih, S. N..

5 recordsLinked to original sources

bioRxiv2024

Barcode activity in a recurrent network model of the hippocampus enables efficient memory binding

Forming an episodic memory requires binding together disparate elements that co-occur in a single experience. One model of this process is that neurons representing different components of a memory bind to an "index" -- a subset of neurons unique to that memory. Evidence for this model has recently been found in chickadees, which use hippocampal memory to store and recall locations of cached food. Chickadee hippocampus produces sparse, high-dimensional patterns ("barcodes") that uniquely specify each caching event. Unexpectedly, the same neurons that participate in barcodes also exhibit conventional place tuning. It is unknown how barcode activity is generated, and what role it plays in memory formation and retrieval. It is also unclear how a memory index (e.g. barcodes) could function in the same neural population that represents memory content (e.g. place). Here, we design a biologically plausible model that generates barcodes and uses them to bind experiential content. Our model generates barcodes from place inputs through the chaotic dynamics of a recurrent neural network and uses Hebbian plasticity to store barcodes as attractor states. The model matches experimental observations that memory indices (barcodes) and content signals (place tuning) are randomly intermixed in the activity of single neurons. We demonstrate that barcodes reduce memory interference between correlated experiences. We also show that place tuning plays a complementary role to barcodes, enabling flexible, contextually-appropriate memory retrieval. Finally, our model is compatible with previous models of the hippocampus as generating a predictive map. Distinct predictive and indexing functions of the network are achieved via an adjustment of global recurrent gain. Our results suggest how the hippocampus may use barcodes to resolve fundamental tensions between memory specificity (pattern separation) and flexible recall (pattern completion) in general memory systems.

neuroscience↗

bioRxiv2023

Barcoding of episodic memories in the hippocampus of a food-caching bird

Episodic memory, or memory of experienced events, is a critical function of the hippocampus1-3. It is therefore important to understand how hippocampal activity represents specific events in an animals life. We addressed this question in chickadees - specialist food-caching birds that hide food at scattered locations and use memory to find their caches later in time4, 5. We performed high-density neural recordings in the hippocampus of chickadees as they cached and retrieved seeds in a laboratory arena. We found that each caching event was represented by a burst of firing in a unique set of hippocampal neurons. These barcode-like patterns of activity were sparse (<10% of neurons active), uncorrelated even for immediately adjacent caches, and different even for separate caches at the same location. The barcode representing a specific caching event was transiently reactivated whenever a bird later interacted with the same cache - for example, to retrieve food. Barcodes co-occurred with conventional place cell activity6, 7, as well as location-independent responses to cached seeds. We propose that barcodes are signatures of episodic memories evoked during memory recall. These patterns assign a unique identifier to each event and may be a mechanism for rapid formation and storage of many non-interfering memories.

neuroscience↗

bioRxiv2021

Cognitive experience alters cortical involvement in navigation decisions

The neural correlates of decision-making have been investigated extensively, and recent work aims to identify under what conditions cortex is actually necessary for making accurate decisions. We discovered that mice with distinct cognitive experiences, beyond sensory and motor learning, use different cortical areas and neural activity patterns to solve the same task, revealing past learning as a critical determinant of whether cortex is necessary for decision-making. We used optogenetics and calcium imaging to study the necessity and neural activity of multiple cortical areas in mice with different training histories. Posterior parietal cortex and retrosplenial cortex were mostly dispensable for accurate decision-making in mice performing a simple navigation-based decision task. In contrast, these areas were essential for the same simple task when mice were previously trained on complex tasks with delay periods or association switches. Multi-area calcium imaging showed that, in mice with complex-task experience, single-neuron activity had higher selectivity and neuron-neuron correlations were weaker, leading to codes with higher task information. Therefore, past experience sets the landscape for how future tasks are solved by the brain and is a key factor in determining whether cortical areas have a causal role in decision-making.

neuroscience↗

bioRxiv2020

Modeling robust and efficient coding in the mouse primary visual cortex using computational perturbations

Investigating how visual inputs are encoded in visual cortex is important for elucidating the roles of cell populations in circuit computations. We here use a recently developed, large-scale model of mouse primary visual cortex (V1) and perturb both single neurons as well as functional- and cell-type defined population of neurons to mimic equivalent optogenetic perturbations. First, perturbations were performed to study the functional roles of layer 2/3 excitatory neurons in inter-laminar interactions. We observed activity changes consistent with the canonical cortical model (Douglas and Martin 1991). Second, single neuron perturbations in layer 2/3 revealed a center-surround inhibition-dominated effect, consistent with recent experiments. Finally, perturbations of multiple excitatory layer 2/3 neurons during visual stimuli of varying contrasts indicated that the V1 model has both efficient and robust coding features. The circuit transitions from predominantly broad like-to-like inhibition at high contrasts to predominantly specific like-to-like excitation at low contrasts. These in silico results demonstrate how the circuit can shift from redundancy reduction to robust codes as a function of stimulus contrast.

neuroscience↗

bioRxiv2020

Scaling of information in large neural populations reveals signatures of information-limiting correlations

How is information distributed across large neuronal populations within a given brain area? One possibility is that information is distributed roughly evenly across neurons, so that total information scales linearly with the number of recorded neurons. Alternatively, the neural code might be highly redundant, meaning that total information saturates. Here we investigated how information about the direction of a moving visual stimulus is distributed across hundreds of simultaneously recorded neurons in mouse primary visual cortex (V1). We found that information scales sublinearly, due to the presence of correlated noise in these populations. Using recent theoretical advances, we compartmentalized noise correlations into information-limiting and nonlimiting components, and then extrapolated to predict how information grows when neural populations are even larger. We predict that tens of thousands of neurons are required to encode 95% of the information about visual stimulus direction, a number much smaller than the number of neurons in V1. Overall, these findings suggest that the brain uses a widely distributed, but nonetheless redundant code that supports recovering most information from smaller subpopulations.

neuroscience↗