Biology subjects

Savin, C.

Publications and source records attributed to Savin, C..

25 records · Page 2Linked to original sources

bioRxiv2021

The structure of hippocampal CA1 interactions optimizes spatial coding across experience

Although much is known about how single neurons in the hippocampus represent an animals position, how cell-cell interactions contribute to spatial coding remains poorly understood. Using a novel statistical estimator and theoretical modeling, both developed in the framework of maximum entropy models, we reveal highly structured cell-to-cell interactions whose statistics depend on familiar vs. novel environment. In both conditions the circuit interactions optimize the encoding of spatial information, but for regimes that differ in the signal-to-noise ratio of their spatial inputs. Moreover, the topology of the interactions facilitates linear decodability, making the information easy to read out by downstream circuits. These findings suggest that the efficient coding hypothesis is not applicable only to individual neuron properties in the sensory periphery, but also to neural interactions in the central brain.

neuroscience↗

bioRxiv2021

Across-animal odor decoding by probabilistic manifold alignment

Identifying the common structure of neural dynamics across subjects is key for extracting unifying principles of brain computation and for many brain machine interface applications. Here, we propose a novel probabilistic approach for aligning stimulus-evoked responses from multiple animals in a common low dimensional manifold and use hierarchical inference to identify which stimulus drives neural activity in any given trial. Our probabilistic decoder is robust to a range of features of the neural responses and significantly outperforms existing neural alignment procedures. When applied to recordings from the mouse olfactory bulb, our approach reveals low-dimensional population dynamics that are odor specific and have consistent structure across animals. Thus, our decoder can be used for increasing the robustness and scalability of neural-based chemical detection.

neuroscience↗

bioRxiv2021

Subpopulations of neurons in lOFC encode previous and current rewards at time of choice

1Studies of neural dynamics in lateral orbitofrontal cortex (lOFC) have shown that subsets of neurons that encode distinct aspects of behavior, such as value, may project to common downstream targets. However, it is unclear whether reward history, which may subserve lOFCs well-documented role in learning, is represented by functional subpopulations in lOFC. Previously, we analyzed neural recordings from rats performing a value-based decision-making task, and we documented trial-by-trial learning that required lOFC (Constantinople et al., 2019). Here we characterize functional subpopulations of lOFC neurons during behavior, including their encoding of task variables. We found five distinct clusters of lOFC neurons, either based on clustering of their trial-averaged peristimulus time histograms (PSTHs), or a feature space defined by their average conditional firing rates aligned to different task variables. We observed weak encoding of reward attributes, but stronger encoding of reward history, the animals left or right choice, and reward receipt across all clusters. Only one cluster, however, encoded the animals reward history at the time shortly preceding the choice, suggesting a possible role in integrating previous and current trial outcomes at the time of choice. This cluster also exhibits qualitatively similar responses to identified corticostriatal projection neurons in a recent study (Hirokawa et al., 2019), and suggests a possible role for subpopulations of lOFC neurons in mediating trial-by-trial learning.

neuroscience↗

bioRxiv2021

Targeted comodulation supports flexible and accurate decoding in V1

Sensory-guided behavior requires reliable encoding of stimulus information in neural populations, and task-specific readout through a selective combination of these responses. The former has been the topic of extensive study, but the means by which the brain achieves the latter remain poorly understood, especially when adapting to changing task demands. Here we introduce a novel theory for adaptive sensory processing based on functionally-targeted stochastic modulation. We find that responses of neurons in area V1 of monkeys performing a visual orientation discrimination task exhibit low-dimensional, rapidly fluctuating gain modulation, which is stronger in neurons that are most informative for the current behavioral task. We propose that this modulation serves as a label that supports adaptive downstream readout. Our theoretical and experimental results show that V1 modulation can be used to decode from neural activity after only a small number of training trials, consistent with observed behavior. In a hierarchical visual neural network model, the modulator-induced labels are learned quickly and accompany task information across several processing stages to finally guide decisions. Consistent with this model, we find that the V1 modulatory signal is also present in the activity of simultaneously recorded MT units, and that its label of task information is preserved. Our findings provide evidence for a novel mechanism for task-adaptive information routing in the brain, through targeted co-modulation.

neuroscience↗

bioRxiv2020

Learning efficient task-dependent representations with synaptic plasticity

Neural populations do not perfectly encode the sensory world: their capacity is limited by the number of neurons, metabolic and other biophysical resources, and intrinsic noise. The brain is presumably shaped by these limitations, improving efficiency by discarding some aspects of incoming sensory streams, while prefer-entially preserving commonly occurring, behaviorally-relevant information. Here we construct a stochastic recurrent neural circuit model that can learn efficient, task-specific sensory codes using a novel form of reward-modulated Hebbian synaptic plasticity. We illustrate the flexibility of the model by training an initially unstructured neural network to solve two different tasks: stimulus estimation, and stimulus discrimination. The network achieves high performance in both tasks by appropriately allocating resources and using its recurrent circuitry to best compensate for different levels of noise. We also show how the interaction between stimulus priors and task structure dictates the emergent network representations.

neuroscience↗

bioRxiv2020

A mouse bio-electronic nose for sensitive and versatile chemical detection

When it comes to simultaneous versatility, speed, and specificity in detecting volatile chemicals, biological olfactory systems far outperform all artificial chemical detection devices. Consequently, the use of trained animals for chemical detection in security, defense, healthcare, agriculture, and other applications has grown astronomically. However, the use of animals in this capacity requires extensive training and behavior-based communication. Here we propose an alternative strategy, a bio-electronic nose, that capitalizes on the superior capability of the mammalian olfactory system, but bypasses behavioral output by reading olfactory information directly from the brain. We engineered a brain-machine interface that captures neuronal signals from an early stage of olfactory processing in awake mice, and used machine learning techniques to form a sensitive and selective chemical detector. We chronically implanted a grid electrode array on the surface of the mouse olfactory bulb and systematically recorded responses to a large battery of odorants and odorant mixtures across a wide range of concentrations. The bio-electronic nose has a comparable sensitivity to the trained animal and can detect odors on a variable background. We also introduce a novel genetic engineering approach designed to improve the sensitivity of our bio-electronic nose for specific chemical targets. Our bio-electronic nose outperforms current detection methods and unlocks a wide spectrum of civil, medical and environmental applications.

bioengineering↗

bioRxiv2019

Flexible and accurate decoding of neural populations through stochastic comodulation

Sensory-guided behavior requires reliable encoding of information (from stimuli to neural responses) and flexible decoding (from neural responses to behavior). In typical decision tasks, a small subset of cells within a large population encode task-relevant stimulus information and need to be identified by later processing stages for relevant information to be transmitted. A statistically optimal decoder (e.g., maximum likelihood) can utilize task-relevant cells for any given task configuration, but relies on complete knowledge of the relationship between the task and the stimulus-response and noise properties of the encoding population. The brain could learn an optimal decoder for a task through supervised learning (i.e., regression), but this typically requires many training trials, and thus lacks the flexibility of humans or animals, that can rapidly adjust to changes in task parameters or structure. Here, we propose a novel decoding solution based on functionally targeted stochastic modulation. Population recordings during different discrimination tasks have revealed that a substantial portion of trial-to-trial variability in cell responses can be explained by stochastic modulatory signals that are shared, and that seem to preferentially target task-informative neurons (Rabinowitz et al., 2015). The variability introduced by these modulators corrupts the encoded stimulus signal, but we propose that it also serves as a label for the informative neurons, allowing the decoder to solve the identification problem. We show in simulations of a modulated Poisson spiking model that a linear decoder with readout weights proportional to the estimated neuron-specific strength of modulation achieves performance close to an optimal decoder.

neuroscience↗