Neural coding

…The search targets expertise in neural coding, computation and dynamics, or approaches at the intersection of neuroscience and artificial intelligence, including large language models. The ideal candidate runs collaborative dry-lab research aligned with departmental strengths such as learning and memory, decision making, motor control, affective neuro

…in both biological and artificial neural network. For example, normalization can maximize sensitivity in retina neurons, reduce redundancy in sensory cortex, and accelerate and stabilize the learning process in deep neural networks. However, the mechanism underlying the neuronal heterogeneity of normalization and

…is how posterior probablities are neurally encoded. In recent years, standard approaches like sampling-based and variational approximations have been extended by deep neural networks (DNNs). For variational approaches, DNNs are frequently used to define encoders in variational autoencoders (VAE) settings. It remains unclear, however, what aspects of posterior approximations are important to explain neural encoding . Unlik

…resulted in computational theories of neural coding that assume binarity.Despite this assumption, a considerable body of evidence shows that AP waveforms exhibit systematic, within-neuron variation that may contribute additional informat

Well designed behavioral experiments are vital to understand the complexities of neural coding, particularly as it becomes evident that such coding may be context-specific, highly distributed, and multiplexed. Traditional commercial solutions for behavioral setups, although available, often lack flexibility and comprehensive user control, and their high costs limit accessibility. Recognizing the necessity for rapid development and adaptability in behavioral experiments, we introduce here a novel cost-efficient, open-source system for controlling behavioral experiments that maintains the crucia

…highlight evidence that the same neural coding mechanisms found to underly the representation of physical spaces also apply to conceptual domains. Specifically, the representation of locations in both physical and conceptual spaces is reflected in distinct neural activity patterns in hippocampus and entorhinal cortex. Electrophysiological work from rodents has shown that single hippocampal place cells represent different locations in different spaces – a functio

…This classical view of the so-called “neural coding” emphasize the importance of information carried by changes in the neuronal rate a

Jointly designing computational signal extraction and optics allows scalability to larger fields-of-view, lighter and faster functional neuroimaging devices. Using this insight, we developed a modular, mesoscale light field (MesoLF) imaging hardware and software solution (Nature Methods 20(4), 2023) that allows recording from thousands of neurons within volumes of ⌀ 4 × 0.2 mm located at depths of up to 350 μm in the mouse cortex. MesoLF achieves an imaging rate of 18 volumes per second and an effective voxel rate of approximately 40 Megavoxels per second. By using our optical design and compu

…a fundamentally different approach to neural coding by using a novel method, SpikeShip, that is based on optimal transport theory. SpikeShip captures the information in all the relative spike-timing relations among neurons, without defining arbitrary features and counting spikes within wi

…distinguish competing accounts of neural coding.

A neuron’s input-output function is a central component of network dynamics and is commonly understood in terms of two fundamental operating regimes: 1) the mean-driven regime where the mean input drives regular and frequent firing, and 2) the fluctuation-driven regime where input fluctuations drive responses at relatively low firing frequency but with variable intervals. Active dendrites are expected to profoundly influence the input-output function by either controlling gain modulation or through the additive modulation by dendritic inputs that have been transformed nonlinearly as in an arti

Learning general principles from single examples is a hallmark of high cognitive functions. This type of learning is computationally challenging for artificial agents, yet many animals such as primates can perform it with ease. To study one-exemplar generalization in the lab, it would be ideal to develop appropriate behavioral tasks in more tractable model organisms, such as rodents. Here we report that mice can perform one-exemplar generalization with difficult texture-based visual stimuli such as “leaves” and “rocks”. We trained mice to discriminate between single images from two texture cat

Studying chemosensory processing desires precise chemical cue presentation, behavioral response monitoring, and large-scale neuronal activity recording. Larval zebrafish are especially attractive vertebrates for such study. Behavioral assays and imaging systems that can achieve the necessary spatial precision for chemical delivery is an important pre-requisite for data interpretation. Here we present Fish-on-Chips, a set of optofluidic tools for highly-controlled chemical delivery while simultaneously imaging behavioral outputs and whole-brain neuronal activities at cellular resolution in larv

Neural activity in visual cortex is well described by models composed of a deterministic tuning function, a stochastic gain, and a point process. Recent work has shown that in the responses of neurons in the visual cortex of nonhuman primates, variability in the gain is tuned to the statistics of the sensory input. This suggests that gain variability may reflect signal rather than noise. Some theories of neural coding suggest that perceptual uncertainty may be encoded in neural response variability, and that, specifically, gain variability appears well suited to inform downstream uncertainty e

To understand how the brain controls behavior, we need to understand the neural code for movement. Descending neurons (DNs) represent a bottleneck for motor control signals, and so provide an opportunity to study neural coding in a compact form. In Drosophila, almost all DNs are uniquely identifiable in the connectome of the central nervous system and targetable with specific genetic drivers, allowing us to study their connectivity and physiology in parallel. Previous studies have concluded that these DNs generate high-level motor commands, such as “stop” or “turn”, leaving the detailed implem

The research in my lab focuses on sensory signal processing, particularly in cases where sensory systems perform at or near the limits imposed by physics. Photon counting in the visual system is a beautiful example. At its peak sensitivity, the performance of the visual system is limited largely by the division of light into discrete photons. This observation has several implications for phototransduction and signal processing in the retina: rod photoreceptors must transduce single photon absorptions with high fidelity, single photon signals in photoreceptors, which are only 0.03 – 0.1 mV, mus

Neural circuits encode and transmit information to support a diversity of tasks. Such neural coding is corrupted by noise and uncertainty, which in turn results in different magnitudes of error in the signals that are received by downstream regions. Therefore, even in the same task and given a fixed distribution of input signals, the performance of a neural code is characterized by a distribution of errors. Different task demands might necessitate distributions with different properties; for example, a task that requires avoiding failure at all costs might necessitate coding schemes that minim

…understand the efficiency of various neural coding or communication schemes. Information-theoretic measures called partial i

According to the efficient coding hypothesis, neural responses represent information so as to enable the most accurate readout possible, constrained by neuronal resources and neuronal noise. To date, much of the theoretical work on efficient neural coding has focused on relatively simple models of neural activity, characterized by smooth, often unimodal tuning curves. Real neurons, however, often exhibit more complex tuning curves. For instance, in the enthorinal cortex, the periodicity of grid cell tuning curves, as well as their functional organization in modules, imparts the population code

It is an open question to what extent neural coding and computation are based on precise patterns of spikes. Theoretically individual neurons can serve as detectors for given spatio-temporal spike patterns, however, this requires supervised adjustment of their input synapses. It is not known if existing activity dependent synaptic plasticity mechanisms can lead to unsupervised emergence of spatio-temporal pattern selectivity. Here, a combination of realistic mechanisms is demonstrated to self-organize the synaptic input e?cacies such that neurons become detectors of patterns repeating in the i

…This necessitates neural coding strategies that can dynamically balance these conflicting needs. I will discuss our ongoing theoretical work to understand how this balance can best be achieved. We connect ideas from efficient coding and Bayesian inference to ask how sensory syste

…This necessitates neural coding strategies that can dynamically balance these conflicting needs. I will discuss our ongoing theoretical work to understand how this balance can best be achieved. We connect ideas from efficient coding and Bayesian inference to ask how sensory syste

…will show how an essentially 'spatial' coding mec

…Second, we construct a neural coding framework by which these rational thoughts, their computational dynamics, and actions can be identified within the manifold of neural activity. We illustrate the value of this a