Convolutional neural networks

Convolutional neural networks (CNNs) provide a generalizable architecture for encoding models that predict neural activity evoked by natural sounds with much greater accuracy than other established models. However, the complexity of CNNs makes it difficult to discern the computational properties that support their improved predictive power. We used a combination of local linear approximation and dimensionality reduction to measure the tuning subspace captured by a CNN. Single-unit data was recorded using microelectrode arrays from primary auditory cortex (A1) of awake, passively listening ferr

…Models of primate vision based on convolutional neural networks (CNNs) achieve high object recognition performance but, unlike primates, emphasize texture over shape. Overcoming this texture bias and making models more brain-like requires data sets that distinguish how shape and texture cues interact in mid-level visual processing. To this end, we created sets of images that parametrically varied between objects and textures, and recorded responses to these images from populations of single units in macaque mid-level vi

…Convolutional neural networks are state-of-the-art models of the primary visual cortex (V1). Sensory processing in V1 is determined by contextual modulation via feedback and recurrent connections. However, the computational goal and the neural implementation that shape V1 activity are not yet fully understood. We use transfer learning to use an object recognition convolutional neural network (VGG-16) to predict firing rates and gam

In cases of profound cochlear dysfunction, electrical cochlear implants (eCIs) offer partial restoration of hearing. Nevertheless, the auditory experience with eCIs remains imperfect due to the broad excitation of auditory nerve neurons by electrical signals that limits the perceptual channels available to users. Optogenetics offers a breakthrough by enabling the stimulation of the auditory nerve by an optical cochlear implant (oCI). Light, unlike electrical signals, can be precisely confined in space, resulting in reduced spread of excitation and improved frequency selectivity. However, optog

Animals and humans adapt to environmental changes by encoding prior experiences. While classical conditioning paradigms like fear conditioning have been the main focus in the learning and memory field, other forms of associative learning are gaining attention. One example is higher-order conditioning, mediated learning, which allows non-reinforced neutral stimuli to induce attraction or aversion through associations with directly reinforced cues. In rodents, sensory preconditioning, and second-order conditioning tasks have been used to assess higher-order conditioning. In this study, we develo

The increasing complexity and volume of extracellular neural data generated by modern silicon-based probes present significant storage, transmission, and analysis challenges. Spike sorting algorithms have been developed to cope with this data deluge, but innovative approaches are required to compress the data while maintaining information fidelity efficiently.This project explores the application of deep learning techniques for compressing extracellular neural data, leveraging the hierarchical representations learned by convolutional neural networks to achieve efficient compression without com

AIMS: Sharp-wave ripples (SWR) are high frequency events recorded in the local field potential (LFP) of the hippocampus of rodents and humans. During neurological conditions such as epilepsy and Alzheimer’s disease, changes on the waveforms of SWRs are considered as biomarkers of dysfunction. However, SWR features cannot be fully characterized by spectral methods alone.METHODS: Here, we introduce a machine learning (ML) toolbox resulting from a community-based hackathon, that operates over high-density LFP recordings to detect hippocampal SWR. We train a wide variety of ML architectures (suppo

…Here, I will show how convolutional neural networks can be used as a model of the visual system that connects neural activity changes with such performance changes. Specifically, I will discuss how different anatomical forms of recurrence can account for better classification of noisy and degraded images with extended processing time. I will then show how experimentally-obser

…In recent years, convolutional neural networks (CNNs) have successfully modeled neural behavior across the visual cortices. However, there have been limited studies addressing the multitude of contextual effects that have

…and fMRI studies have tested neural sensitivity to images altered to exhibit either typical or atypical statistics. A general finding is that visual cortical neurons respond more strongly to images with natural statistics, and that this can vary across the visual hierarchy. Convolutional neural networks (CNNs) have achieved human-level performance in vision tasks and have been used as visual cortex models. However, CNN responses to images with altered s

The human visual cortex processes visual stimuli hierarchically. Early visual areas (V1, V2) of the ventral visual stream feed crude visual features (like orientation and edges) into later visual areas (V4, lateral occipital (Lat Occ), inferior temporal (IT)) that then encode complex visual features (like object form). Previous studies have reported a difference in fMRI responses between natural and urban landscapes in certain parts of the brain. Here we asked if this distinction in the representation of complex natural and man-made visual stimuli extends to the ventral visual stream and if st

Convolutional neural networks currently provide the best models of biological vision. However, their decision behavior, including the facts that they are deterministic and use equal number of computations for easy and difficult stimuli, differs markedly from human decision-making, thus limiting their applicability as models of human perceptual behavior. Here we develop a new neural network, RTNet, that generates stochastic decisions and human-like response time (RT) distributions, and also reproduces all foundational features of human accuracy, RT, and confidence. To test RTNet9s ability to pr

…To measure neural response, Electrocorticography (ECoG) has been utilized as an effective modality for brain-computer interfaces (BCIs). Many studies have shown that both motor imagery and movement execution are located in the frontoparietal region of the brain (Hardwick et. al., 2018). In this study, we aim to demonstrate a shared representation o

…surround share similar statistics, the neural responses are more suppressed by the surround. This behavior has been connected to computational benefits suc

…As a prime example, convolutional neural networks, a type of feedforward neural networks, are now approaching – and sometimes even surpassing – human accuracy on a variety of visual recognition tasks. In this talk, however, I will show that these neural networks and their recent extensions exhibit a limited ability to solve seemingly simple visual reasoning problems involving incremental grouping, similarity, and spatial relation judgments. Our group has developed a recurrent network model of classical and

…at the circuit level, and whether the neural architectures that support texture processing are shared across mammalian species is currently unknown. We addressed these questions in the mouse by first examining the mouse’s perceptual ability to discriminate higher order texture stat

…and accurate predictions in neural circuits. In neuroscience, translation invariance is at the heart of models of the visual system, while convolutional neural networks designed to exploit translation invariance triggered the first wave of deep learning successes. While the hallmarks of convolutions, namely localised receptive fields that tile the input space, can be implemented with fully-connected neural networks, learning convolutions directly from inputs in a fully-connected network has so far proven

Two very successful solutions exist to the problem of vision: biological brains and convolutional neural networks (CNNs). Despite the inspiration of artificial architectures from their biological counterparts, the extent to which the two solutions are comparable remains unclear, with evidence pointing to both core similarities (e.g., hierarchical learning of feature detectors with increasingly complex tuning) and key differences (e.g., very different sensitivity to pixel-level noise). In our work, we present results that bridge these two worlds, showing that prominent information processing tr

The responses of neurons in primary visual cortex depend strongly on the spatial context in which stimuli are embedded. This context-dependence has been theorized to reflect efficient and/or predictive coding of natural scenes. Critical tests of these theories in a generic form for natural images are currently lacking , because it is unclear how predictions, and measures of predictability, should be operationalized for natural scenes. Further, it is unclear whether there is just one type or level of predictability given the hierarchical processing in the primate visual system. Here, we traine

…Recently, feedforward Convolutional Neural Networks (ffCNNs), inspired by this classic framework, have revolutionized computer vision and been adopted as tools in neuroscience. However, despite these successes, there is much more to vision. I will present our work using visual crowding and related psychophysical effects as probes into visual processes that go beyond the classic fra

The last quarter century has provided extensive evidence that some regions of the human cortex are selectively engaged in processing a single specific domain of information, from faces, places, and bodies to language, music, and other people’s thoughts. This work dovetails with earlier theories in cognitive science highlighting domain specificity in human cognition, development, and evolution. But many questions remain unanswered about even the clearest cases of domain specificity in the brain, the selective engagement of the FFA, PPA, and EBA in the perception of faces, places, and bodies, r