Benjamin D. Haeffele

Multiscale Optical Ca2+ Imaging of Tonal Organization in Mouse Auditory Cortex

Spatial patterns of functional organization, resolved by microelectrode mapping, comprise a core principle of sensory cortices. In auditory cortex, however, recent two-photon Ca2+ imaging challenges this precept, as the traditional tonotopic arrangement appears weakly organized at the level of individual neurons. To resolve this fundamental ambiguity about the organization of auditory cortex, we developed multiscale optical Ca2+ imaging of unanesthetized GCaMP transgenic mice. Single-neuron activity monitored by two-photon imaging was precisely registered to large-scale cortical maps provided by transcranial widefield imaging. Neurons in the primary field responded well to tones; neighboring neurons were appreciably cotuned, and preferred frequencies adhered tightly to a tonotopic axis. By contrast, nearby secondary-field neurons exhibited heterogeneous tuning. The multiscale imaging approach also readily localized vocalization regions and neurons. Altogether, these findings cohere electrode and two-photon perspectives, resolve new features of auditory cortex, and offer a promising approach generalizable to any cortical area.

Global Optimality in Neural Network Training

The past few years have seen a dramatic increase in the performance of recognition systems thanks to the introduction of deep networks for representation learning. However, the mathematical reasons for this success remain elusive. A key issue is that the neural network training problem is nonconvex, hence optimization algorithms may not return a global minima. This paper provides sufficient conditions to guarantee that local minima are globally optimal and that a local descent strategy can reach a global minima from any initialization. Our conditions require both the network output and the regularization to be positively homogeneous functions of the network parameters, with the regularization being designed to control the network size. Our results apply to networks with one hidden layer, where size is measured by the number of neurons in the hidden layer, and multiple deep subnetworks connected in parallel, where size is measured by the number of subnetworks.

Multiscale mapping of frequency sweep rate in mouse auditory cortex

&NA; Functional organization is a key feature of the neocortex that often guides studies of sensory processing, development, and plasticity. Tonotopy, which arises from the transduction properties of the cochlea, is the most widely studied organizational feature in auditory cortex; however, in order to process complex sounds, cortical regions are likely specialized for higher order features. Here, motivated by the prevalence of frequency modulations in mouse ultrasonic vocalizations and aided by the use of a multiscale imaging approach, we uncover a functional organization across the extent of auditory cortex for the rate of frequency modulated (FM) sweeps. In particular, using two‐photon Ca2+ imaging of layer 2/3 neurons, we identify a tone‐insensitive region at the border of AI and AAF. This central sweep region behaves fundamentally differently from nearby neurons in AI and AII, responding preferentially to fast FM sweeps but not to tones or bandlimited noise. Together these findings define a second dimension of organization in the mouse auditory cortex for sweep rate complementary to that of tone frequency. HighlightsResponses to sounds were measured using multiscale Ca2+ imaging of auditory cortex.Regions responding to tones responded to slow frequency modulated (FM) sweeps.Regions unresponsive to tones instead responded to fast FM sweeps.Fast‐FM neurons were not responsive to bandlimited noise.

Blood cell detection and counting in holographic lens-free imaging by convolutional sparse dictionary learning and coding

We propose a convolutional sparse dictionary learning and coding approach for detecting and counting instances of a repeated object in a holographic lens-free image. The proposed approach exploits the fact that an image containing a single object instance can be approximated as the convolution of a (small) object template with a spike at the location of the object instance. Therefore, an image containing multiple non-overlapping instances of an object can be approximated as the sum of convolutions of templates with spikes. Given one or more images, one can learn a dictionary of templates using a convolutional extension of the K-SVD algorithm for sparse dictionary learning. Given a set of templates, one can efficiently detect object instances in a new image using a convolutional extension of the matching pursuit algorithm for sparse coding. Experiments on red blood cell (RBC) and white blood cell (WBC) detection and counting demonstrate that the proposed method produces promising results without requiring additional post-processing.

Removal of the twin image artifact in holographic lens-free imaging by sparse dictionary learning and coding

Mitigating the effects of the twin image artifact is one of the key challenges in holographic lens-free microscopy. This artifact arises due to the fact that imaging detectors can only record the magnitude of the hologram wavefront but not the phase. Prior work addresses this problem by attempting to simultaneously estimate the missing phase and reconstruct an image of the object specimen. Here we explore a fundamentally different approach based on post-processing the reconstructed image using sparse dictionary learning and coding techniques originally developed for processing conventional images. First, a dictionary of atoms representing characteristics from either the true image of the specimen or the twin image are learned from a collection of patches of the observed images. Then, by expressing each patch of the observed image as a sparse linear combination of the dictionary atoms, the observed image is decomposed into a component that corresponds to the true image and another one that corresponds to the twin image artifact. Experiments on counting red blood cells demonstrate the effectiveness of the proposed approach.

Efficient Reconstruction of Holographic Lens-Free Images by Sparse Phase Recovery

Digital holographic lens-free imaging is based on recording the diffraction pattern of light after it passes through a specimen and post-processing the recorded diffraction pattern to reconstruct an image of the specimen. If the full, complex-valued wave-front of the diffraction pattern could be recorded then the image reconstruction process would be straight-forward, but unfortunately image sensors typically only record the amplitude of the diffraction pattern but not the phase. As a result, many conventional reconstruction techniques suffer from substantial artifacts and degraded image quality. This paper presents a computationally efficient technique to reconstruct holographic lens-free images based on sparsity, which improves image quality over existing techniques, allows for the possibility of reconstructing images over a 3D volume of focal-depths simultaneously from a single recorded hologram, provides a robust estimate of the missing phase information in the hologram, and automatically identifies the focal depths of the imaged objects in a robust manner.