96 research outputs found
FocusStack and StimServer: a new open source MATLAB toolchain for visual stimulation and analysis of two-photon calcium neuronal imaging data
Two-photon calcium imaging of neuronal responses is an increasingly accessible technology for probing population responses in cortex at single cell resolution, and with reasonable and improving temporal resolution. However, analysis of two-photon data is usually performed using ad-hoc solutions. To date, no publicly available software exists for straightforward analysis of stimulus-triggered two-photon imaging experiments. In addition, the increasing data rates of two-photon acquisition systems imply increasing cost of computing hardware required for in-memory analysis. Here we present a Matlab toolbox, FocusStack, for simple and efficient analysis of two-photon calcium imaging stacks on consumer-level hardware, with minimal memory footprint. We also present a Matlab toolbox, StimServer, for generation and sequencing of visual stimuli, designed to be triggered over a network link from a two-photon acquisition system. FocusStack is compatible out of the box with several existing two-photon acquisition systems, and is simple to adapt to arbitrary binary file formats. Analysis tools such as stack alignment for movement correction, automated cell detection and peri-stimulus time histograms are already provided, and further tools can be easily incorporated. Both packages are available as publicly-accessible source-code repositories
Eigenspectrum bounds for semirandom matrices with modular and spatial structure for neural networks
The eigenvalue spectrum of the matrix of directed weights defining a neural network model is informative of several stability and dynamical properties of network activity. Existing results for eigenspectra of sparse asymmetric random matrices neglect spatial or other constraints in determining entries in these matrices, and so are of partial applicability to cortical-like architectures. Here we examine a parameterized class of networks that are defined by sparse connectivity, with connection weighting modulated by physical proximity (i.e., asymmetric Euclidean random matrices), modular network partitioning, and functional specificity within the excitatory population. We present a set of analytical constraints that apply to the eigenvalue spectra of associated weight matrices, highlighting the relationship between connectivity rules and classes of network dynamics
Sub-mW Neuromorphic SNN audio processing applications with Rockpool and Xylo
Spiking Neural Networks (SNNs) provide an efficient computational mechanism
for temporal signal processing, especially when coupled with low-power SNN
inference ASICs. SNNs have been historically difficult to configure, lacking a
general method for finding solutions for arbitrary tasks. In recent years,
gradient-descent optimization methods have been applied to SNNs with increasing
ease. SNNs and SNN inference processors therefore offer a good platform for
commercial low-power signal processing in energy constrained environments
without cloud dependencies. However, to date these methods have not been
accessible to ML engineers in industry, requiring graduate-level training to
successfully configure a single SNN application. Here we demonstrate a
convenient high-level pipeline to design, train and deploy arbitrary temporal
signal processing applications to sub-mW SNN inference hardware. We apply a new
straightforward SNN architecture designed for temporal signal processing, using
a pyramid of synaptic time constants to extract signal features at a range of
temporal scales. We demonstrate this architecture on an ambient audio
classification task, deployed to the Xylo SNN inference processor in streaming
mode. Our application achieves high accuracy (98%) and low latency (100ms) at
low power (<100W inference power). Our approach makes training and
deploying SNN applications available to ML engineers with general NN
backgrounds, without requiring specific prior experience with spiking NNs. We
intend for our approach to make Neuromorphic hardware and SNNs an attractive
choice for commercial low-power and edge signal processing applications.Comment: This submission has been removed by arXiv administrators because the
submitter did not have the authority to grant a license to the work at the
time of submissio
From Neural Arbors to Daisies
Pyramidal neurons in layers 2 and 3 of the neocortex collectively form an horizontal lattice of long-range, periodic axonal projections, known as the superficial patch system. The precise pattern of projections varies between cortical areas, but the patch system has nevertheless been observed in every area of cortex in which it has been sought, in many higher mammals. Although the clustered axonal arbors of single pyramidal cells have been examined in detail, the precise rules by which these neurons collectively merge their arbors remain unknown. To discover these rules, we generated models of clustered axonal arbors following simple geometric patterns. We found that models assuming spatially aligned but independent formation of each axonal arbor do not produce patchy labeling patterns for large simulated injections into populations of generated axonal arbors. In contrast, a model that used information distributed across the cortical sheet to generate axonal projections reproduced every observed quality of cortical labeling patterns. We conclude that the patch system cannot be built during development using only information intrinsic to single neurons. Information shared across the population of patch-projecting neurons is required for the patch system to reach its adult stat
Network insensitivity to parameter noise via adversarial regularization
Neuromorphic neural network processors, in the form of compute-in-memory
crossbar arrays of memristors, or in the form of subthreshold analog and
mixed-signal ASICs, promise enormous advantages in compute density and energy
efficiency for NN-based ML tasks. However, these technologies are prone to
computational non-idealities, due to process variation and intrinsic device
physics. This degrades the task performance of networks deployed to the
processor, by introducing parameter noise into the deployed model. While it is
possible to calibrate each device, or train networks individually for each
processor, these approaches are expensive and impractical for commercial
deployment. Alternative methods are therefore needed to train networks that are
inherently robust against parameter variation, as a consequence of network
architecture and parameters. We present a new adversarial network optimisation
algorithm that attacks network parameters during training, and promotes robust
performance during inference in the face of parameter variation. Our approach
introduces a regularization term penalising the susceptibility of a network to
weight perturbation. We compare against previous approaches for producing
parameter insensitivity such as dropout, weight smoothing and introducing
parameter noise during training. We show that our approach produces models that
are more robust to targeted parameter variation, and equally robust to random
parameter variation. Our approach finds minima in flatter locations in the
weight-loss landscape compared with other approaches, highlighting that the
networks found by our technique are less sensitive to parameter perturbation.
Our work provides an approach to deploy neural network architectures to
inference devices that suffer from computational non-idealities, with minimal
loss of performance. ..
Developmental Origin of Patchy Axonal Connectivity in the Neocortex: A Computational Model
Injections of neural tracers into many mammalian neocortical areas reveal a common patchy motif of clustered axonal projections. We studied in simulation a mathematical model for neuronal development in order to investigate how this patchy connectivity could arise in layer II/III of the neocortex. In our model, individual neurons of this layer expressed the activator-inhibitor components of a Gierer-Meinhardt reaction-diffusion system. The resultant steady-state reaction-diffusion pattern across the neuronal population was approximately hexagonal. Growth cones at the tips of extending axons used the various morphogens secreted by intrapatch neurons as guidance cues to direct their growth and invoke axonal arborization, so yielding a patchy distribution of arborization across the entire layer II/III. We found that adjustment of a single parameter yields the intriguing linear relationship between average patch diameter and interpatch spacing that has been observed experimentally over many cortical areas and species. We conclude that a simple Gierer-Meinhardt system expressed by the neurons of the developing neocortex is sufficient to explain the patterns of clustered connectivity observed experimentall
Embedding of Cortical Representations by the Superficial Patch System
Pyramidal cells in layers 2 and 3 of the neocortex of many species collectively form a clustered system of lateral axonal projections (the superficial patch systemâLund JS, Angelucci A, Bressloff PC. 2003. Anatomical substrates for functional columns in macaque monkey primary visual cortex. Cereb Cortex. 13:15-24. or daisy architectureâDouglas RJ, Martin KAC. 2004. Neuronal circuits of the neocortex. Annu Rev Neurosci. 27:419-451.), but the function performed by this general feature of the cortical architecture remains obscure. By comparing the spatial configuration of labeled patches with the configuration of responses to drifting grating stimuli, we found the spatial organizations both of the patch system and of the cortical response to be highly conserved between cat and monkey primary visual cortex. More importantly, the configuration of the superficial patch system is directly reflected in the arrangement of function across monkey primary visual cortex. Our results indicate a close relationship between the structure of the superficial patch system and cortical responses encoding a single value across the surface of visual cortex (self-consistent states). This relationship is consistent with the spontaneous emergence of orientation response-like activity patterns during ongoing cortical activity (Kenet T, Bibitchkov D, Tsodyks M, Grinvald A, Arieli A. 2003. Spontaneously emerging cortical representations of visual attributes. Nature. 425:954-956.). We conclude that the superficial patch system is the physical encoding of self-consistent cortical states, and that a set of concurrently labeled patches participate in a network of mutually consistent representations of cortical inpu
Bob Dylan's Phonographic Imagination
In this article I emphasize the deliberate and reflexive way that Bob Dylan has approached studio recording, sketching features of a phonographic aesthetic, to highlight a neglected aspect of Dylanâs creative practice and to counter the view of Dylan as primarily a âperforming artistâ, one who approaches the studio in a casual manner as a place to cut relatively spontaneous drafts of songs that are later developed on stage. Drawing on Evan Eisenbergâs discussion of the âart of phonographyâ, and the way recording radically separates a performance from its contexts of âoriginâ (allowing recordings to be taken into a private space and subjected to intense, repeated listening), I argue that studio practice, a recording aesthetic and the art of phonography are integral to Dylanâs songwriting. The process and practice of songwriting is realised through the act of recording and informed by listening to songs and performances from recordings, regardless of how much time is actually spent in the studio. Exploring how Dylanâs phonographic imagination has been shaped by folk, blues and pop sonorities, along with film music, I argue that recording should be integrated into discussions of Dylanâs art, alongside the attention devoted to lyrics, performance and biography
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