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Constraints on the design of neuromorphic circuits set by the properties of neural population codes

NEUROMORPHIC COMPUTING AND ENGINEERING 2023年第1期3卷 012001-012001页

作者： Panzeri, Stefano Janotte, Ella Pequeno-Zurro, Alejandro Bonato, Jacopo Bartolozzi, Chiara Univ Med Ctr Hamburg Eppendorf UKE Dept Excellence Neural Informat Proc Hamburg Germany Ist Italiano Tecnol Ctr Human Technol CHT I-16153 Genoa Italy Ist Italiano Tecnol Ctr Robot & Intelligent Syst CRIS Genoa Italy Univ Bologna Dept Pharm & Biotechnol Bologna Italy

In the brain, information is encoded, transmitted and used to inform behaviour at the level of timing of action potentials distributed over population of neurons. To implement neural-like systems in silico, to emulate neural function, and to interface successfully with the brain, neuromorphic circuits need to encode information in a way compatible to that used by populations of neuron in the brain. To facilitate the cross-talk between neuromorphic engineering and neuroscience, in this review we first critically examine and summarize emerging recent findings about how population of neurons encode and transmit information. We examine the effects on encoding and readout of information for different features of neural population activity, namely the sparseness of neural representations, the heterogeneity of neural properties, the correlations among neurons, and the timescales (from short to long) at which neurons encode information and maintain it consistently over time. Finally, we critically elaborate on how these facts constrain the design of information coding in neuromorphic circuits. We focus primarily on the implications for designing neuromorphic circuits that communicate with the brain, as in this case it is essential that artificial and biological neurons use compatible neural codes. However, we also discuss implications for the design of neuromorphic systems for implementation or emulation of neural computation.

关键词： neural coding neuromorphic circuit design population coding

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Movement-specific signaling is differentially distributed across motor cortex layer 5 projection neuron classes

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CELL REPORTS 2022年第6期39卷 000-000页

作者： Currie, Stephen P. Ammer, Julian J. Premchand, Brian Dacre, Joshua Wu, Yufei Eleftheriou, Constantinos Colligan, Matt Clarke, Thomas Mitchell, Leah Faisal, A. Aldo Hennig, Matthias H. Duguid, Ian Univ Edinburgh Edinburgh Med Sch Biomed Sci Ctr Discovery Brain Sci Hugh Robson BldgGeorge Sq Edinburgh EH8 9XD Midlothian Scotland Univ Edinburgh Edinburgh Med Sch Biomed Sci Patrick Wild Ctr Hugh Robson BldgGeorge Sq Edinburgh EH8 9XD Midlothian Scotland Univ Edinburgh Edinburgh Med Sch Biomed Sci Simons Initiat Developing Brain Edinburgh EH8 9XD Midlothian Scotland Imperial Coll London Dept Bioengn London SW7 2AZ England Imperial Coll London Dept Comp London SW7 2AZ England MRC London Inst Med Sci London W12 0NN England Univ Edinburgh Sch Informat Inst Adapt & Neural Computat Edinburgh EH8 9AB Midlothian Scotland Univ Freiburg Fac Biol Optophysiol D-79100 Freiburg Germany ASTAR Infocomm Res 1 Fusionopolis Way21-01 Connexis South Tower Singapore 138632 Singapore

Motor cortex generates descending output necessary for executing a wide range of limb movements. Although movement-related activity has been described throughout motor cortex, the spatiotemporal organization of movement-specific signaling in deep layers remains largely unknown. Here we record layer 5B population dynamics in the caudal forelimb area of motor cortex while mice perform a forelimb push/ pull task and find that most neurons show movement-invariant responses, with a minority displaying movement specificity. Using cell-type-specific imaging, we identify that invariant responses dominate pyramidal tract (PT) neuron activity, with a small subpopulation representing movement type, whereas a larger proportion of intratelencephalic (IT) neurons display movement-type-specific signaling. The proportion of IT neurons decoding movement-type peaks prior to movement initiation, whereas for PT neurons, this occurs during movement execution. Our data suggest that layer 5B population dynamics largely reflect movement-invariant signaling, with information related to movement-type being routed through relatively small, distributed subpopulations of projection neurons.

关键词： motor cortex action-specific calcium imaging motor control layer 5B neural coding motor behavior decoding object manipulation cortical silencing

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Rational thoughts in neural codes

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2020年第47期117卷 29311-29320页

作者： Wu, Zhengwei Kwon, Minhae Daptardar, Saurabh Schrater, Paul Pitkow, Xaq Baylor Coll Med Dept Neurosci Houston TX 77030 USA Rice Univ Dept Elect & Comp Engn Houston TX 77030 USA Baylor Coll Med Ctr Neurosci & Artificial Intelligence Houston TX 77030 USA Google Atom Maps Geo Core Data Mountain View CA 94043 USA Univ Minnesota Dept Psychol Minneapolis MN 55455 USA Univ Minnesota Dept Comp Sci Minneapolis MN 55455 USA

Complex behaviors are often driven by an internal model, which integrates sensory information over time and facilitates long-term planning to reach subjective goals. A fundamental challenge in neuroscience is, How can we use behavior and neural activity to understand this internal model and its dynamic latent variables? Here we interpret behavioral data by assuming an agent behaves rationally-that is, it takes actions that optimize its subjective reward according to its understanding of the task and its relevant causal variables. We apply a method, inverse rational control (IRC), to learn an agent's internal model and reward function by maximizing the likelihood of its measured sensory observations and actions. This thereby extracts rational and interpretable thoughts of the agent from its behavior. We also provide a framework for interpreting encoding, recoding, and decoding of neural data in light of this rational model for behavior. When applied to behavioral and neural data from simulated agents performing suboptimally on a naturalistic foraging task, this method successfully recovers their internal model and reward function, as well as the Markovian computational dynamics within the neural manifold that represent the task. This work lays a foundation for discovering how the brain represents and computes with dynamic latent variables.

关键词： cognition neuroscience computation rational neural coding

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Design Space Exploration of Hardware Spiking Neurons for Embedded Artificial Intelligence

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neural NETWORKS 2020年 121卷 366-386页

作者： Abderrahmane, Nassim Lemaire, Edgar Miramond, Benoit Univ Cote Azur CNRS LEAT Nice France Thales Res Technol STI Grp LCHP Palaiseau France

Machine learning is yielding unprecedented interest in research and industry, due to recent success in many applied contexts such as image classification and object recognition. However, the deployment of these systems requires huge computing capabilities, thus making them unsuitable for embedded systems. To deal with this limitation, many researchers are investigating brain-inspired computing, which would be a perfect alternative to the conventional Von Neumann architecture based computers (CPU/GPU) that meet the requirements for computing performance, but not for energy-efficiency. Therefore, neuromorphic hardware circuits that are adaptable for both parallel and distributed computations need to be designed. In this paper, we focus on Spiking neural Networks (SNNs) with a comprehensive study of neural coding methods and hardware exploration. In this context, we propose a framework for neuromorphic hardware design space exploration, which allows to define a suitable architecture based on application-specific constraints and starting from a wide variety of possible architectural choices. For this framework, we have developed a behavioral level simulator for neuromorphic hardware architectural exploration named NAXT. Moreover, we propose modified versions of the standard Rate coding technique to make trade-offs with the Time coding paradigm, which is characterized by the low number of spikes propagating in the network. Thus, we are able to reduce the number of spikes while keeping the same neuron's model, which results in an SNN with fewer events to process. By doing so, we seek to reduce the amount of power consumed by the hardware. Furthermore, we present three neuromorphic hardware architectures in order to quantitatively study the implementation of SNNs. One of these architectures integrates a novel hybrid structure: a highly-parallel computation core for most solicited layers, and time-multiplexed computation units for deeper layers. These architectures are deri

关键词： Artificial intelligence Neuromorphic computing Spiking neural networks neural coding Embedded systems Power consumption

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Brain-inspired methods for achieving robust computation in heterogeneous mixed-signal neuromorphic processing systems

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NEUROMORPHIC COMPUTING AND ENGINEERING 2023年第3期3卷 034002-034002页

作者： Zendrikov, Dmitrii Solinas, Sergio Indiveri, Giacomo Univ Zurich Inst Neuroinformat Zurich Switzerland Swiss Fed Inst Technol Zurich Switzerland Univ Sassari Sassari Italy

Neuromorphic processing systems implementing spiking neural networks with mixed signal analog/digital electronic circuits and/or memristive devices represent a promising technology for edge computing applications that require low power, low latency, and that cannot connect to the cloud for off-line processing, either due to lack of connectivity or for privacy concerns. However, these circuits are typically noisy and imprecise, because they are affected by device-to-device variability, and operate with extremely small currents. So achieving reliable computation and high accuracy following this approach is still an open challenge that has hampered progress on the one hand and limited widespread adoption of this technology on the other. By construction, these hardware processing systems have many constraints that are biologically plausible, such as heterogeneity and non-negativity of parameters. More and more evidence is showing that applying such constraints to artificial neural networks, including those used in artificial intelligence, promotes robustness in learning and improves their reliability. Here we delve even more into neuroscience and present network-level brain-inspired strategies that further improve reliability and robustness in these neuromorphic systems: we quantify, with chip measurements, to what extent population averaging is effective in reducing variability in neural responses, we demonstrate experimentally how the neural coding strategies of cortical models allow silicon neurons to produce reliable signal representations, and show how to robustly implement essential computational primitives, such as selective amplification, signal restoration, working memory, and relational networks, exploiting such strategies. We argue that these strategies can be instrumental for guiding the design of robust and reliable ultra-low power electronic neural processing systems implemented using noisy and imprecise computing substrates such as subthreshold neuromorph

关键词： heterogeneity neural coding EI spiking networks neuromorphic hardware

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Finding the Brain in the Nose

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ANNUAL REVIEW OF NEUROSCIENCE, VOL 43 2020年第1期43卷 277-295页

作者： Brann, David H. Datta, Sandeep Robert Harvard Med Sch Dept Neurobiol Boston MA 02115 USA

Olfaction is fundamentally distinct from other sensory modalities. Natural odor stimuli are complex mixtures of volatile chemicals that interact in the nose with a receptor array that, in rodents, is built from more than 1,000 unique receptors. These interactions dictate a peripheral olfactory code, which in the brain is transformed and reformatted as it is broadcast across a set of highly interconnected olfactory regions. Here we discuss the problems of characterizing peripheral population codes for olfactory stimuli, of inferring the specific functions of different higher olfactory areas given their extensive recurrence, and of ultimately understanding how odor representations are linked to perception and action. We argue that, despite the differences between olfaction and other sensory modalities, addressing these specific questions will reveal general principles underlying brain function.

关键词： olfaction neural coding sensory representations

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Fractal spike dynamics and neuronal coupling in the primate visual system

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JOURNAL OF PHYSIOLOGY-LONDON 2020年第8期598卷 1551-1571页

作者： Munn, Brandon Zeater, Natalie Pietersen, Alexander N. Solomon, Samuel G. Cheong, Soon Keen Martin, Paul R. Gong, Pulin Univ Sydney Sch Phys Sydney NSW 2006 Australia Univ Sydney Australian Res Council Ctr Excellence Integrat Br Sydney NSW 2006 Australia Univ Sydney Save Sight Inst Eye Hosp Campus8 Macquarie St Sydney NSW 2001 Australia Univ Sydney Discipline Physiol Sydney NSW 2006 Australia UCL Dept Expt Psychol London WC1P 0AH England

Key points We measured fractal (self-similar) fluctuations in ongoing spiking activity in subcortical (lateral geniculate nucleus, LGN) and cortical (area MT) visual areas in anaesthetised marmosets. Cells in the evolutionary ancient koniocellular LGN pathway and in area MT show high-amplitude fractal fluctuations, whereas evolutionarily newer parvocellular and magnocellular LGN cells do not. Spiking activity in koniocellular cells and MT cells shows substantial correlation to the local population activity, whereas activity in parvocellular and magnocellular cells is less correlated with local activity. We develop a model consisting of a fractal process and a global rate modulation which can reproduce and explain the fundamental relationship between fractal fluctuations and population coupling in LGN and MT. The model provides a unified account of apparently disparate aspects of neural spiking activity and can improve our understanding of information processing in evolutionary ancient and modern visual pathways. The brain represents and processes information through patterns of spiking activity, which is influenced by local and widescale brain circuits as well as intrinsic neural dynamics. Whether these influences have independent or linked effects on spiking activity is, however, not known. Here we measured spiking activity in two visual centres, the lateral geniculate nucleus (LGN) and cortical area MT, in marmoset monkeys. By combining the Fano-factor time curve, power spectral analysis and rescaled range analysis, we reveal inherent fractal fluctuations of spiking activity in LGN and MT. We found that the evolutionary ancient koniocellular (K) pathway in LGN and area MT exhibits strong fractal fluctuations at short (<1 s) time scales. Parvocellular (P) and magnocellular (M) LGN cells show weaker fractal fluctuations at longer (multi-second) time scales. In both LGN and MT, the amplitude and time scale of fractal fluctuations can explain short and long time scale

关键词： fractal lateral geniculate nucleus neural coding visual cortex visual system

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Advantages and detection of phase coding in the absence of rhythmicity

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HIPPOCAMPUS 2020年第7期30卷 745-762页

作者： Bush, Daniel Burgess, Neil UCL Inst Cognit Neurosci London WC1N 3AZ England UCL Queen Sq Inst Neurol London England

The encoding of information in spike phase relative to local field potential (LFP) oscillations offers several theoretical advantages over equivalent firing rate codes. One notable example is provided by place and grid cells in the rodent hippocampal formation, which exhibit phase precession-firing at progressively earlier phases of the 6-12 Hz movement-related theta rhythm as their spatial firing fields are traversed. It is often assumed that such phase coding relies on a high amplitude baseline oscillation with relatively constant frequency. However, sustained oscillations with fixed frequency are generally absent in LFP and spike train recordings from the human brain. Hence, we examine phase coding relative to LFP signals with broadband low-frequency (2-20 Hz) power but without regular rhythmicity. We simulate a population of grid cells that exhibit phase precession against a baseline oscillation recorded from depth electrodes in human hippocampus. We show that this allows grid cell firing patterns to multiplex information about location, running speed and movement direction, alongside an arbitrary fourth variable encoded in LFP frequency. This is of particular importance given recent demonstrations that movement direction, which is essential for path integration, cannot be recovered from head direction cell firing rates. In addition, we investigate how firing phase might reduce errors in decoded location, including those arising from differences in firing rate across grid fields. Finally, we describe analytical methods that can identify phase coding in the absence of high amplitude LFP oscillations with approximately constant frequency, as in single unit recordings from the human brain and consistent with recent data from the flying bat. We note that these methods could also be used to detect phase coding outside of the spatial domain, and that multi-unit activity can substitute for the LFP signal. In summary, we demonstrate that the computational advantages off

关键词： entorhinal cortex grid cells hippocampus neural coding oscillations place cells

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Pairwise Synchrony and Correlations Depend on the Structure of the Population Code in Visual Cortex

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CELL REPORTS 2020年第6期33卷 108367-108367页

作者： Koren, Veronika Andrei, Ariana R. Hu, Ming Dragoi, Valentin Obermayer, Klaus Tech Univ Berlin Inst Software Engn & Theoret Comp Sci D-10587 Berlin Germany Bernstein Ctr Computat Neurosci Berlin Germany Univ Texas Houston Dept Neurobiol & Anat Sch Med Houston TX 77030 USA Tech Univ Berlin Inst Math D-10623 Berlin Germany Baylor Coll Med Houston TX 77030 USA

In visual areas of primates, neurons activate in parallel while the animal is engaged in a behavioral task. In this study, we examine the structure of the population code while the animal performs delayed match-to-sample tasks on complex natural images. The macaque monkeys visualized two consecutive stimuli that were either the same or different, while being recorded with laminar arrays across the cortical depth in cortical areas V1 and V4. We decode correct choice behavior from neural populations of simultaneously recorded units. Utilizing decoding weights, we divide neurons into most informative and less informative and show that most informative neurons in V4, but not in V1, are more strongly synchronized, coupled, and correlated than less informative neurons. Because neurons are divided into two coding pools according to their coding preference, in V4, but not in V1, spiking synchrony, coupling, and correlations within the coding pool are stronger than across coding pools.

关键词： monkey visual cortex discrimination choice behavior classification noise correlations neural coding population code information

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Turning Touch into Perception

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NEURON 2020年第1期105卷 16-33页

作者： Romo, Ranulfo Rossi-Pool, Roman Univ Nacl Autonoma Mexico Inst Fisiol Celular Neurociencias Mexico City 04510 DF Mexico Colegio Nacl Mexico City 06020 DF Mexico

Many brain areas modulate their activity during vibrotactile tasks. The activity from these areas may code the stimulus parameters, stimulus perception, or perceptual reports. Here, we discuss findings obtained in behaving monkeys aimed to understand these processes. In brief, neurons from the somatosensory thalamus and primary somatosensory cortex (S1) only code the stimulus parameters during the stimulation periods. In contrast, areas downstream of S1 code the stimulus parameters during not only the task components but also perception. Surprisingly, the midbrain dopamine system is an actor not considered before in perception. We discuss the evidence that it codes the subjective magnitude of a sensory percept. The findings reviewed here may help us to understand where and how sensation transforms into perception in the brain.

关键词： behaving monkeys psychophysics sensation perception working memory decision making somatosensory thalamus sensory and frontal cortices midbrain dopamine system neural coding microstimulation bimodal sensory coding population coding conscious sensory experience

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