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1. Systematic generation of biophysically detailed models with generalization capability for non-spiking neurons

   https://doi.org/10.1371/journal.pone.0268380  

   Naudin, Loïs; Jiménez Laredo, Juan Luis; Liu, Qiang; Corson, Nathalie (May 2022, PLOS ONE)

   Guo, Daqing (Ed.)

   Unlike spiking neurons which compress continuous inputs into digital signals for transmitting information via action potentials, non-spiking neurons modulate analog signals through graded potential responses. Such neurons have been found in a large variety of nervous tissues in both vertebrate and invertebrate species, and have been proven to play a central role in neuronal information processing. If general and vast efforts have been made for many years to model spiking neurons using conductance-based models (CBMs), very few methods have been developed for non-spiking neurons. When a CBM is built to characterize the neuron behavior, it should be endowed with generalization capabilities ( i.e . the ability to predict acceptable neuronal responses to different novel stimuli not used during the model’s building). Yet, since CBMs contain a large number of parameters, they may typically suffer from a lack of such a capability. In this paper, we propose a new systematic approach based on multi-objective optimization which builds general non-spiking models with generalization capabilities. The proposed approach only requires macroscopic experimental data from which all the model parameters are simultaneously determined without compromise. Such an approach is applied on three non-spiking neurons of the nematode Caenorhabditis elegans ( C. elegans ), a well-known model organism in neuroscience that predominantly transmits information through non-spiking signals. These three neurons, arbitrarily labeled by convention as RIM, AIY and AFD, represent, to date, the three possible forms of non-spiking neuronal responses of C. elegans . 

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2. Biophysical modeling of the whole-cell dynamics of C. elegans motor and interneurons families

   https://doi.org/10.1371/journal.pone.0298105  

   Nicoletti, Martina; Chiodo, Letizia; Loppini, Alessandro; Liu, Qiang; Folli, Viola; Ruocco, Giancarlo; Filippi, Simonetta (March 2024, PLOS ONE)

   Cymbalyuk, Gennady S (Ed.)

   The nematodeCaenorhabditis elegansis a widely used model organism for neuroscience. Although its nervous system has been fully reconstructed, the physiological bases of single-neuron functioning are still poorly explored. Recently, many efforts have been dedicated to measuring signals fromC.elegansneurons, revealing a rich repertoire of dynamics, including bistable responses, graded responses, and action potentials. Still, biophysical models able to reproduce such a broad range of electrical responses lack. Realistic electrophysiological descriptions started to be developed only recently, merging gene expression data with electrophysiological recordings, but with a large variety of cells yet to be modeled. In this work, we contribute to filling this gap by providing biophysically accurate models of six classes ofC.elegansneurons, the AIY, RIM, and AVA interneurons, and the VA, VB, and VD motor neurons. We test our models by comparing computational and experimental time series and simulate knockout neurons, to identify the biophysical mechanisms at the basis of inter and motor neuron functioning. Our models represent a step forward toward the modeling ofC.elegansneuronal networks and virtual experiments on the nematode nervous system. 

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3. A Sparse and Spike‐Timing‐Based Adaptive Photoencoder for Augmenting Machine Vision for Spiking Neural Networks

   https://doi.org/10.1002/adma.202202535  

   Subbulakshmi_Radhakrishnan, Shiva; Chakrabarti, Shakya; Sen, Dipanjan; Das, Mayukh; Schranghamer, Thomas_F; Sebastian, Amritanand; Das, Saptarshi (July 2022, Advanced Materials)

   Abstract The representation of external stimuli in the form of action potentials or spikes constitutes the basis of energy efficient neural computation that emerging spiking neural networks (SNNs) aspire to imitate. With recent evidence suggesting that information in the brain is more often represented by explicit firing times of the neurons rather than mean firing rates, it is imperative to develop novel hardware that can accelerate sparse and spike‐timing‐based encoding. Here a medium‐scale integrated circuit composed of two cascaded three‐stage inverters and one XOR logic gate fabricated using a total of 21 memtransistors based on photosensitive 2D monolayer MoS₂ for spike‐timing‐based encoding of visual information, is introduced. It is shown that different illumination intensities can be encoded into sparse spiking with time‐to‐first‐spike representing the illumination information, that is, higher intensities invoke earlier spikes and vice versa. In addition, non‐volatile and analog programmability in the photoencoder is exploited for adaptive photoencoding that allows expedited spiking under scotopic (low‐light) and deferred spiking under photopic (bright‐light) conditions, respectively. Finally, low energy expenditure of less than 1 µJ by the 2D‐memtransistor‐based photoencoder highlights the benefits of in‐sensor and bioinspired design that can be transformative for the acceleration of SNNs. 

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4. Optimal noise level for coding with tightly balanced networks of spiking neurons in the presence of transmission delays

   https://doi.org/10.1371/journal.pcbi.1010593  

   Timcheck, Jonathan; Kadmon, Jonathan; Boahen, Kwabena; Ganguli, Surya (October 2022, PLOS Computational Biology)

   Blohm, Gunnar (Ed.)

   Neural circuits consist of many noisy, slow components, with individual neurons subject to ion channel noise, axonal propagation delays, and unreliable and slow synaptic transmission. This raises a fundamental question: how can reliable computation emerge from such unreliable components? A classic strategy is to simply average over a population ofNweakly-coupled neurons to achieve errors that scale as $1/\sqrt{N}$. But more interestingly, recent work has introduced networks of leaky integrate-and-fire (LIF) neurons that achieve coding errors that scalesuperclassicallyas 1/Nby combining the principles of predictive coding and fast and tight inhibitory-excitatory balance. However, spike transmission delays preclude such fast inhibition, and computational studies have observed that such delays can cause pathological synchronization that in turn destroys superclassical coding performance. Intriguingly, it has also been observed in simulations that noise can actuallyimprovecoding performance, and that there exists some optimal level of noise that minimizes coding error. However, we lack a quantitative theory that describes this fascinating interplay between delays, noise and neural coding performance in spiking networks. In this work, we elucidate the mechanisms underpinning this beneficial role of noise by derivinganalyticalexpressions for coding error as a function of spike propagation delay and noise levels in predictive coding tight-balance networks of LIF neurons. Furthermore, we compute the minimal coding error and the associated optimal noise level, finding that they grow as power-laws with the delay. Our analysis reveals quantitatively how optimal levels of noise can rescue neural coding performance in spiking neural networks with delays by preventing the build up of pathological synchrony without overwhelming the overall spiking dynamics. This analysis can serve as a foundation for the further study of precise computation in the presence of noise and delays in efficient spiking neural circuits. 

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5. Inhibitory feedback from the motor circuit gates mechanosensory processing in Caenorhabditis elegans

   https://doi.org/10.1371/journal.pbio.3002280  

   Kumar, Sandeep; Sharma, Anuj K; Tran, Andrew; Liu, Mochi; Leifer, Andrew M (September 2023, PLOS Biology)

   Sengupta, Piali (Ed.)

   Animals must integrate sensory cues with their current behavioral context to generate a suitable response. How this integration occurs is poorly understood. Previously, we developed high-throughput methods to probe neural activity in populations ofCaenorhabditis elegansand discovered that the animal’s mechanosensory processing is rapidly modulated by the animal’s locomotion. Specifically, we found that when the worm turns it suppresses its mechanosensory-evoked reversal response. Here, we report thatC.elegansuse inhibitory feedback from turning-associated neurons to provide this rapid modulation of mechanosensory processing. By performing high-throughput optogenetic perturbations triggered on behavior, we show that turning-associated neurons SAA, RIV, and/or SMB suppress mechanosensory-evoked reversals during turns. We find that activation of the gentle-touch mechanosensory neurons or of any of the interneurons AIZ, RIM, AIB, and AVE during a turn is less likely to evoke a reversal than activation during forward movement. Inhibiting neurons SAA, RIV, and SMB during a turn restores the likelihood with which mechanosensory activation evokes reversals. Separately, activation of premotor interneuron AVA evokes reversals regardless of whether the animal is turning or moving forward. We therefore propose that inhibitory signals from SAA, RIV, and/or SMB gate mechanosensory signals upstream of neuron AVA. We conclude thatC.elegansrely on inhibitory feedback from the motor circuit to modulate its response to sensory stimuli on fast timescales. This need for motor signals in sensory processing may explain the ubiquity in many organisms of motor-related neural activity patterns seen across the brain, including in sensory processing areas. 

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