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1. Diverse coactive neurons encode stimulus-driven and stimulus-independent variables

   https://doi.org/10.1152/jn.00431.2020  

   Xia, Ji; Marks, Tyler D.; Goard, Michael J.; Wessel, Ralf (November 2020, Journal of Neurophysiology)

   null (Ed.)

   Both experimenter-controlled stimuli and stimulus-independent variables impact cortical neural activity. A major hurdle to understanding neural representation is distinguishing between qualitatively different causes of the fluctuating population activity. We applied an unsupervised low-rank tensor decomposition analysis to the recorded population activity in the visual cortex of awake mice in response to repeated presentations of naturalistic visual stimuli. We found that neurons covaried largely independently of individual neuron stimulus response reliability and thus encoded both stimulus-driven and stimulus-independent variables. Importantly, a neuron’s response reliability and the neuronal coactivation patterns substantially reorganized for different external visual inputs. Analysis of recurrent balanced neural network models revealed that both the stimulus specificity and the mixed encoding of qualitatively different variables can arise from clustered external inputs. These results establish that coactive neurons with diverse response reliability mediate a mixed representation of stimulus-driven and stimulus-independent variables in the visual cortex. NEW & NOTEWORTHY V1 neurons covary largely independently of individual neuron’s response reliability. A single neuron’s response reliability imposes only a weak constraint on its encoding capabilities. Visual stimulus instructs a neuron’s reliability and coactivation pattern. Network models revealed using clustered external inputs. 

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2. Sparse RNNs can support high-capacity classification

   https://doi.org/10.1371/journal.pcbi.1010759  

   Turcu, Denis; Abbott, L. F. (December 2022, PLOS Computational Biology)

   Soltani, Alireza (Ed.)

   Feedforward network models performing classification tasks rely on highly convergent output units that collect the information passed on by preceding layers. Although convergent output-unit like neurons may exist in some biological neural circuits, notably the cerebellar cortex, neocortical circuits do not exhibit any obvious candidates for this role; instead they are highly recurrent. We investigate whether a sparsely connected recurrent neural network (RNN) can perform classification in a distributed manner without ever bringing all of the relevant information to a single convergence site. Our model is based on a sparse RNN that performs classification dynamically. Specifically, the interconnections of the RNN are trained to resonantly amplify the magnitude of responses to some external inputs but not others. The amplified and non-amplified responses then form the basis for binary classification. Furthermore, the network acts as an evidence accumulator and maintains its decision even after the input is turned off. Despite highly sparse connectivity, learned recurrent connections allow input information to flow to every neuron of the RNN, providing the basis for distributed computation. In this arrangement, the minimum number of synapses per neuron required to reach maximum memory capacity scales only logarithmically with network size. The model is robust to various types of noise, works with different activation and loss functions and with both backpropagation- and Hebbian-based learning rules. The RNN can also be constructed with a split excitation-inhibition architecture with little reduction in performance. 

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3. Primate V2 Receptive Fields Derived from Anatomically Identified Large-Scale V1 Inputs

   https://doi.org/10.21203/rs.3.rs-4139501/v1  

   Angelucci, Alessandra; Hassanpour, Mahlega; Merlin, Sam; Federer, Frederick; Zaidi, Qasim (March 2024, Research Square)

   Abstract In the primate visual system, visual object recognition involves a series of cortical areas arranged hierarchically along the ventral visual pathway. As information flows through this hierarchy, neurons become progressively tuned to more complex image features. The circuit mechanisms and computations underlying the increasing complexity of these receptive fields (RFs) remain unidentified. To understand how this complexity emerges in the secondary visual area (V2), we investigated the functional organization of inputs from the primary visual cortex (V1) to V2 by combining retrograde anatomical tracing of these inputs with functional imaging of feature maps in macaque monkey V1 and V2. We found that V1 neurons sending inputs to single V2 orientation columns have a broad range of preferred orientations, but are strongly biased towards the orientation represented at the injected V2 site. For each V2 site, we then constructed a feedforward model based on the linear combination of its anatomically- identified large-scale V1 inputs, and studied the response proprieties of the generated V2 RFs. We found that V2 RFs derived from the linear feedforward model were either elongated versions of V1 filters or had spatially complex structures. These modeled RFs predicted V2 neuron responses to oriented grating stimuli with high accuracy. Remarkably, this simple model also explained the greater selectivity to naturalistic textures of V2 cells compared to their V1 input cells. Our results demonstrate that simple linear combinations of feedforward inputs can account for the orientation selectivity and texture sensitivity of V2 RFs. 

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4. A case study in the functional consequences of scaling the sizes of realistic cortical models.

   https://doi.org/10.1371/journal.pcbi.1007198  

   Joglekar, M. R.; Chariker, L.; Shapley, R.; Young, L-S. (July 2019, PLoS computational biology)

   Tao, L (Ed.)

   Neuroscience models come in a wide range of scales and specificity, from mean-field rate models to large-scale networks of spiking neurons. There are potential trade-offs between simplicity and realism, versatility and computational speed. This paper is about large-scale cortical network models, and the question we address is one of scalability: would scaling down cell density impact a network’s ability to reproduce cortical dynamics and function? We investigated this problem using a previously constructed realistic model of the monkey visual cortex that is true to size. Reducing cell density gradually up to 50-fold, we studied changes in model behavior. Size reduction without parameter adjustment was catastrophic. Surprisingly, relatively minor compensation in synaptic weights guided by a theoretical algo- rithm restored mean firing rates and basic function such as orientation selectivity to models 10-20 times smaller than the real cortex. Not all was normal in the reduced model cortices: intracellular dynamics acquired a character different from that of real neurons, and while the ability to relay feedforward inputs remained intact, reduced models showed signs of defi- ciency in functions that required dynamical interaction among cortical neurons. These findings are not confined to models of the visual cortex, and modelers should be aware of potential issues that accompany size reduction. Broader implications of this study include the importance of homeostatic maintenance of firing rates, and the functional consequences of feedforward versus recurrent dynamics, ideas that may shed light on other species and on systems suffering cell loss. 

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5. 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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