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1. Columnar Learning Networks for Multisensory Spatiotemporal Learning

   https://doi.org/10.1002/aisy.202200179  

   Yoo, Sangmin; Park, Yongmo; Wang, Ziyu; Wu, Yuting; Medepalli, Saaketh; Thio, Wesley; Lu, Wei_D (October 2022, Advanced Intelligent Systems)

   Network features found in the brain may help implement more efficient and robust neural networks. Spiking neural networks (SNNs) process spikes in the spatiotemporal domain and can offer better energy efficiency than deep neural networks. However, most SNN implementations rely on simple point neurons that neglect the rich neuronal and dendritic dynamics. Herein, a bio‐inspired columnar learning network (CLN) structure that employs feedforward, lateral, and feedback connections to make robust classification with sparse data is proposed. CLN is inspired by the mammalian neocortex, comprising cortical columns each containing multiple minicolumns formed by interacting pyramidal neurons. A column continuously processes spatiotemporal signals from its sensor, while learning spatial and temporal correlations between features in different regions of an object along with the sensor's movement through sensorimotor interaction. CLN can be implemented using memristor crossbars with a local learning rule, spiking timing‐dependent plasticity (STDP), which can be natively obtained in second‐order memristors. CLN allows inputs from multiple sensors to be simultaneously processed by different columns, resulting in higher classification accuracy and better noise tolerance. Analysis of networks implemented on memristor crossbars shows that the system can operate at very low power and high throughput, with high accuracy and robustness to noise. 

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2. Variable but not random: temporal pattern coding in a songbird brain area necessary for song modification

   https://doi.org/10.1152/jn.00034.2019  

   Palmer, S. E.; Wright, B. D.; Doupe, A. J.; Kao, M. H. (February 2021, Journal of Neurophysiology)

   null (Ed.)

   Practice of a complex motor gesture involves motor exploration to attain a better match to target, but little is known about the neural code for such exploration. We examine spiking in a premotor area of the songbird brain critical for song modification and quantify correlations between spiking and time in the motor sequence. While isolated spikes code for time in song during performance of song to a female bird, extended strings of spiking and silence, particularly bursts, code for time in song during undirected (solo) singing, or “practice.” Bursts code for particular times in song with more information than individual spikes, and this spike-spike synergy is significantly higher during undirected singing. The observed pattern information cannot be accounted for by a Poisson model with a matched time-varying rate, indicating that the precise timing of spikes in both bursts in undirected singing and isolated spikes in directed singing code for song with a temporal code. Temporal coding during practice supports the hypothesis that lateral magnocellular nucleus of the anterior nidopallium neurons actively guide song modification at local instances in time. NEW & NOTEWORTHY This paper shows that bursts of spikes in the songbird brain during practice carry information about the output motor pattern. The brain’s code for song changes with social context, in performance versus practice. Synergistic combinations of spiking and silence code for time in the bird’s song. This is one of the first uses of information theory to quantify neural information about a motor output. This activity may guide changes to the song. 

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3. Unique scales preserve self-similar integrate-and-fire functionality of neuronal clusters

   https://doi.org/10.1038/s41598-021-82461-4  

   Amgalan, Anar; Taylor, Patrick; Mujica-Parodi, Lilianne R.; Siegelmann, Hava T. (March 2021, Scientific Reports)

   Abstract Brains demonstrate varying spatial scales of nested hierarchical clustering. Identifying the brain’s neuronal cluster size to be presented as nodes in a network computation is critical to both neuroscience and artificial intelligence, as these define the cognitive blocks capable of building intelligent computation. Experiments support various forms and sizes of neural clustering, from handfuls of dendrites to thousands of neurons, and hint at their behavior. Here, we use computational simulations with a brain-derived fMRI network to show that not only do brain networks remain structurally self-similar across scales but also neuron-like signal integration functionality (“integrate and fire”) is preserved at particular clustering scales. As such, we propose a coarse-graining of neuronal networks to ensemble-nodes, with multiple spikes making up its ensemble-spike and time re-scaling factor defining its ensemble-time step. This fractal-like spatiotemporal property, observed in both structure and function, permits strategic choice in bridging across experimental scales for computational modeling while also suggesting regulatory constraints on developmental and evolutionary “growth spurts” in brain size, as per punctuated equilibrium theories in evolutionary biology. 

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4. Integrating Temporal Information to Spatial Information in a Neural Circuit

   Wang, Mien Brabeea; Lynch, Nancy (January 2019, 7th Workshop on Biological Distributed Algorithms (BDA))

   In this paper, we consider a network of spiking neurons with a deterministic synchronous firing rule at discrete time. We propose three problems – “first consecutive spikes counting”,“total spikes counting” and “k-spikes temporal to spatial encoding” – to model how brains extract temporal information into spatial information from different neural codings. For a max input length T, we design three networks that solve these three problems with matching lower bounds in bothtime O(T) and number of neurons O(logT) in all three questions. 

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5. From Topological Analyses to Functional Modeling: The Case of Hippocampus

   https://doi.org/10.3389/fncom.2020.593166  

   Dabaghian, Yuri (January 2021, Frontiers in Computational Neuroscience)

   Sharpee, T (Ed.)

   Topological data analyses are widely used for describing and conceptualizing large volumes of neurobiological data, e.g., for quantifying spiking outputs of large neuronal ensembles and thus understanding the functions of the corresponding networks. Below we discuss an approach in which convergent topological analyses produce insights into how information may be processed in mammalian hippocampus—a brain part that plays a key role in learning and memory. The resulting functional model provides a unifying framework for integrating spiking data at different timescales and following the course of spatial learning at different levels of spatiotemporal granularity. This approach allows accounting for contributions from various physiological phenomena into spatial cognition—the neuronal spiking statistics, the effects of spiking synchronization by different brain waves, the roles played by synaptic efficacies and so forth. In particular, it is possible to demonstrate that networks with plastic and transient synaptic architectures can encode stable cognitive maps, revealing the characteristic timescales of memory processing. 

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