Brain Computation Is Organized via Power-of-Two-Based Permutation Logic

Role of Noise in Spontaneous Activity of Networks of Neurons on Patterned Silicon Emulated by Noise–activated CMOS Neural Nanoelectronic Circuits

Background noise in biological cortical microcircuits constitutes a powerful resource to assess their computational tasks, including, for instance, the synchronization of spiking activity, the enhancement of the speed of information transmission, and the minimization of the corruption of signals. We explore the correlation of spontaneous firing activity of ≈ 100 biological neurons adhering to engineered scaffolds by governing the number of functionalized patterned connection pathways among groups of neurons. We then emulate the biological system by a series of noise-activated silicon neural network simulations. We show that by suitably tuning both the amplitude of noise and the number of synapses between the silicon neurons, the same controlled correlation of the biological population is achieved. Our results extend to a realistic silicon nanoelectronics neuron design using noise injection to be exploited in artificial spiking neural networks such as liquid state machines and recurrent neural networks for stochastic computation.

Digital logic gates in soft, conductive mechanical metamaterials

Integrated circuits utilize networked logic gates to compute Boolean logic operations that are the foundation of modern computation and electronics. With the emergence of flexible electronic materials and devices, an opportunity exists to formulate digital logic from compliant, conductive materials. Here, we introduce a general method of leveraging cellular, mechanical metamaterials composed of conductive polymers to realize all digital logic gates and gate assemblies. We establish a method for applying conductive polymer networks to metamaterial constituents and correlate mechanical buckling modes with network connectivity. With this foundation, each of the conventional logic gates is realized in an equivalent mechanical metamaterial, leading to soft, conductive matter that thinks about applied mechanical stress. These findings may advance the growing fields of soft robotics and smart mechanical matter, and may be leveraged across length scales and physics.

A method to cultivate decision-making in soft materials would provide a key step to autonomous engineered matter. Here, the authors report a class of conductive polymer-based mechanical metamaterials that process information by digital logic and permit logic gate assembly.

A Vector Space Model for Neural Network Functions: Inspirations From Similarities Between the Theory of Connectivity and the Logarithmic Time Course of Word Production

The present report examines the coinciding results of two study groups each presenting a power-of-two function to describe network structures underlying perceptual processes in one case and word production during verbal fluency tasks in the other. The former is theorized as neural cliques organized according to the function N = 2 ⁱ - 1, whereas the latter assumes word conglomerations thinkable as tuples following the function N = 2 ⁱ . Both theories assume the innate optimization of energy efficiency to cause the specific connectivity structure. The vast resemblance between both formulae motivated the development of a common formulation. This was obtained by using a vector space model, in which the configuration of neural cliques or connected words is represented by a N-dimensional state vector. A further analysis of the model showed that the entire time course of word production could be derived using basically one single minimal transformation-matrix. This again seems in line with the principle of maximum energy efficiency.

Overcoming randomness does not rule out the importance of inherent randomness for functionality

Randomness is intrinsic to many natural processes. It is also clear that, under certain conditions, disorders are not associated with functionality. Several examples in which overcoming, suppressing, or combining both randomness and non-randomness is required are drawn from various fields. However, the need to suppress or overcome randomness does not negate its importance under certain conditions and its significance in valid processes and organ functions. Randomness should be acknowledged rather than ignored or suppressed; it can be viewed, at worst, as a disturbing disorder that may be treated to produce order, or, at best, as a 'beneficial disorder' that can be considered as a higher level of functionality.

Quantum-like behavior without quantum physics III : Logic and memory

We employ some of the machinery developed in previous work to investigate the inferential and memory functions of quantum-like neural networks. We set up a logical apparatus to implement this in the form of a Gentzen sequent calculus which codifies some of the combinatory rules for the state spaces of the neuronal networks introduced earlier. We discuss memory storage in this context and along the way find formal proof that synchronicity promotes binding and storage. These results lead to an algorithmic fragment in calculus that simulates the memory function known as pattern completion. This claim is tested by noting that the failure of certain steps in the algorithm leads to memory deficits essentially identical to those found in such pathologies as Alzheimer's dementia, schizophrenia, and certain forms of autism. Moreover, a specific "power-of-two" wiring architecture and computational logic, which have been postulated and observed across many brain circuits, emerge spontaneously from our model. We draw conclusions concerning the possible nature of such mental processes qua computations.

Sexually Dimorphic Control of Parenting Behavior by the Medial Amygdala

Social behaviors, including behaviors directed toward young offspring, exhibit striking sex differences. Understanding how these sexually dimorphic behaviors are regulated at the level of circuits and transcriptomes will provide insights into neural mechanisms of sex-specific behaviors. Here, we uncover a sexually dimorphic role of the medial amygdala (MeA) in governing parental and infanticidal behaviors. Contrary to traditional views, activation of GABAergic neurons in the MeA promotes parental behavior in females, while activation of this population in males differentially promotes parental versus infanticidal behavior in an activity-level-dependent manner. Through single-cell transcriptomic analysis, we found that molecular sex differences in the MeA are specifically represented in GABAergic neurons. Collectively, these results establish crucial roles for the MeA as a key node in the neural circuitry underlying pup-directed behaviors and provide important insight into the connection between sex differences across transcriptomes, cells, and circuits in regulating sexually dimorphic behavior.

Large-Scale Circuitry Interactions Upon Earthquake Experiences Revealed by Recurrent Neural Networks

Brain dynamics has recently received increasing interest due to its significant importance in basic and clinical neurosciences. However, due to inherent difficulties in both data acquisition and data analysis methods, studies on large-scale brain dynamics of mouse with local field potential (LFP) recording are very rare. In this paper, we did a series of works on modeling large-scale mouse brain dynamic activities responding to fearful earthquake. Based on LFP recording data from 13 brain regions that are closely related to fear learning and memory and the effective Bayesian connectivity change point model, we divided the response time series into four stages: "Before," "Earthquake," "Recovery," and "After." We first reported the changes in power and theta-gamma coupling during stage transitions. Then, a recurrent neural network model was designed to model the functional dynamics in these thirteen brain regions and six frequency bands in response to the fear stimulus. Interestingly, our results showed that the functional brain connectivities in theta and gamma bands exhibited distinct response processes: in theta band, there is a separated-united-separated alternation in whole-brain connectivity and a low-high-low change in connectivity strength; however, gamma bands have a united-separated-united transition and a high-low-high alternation in connectivity pattern and strength. In general, our results offer a novel perspective in studying functional brain dynamics under fearful stimulus and reveal its relationship to the brain's structural connectivity substrates.

Neural Coding of Appetitive Food Experiences in the Amygdala

Real-life experiences involve the consumption of various foods, yet it is unclear how the brain distinguishes and categorizes such food experiences. Despite the crucial roles of the basolateral amygdala (BLA) in appetitive behavior and emotion, how BLA pyramidal cells and interneurons encode food experiences has not yet been well characterized. Here we employ large-scale tetrode recording techniques to investigate the coding properties of pyramidal neurons vs. fast-spiking interneurons in the BLA as mice freely consumed a variety of foods, such as biscuits, rice, milk and water. We found that putative pyramidal cells conformed to the power-of-two-based permutation logic, as postulated by the Theory of Connectivity, to generate specific-to-general neural clique-coding patterns. Many pyramidal cells exhibited firing increases specific to a given food type, while some other pyramidal cells increased firings to various combinations of multiple foods. In contrast, fast-spiking interneurons can increase or decrease firings to given food types, and were more broadly tuned to various food experiences. We further show that a subset of pyramidal cells exhibited rapid desensitization to repeated eating of the same food, correlated with rapid behavioral habituation. Finally, we provide the intuitive visualization of BLA ensemble activation patterns using the dimensionality-reduction classification method to decode real-time appetitive stimulus identity on a moment-to-moment, single trial basis. Elucidation of the neural coding patterns in the BLA provides a key insight into how the brain's emotion and memory circuits performs the computational operation of pattern discrimination and categorization of natural food experiences.

Neural Coding of Cell Assemblies via Spike-Timing Self-Information

Cracking brain's neural code is of general interest. In contrast to the traditional view that enormous spike variability in resting states and stimulus-triggered responses reflects noise, here, we examine the "Neural Self-Information Theory" that the interspike-interval (ISI), or the silence-duration between 2 adjoining spikes, carries self-information that is inversely proportional to its variability-probability. Specifically, higher-probability ISIs convey minimal information because they reflect the ground state, whereas lower-probability ISIs carry more information, in the form of "positive" or "negative surprisals," signifying the excitatory or inhibitory shifts from the ground state, respectively. These surprisals serve as the quanta of information to construct temporally coordinated cell-assembly ternary codes representing real-time cognitions. Accordingly, we devised a general decoding method and unbiasedly uncovered 15 cell assemblies underlying different sleep cycles, fear-memory experiences, spatial navigation, and 5-choice serial-reaction time (5CSRT) visual-discrimination behaviors. We further revealed that robust cell-assembly codes were generated by ISI surprisals constituted of ~20% of the skewed ISI gamma-distribution tails, conforming to the "Pareto Principle" that specifies, for many events-including communication-roughly 80% of the output or consequences come from 20% of the input or causes. These results demonstrate that real-time neural coding arises from the temporal assembly of neural-clique members via silence variability-based self-information codes.

Multiple Approaches to the Investigation of Cell Assembly in Memory Research-Present and Future

In this review article we focus on research methodologies for detecting the actual activity of cell assemblies, which are populations of functionally connected neurons that encode information in the brain. We introduce and discuss traditional and novel experimental methods and those currently in development and briefly discuss their advantages and disadvantages for the detection of cell-assembly activity. First, we introduce the electrophysiological method, i.e., multineuronal recording, and review former and recent examples of studies showing models of dynamic coding by cell assemblies in behaving rodents and monkeys. We also discuss how the firing correlation of two neurons reflects the firing synchrony among the numerous surrounding neurons that constitute cell assemblies. Second, we review the recent outstanding studies that used the novel method of optogenetics to show causal relationships between cell-assembly activity and behavioral change. Third, we review the most recently developed method of live-cell imaging, which facilitates the simultaneous observation of firings of a large number of neurons in behaving rodents. Currently, all these available methods have both advantages and disadvantages, and no single measurement method can directly and precisely detect the actual activity of cell assemblies. The best strategy is to combine the available methods and utilize each of their advantages with the technique of operant conditioning of multiple-task behaviors in animals and, if necessary, with brain-machine interface technology to verify the accuracy of neural information detected as cell-assembly activity.

Normal CA1 Place Fields but Discoordinated Network Discharge in a Fmr1-Null Mouse Model of Fragile X Syndrome

Silence of FMR1 causes loss of fragile X mental retardation protein (FMRP) and dysregulated translation at synapses, resulting in the intellectual disability and autistic symptoms of fragile X syndrome (FXS). Synaptic dysfunction hypotheses for how intellectual disabilities like cognitive inflexibility arise in FXS predict impaired neural coding in the absence of FMRP. We tested the prediction by comparing hippocampus place cells in wild-type and FXS-model mice. Experience-driven CA1 synaptic function and synaptic plasticity changes are excessive in Fmr1-null mice, but CA1 place fields are normal. However, Fmr1-null discharge relationships to local field potential oscillations are abnormally weak, stereotyped, and homogeneous; also, discharge coordination within Fmr1-null place cell networks is weaker and less reliable than wild-type. Rather than disruption of single-cell neural codes, these findings point to invariant tuning of single-cell responses and inadequate discharge coordination within neural ensembles as a pathophysiological basis of cognitive inflexibility in FXS. VIDEO ABSTRACT.

Neural Code-Neural Self-information Theory on How Cell-Assembly Code Rises from Spike Time and Neuronal Variability

A major stumbling block to cracking the real-time neural code is neuronal variability - neurons discharge spikes with enormous variability not only across trials within the same experiments but also in resting states. Such variability is widely regarded as a noise which is often deliberately averaged out during data analyses. In contrast to such a dogma, we put forth the Neural Self-Information Theory that neural coding is operated based on the self-information principle under which variability in the time durations of inter-spike-intervals (ISI), or neuronal silence durations, is self-tagged with discrete information. As the self-information processor, each ISI carries a certain amount of information based on its variability-probability distribution; higher-probability ISIs which reflect the balanced excitation-inhibition ground state convey minimal information, whereas lower-probability ISIs which signify rare-occurrence surprisals in the form of extremely transient or prolonged silence carry most information. These variable silence durations are naturally coupled with intracellular biochemical cascades, energy equilibrium and dynamic regulation of protein and gene expression levels. As such, this silence variability-based self-information code is completely intrinsic to the neurons themselves, with no need for outside observers to set any reference point as typically used in the rate code, population code and temporal code models. Moreover, temporally coordinated ISI surprisals across cell population can inherently give rise to robust real-time cell-assembly codes which can be readily sensed by the downstream neural clique assemblies. One immediate utility of this self-information code is a general decoding strategy to uncover a variety of cell-assembly patterns underlying external and internal categorical or continuous variables in an unbiased manner.

Overlapping Representation of Primary Tastes in a Defined Region of the Gustatory Cortex

Both physiological and imaging approaches have led to often-disparate conclusions about the organization of taste information in gustatory cortex (GC). In this study, we used neuroanatomical and imaging approaches to delineate the likely area of insular cortex given to gustatory function and to characterize taste responses within this delineated area in female and male C57BL/6J mice. Anterograde tracers were injected into the taste thalamus (the medial parvicellular portion of the ventral posterior medial division, VPMpc) of mice and the thalamic terminal field was investigated across the cortex. Working within the delineated area, we used two-photon imaging to measure basic taste responses in >780 neurons in layer 2/3 located just posterior to the middle cerebral artery. A nonbiased, hierarchical cluster analysis revealed multiple clusters of cells responding best to either individual or combinations of taste stimuli. Taste quality was represented in the activity of taste-responsive cells; however, there was no apparent spatial organization of primary taste qualities in this region.SIGNIFICANCE STATEMENT Recent studies investigating taste coding within the gustatory cortex have reported highly segregated, taste-specific regions containing only narrowly tuned cells responding to a single taste separated by large non-taste-coding areas. However, focusing on the center of this area, we found a large number of taste responsive cells ranging from narrowly to broadly responsive with no apparent local spatial organization. Further, population analysis reveals that activity in the neuronal population in this area appears to be related to measures of taste quality or hedonics.

Overlapping Representation of Primary Tastes in a Defined Region of the Gustatory Cortex

Both physiological and imaging approaches have led to often-disparate conclusions about the organization of taste information in gustatory cortex (GC). In this study, we used neuroanatomical and imaging approaches to delineate the likely area of insular cortex given to gustatory function and to characterize taste responses within this delineated area in female and male C57BL/6J mice. Anterograde tracers were injected into the taste thalamus (the medial parvicellular portion of the ventral posterior medial division, VPMpc) of mice and the thalamic terminal field was investigated across the cortex. Working within the delineated area, we used two-photon imaging to measure basic taste responses in >780 neurons in layer 2/3 located just posterior to the middle cerebral artery. A nonbiased, hierarchical cluster analysis revealed multiple clusters of cells responding best to either individual or combinations of taste stimuli. Taste quality was represented in the activity of taste-responsive cells; however, there was no apparent spatial organization of primary taste qualities in this region.SIGNIFICANCE STATEMENT Recent studies investigating taste coding within the gustatory cortex have reported highly segregated, taste-specific regions containing only narrowly tuned cells responding to a single taste separated by large non-taste-coding areas. However, focusing on the center of this area, we found a large number of taste responsive cells ranging from narrowly to broadly responsive with no apparent local spatial organization. Further, population analysis reveals that activity in the neuronal population in this area appears to be related to measures of taste quality or hedonics.