Categorial compositionality II: universal constructions and a general theory of (quasi-)systematicity in human cognition

A complete theory of cognitive architecture (i.e., the basic processes and modes of composition that together constitute cognitive behaviour) must explain the systematicity property--why our cognitive capacities are organized into particular groups of capacities, rather than some other, arbitrary collection. The classical account supposes: (1) syntactically compositional representations; and (2) processes that are sensitive to--compatible with--their structure. Classical compositionality, however, does not explain why these two components must be compatible; they are only compatible by the ad hoc assumption (convention) of employing the same mode of (concatenative) compositionality (e.g., prefix/postfix, where a relation symbol is always prepended/appended to the symbols for the related entities). Architectures employing mixed modes do not support systematicity. Recently, we proposed an alternative explanation without ad hoc assumptions, using category theory. Here, we extend our explanation to domains that are quasi-systematic (e.g., aspects of most languages), where the domain includes some but not all possible combinations of constituents. The central category-theoretic construct is an adjunction involving pullbacks, where the primary focus is on the relationship between processes modelled as functors, rather than the representations. A functor is a structure-preserving map (or construction, for our purposes). An adjunction guarantees that the only pairings of functors are the systematic ones. Thus, (quasi-)systematicity is a necessary consequence of a categorial cognitive architecture whose basic processes are functors that participate in adjunctions.     

Generative models of rich clubs in Hebbian neuronal networks and large-scale human brain networks

Rich clubs arise when nodes that are ‘rich’ in connections also form an elite, densely connected ‘club’. In brain networks, rich clubs incur high physical connection costs but also appear to be especially valuable to brain function. However, little is known about the selection pressures that drive their formation. Here, we take two complementary approaches to this question: firstly we show, using generative modelling, that the emergence of rich clubs in large-scale human brain networks can be driven by an economic trade-off between connection costs and a second, competing topological term. Secondly we show, using simulated neural networks, that Hebbian learning rules also drive the emergence of rich clubs at the microscopic level, and that the prominence of these features increases with learning time. These results suggest that Hebbian learning may provide a neuronal mechanism for the selection of complex features such as rich clubs. The neural networks that we investigate are explicitly Hebbian, and we argue that the topological term in our model of large-scale brain connectivity may represent an analogous connection rule. This putative link between learning and rich clubs is also consistent with predictions that integrative aspects of brain network organization are especially important for adaptive behaviour.     

Does dynamical synchronization among neurons facilitate learning and enhance task performance?

Synchronization among groups of neurons is an interesting yet mysterious mechanism in the brain. We propose and demonstrate that the adjustable timing of neural activities can produce profound effect on learning and task implementation. On one hand, learning of more complex patterns becomes possible because of the enhanced capability of classification. On the other hand, implementation of a complex task is aided through active maintenance and control of multiple rules and items. This sheds light on the development of new intelligent system, as well as the cause of impaired learning and task performance in patients.     

Categorial Compositionality: A Category Theory Explanation for the Systematicity of Human Cognition

Classical and Connectionist theories of cognitive architecture seek to explain systematicity (i.e., the property of human cognition whereby cognitive capacity comes in groups of related behaviours) as a consequence of syntactically and functionally compositional representations, respectively. However, both theories depend on ad hoc assumptions to exclude specific instances of these forms of compositionality (e.g. grammars, networks) that do not account for systematicity. By analogy with the Ptolemaic (i.e. geocentric) theory of planetary motion, although either theory can be made to be consistent with the data, both nonetheless fail to fully explain it. Category theory, a branch of mathematics, provides an alternative explanation based on the formal concept of adjunction, which relates a pair of structure-preserving maps, called functors. A functor generalizes the notion of a map between representational states to include a map between state transformations (or processes). In a formal sense, systematicity is a necessary consequence of a higher-order theory of cognitive architecture, in contrast to the first-order theories derived from Classicism or Connectionism. Category theory offers a re-conceptualization for cognitive science, analogous to the one that Copernicus provided for astronomy, where representational states are no longer the center of the cognitive universe—replaced by the relationships between the maps that transform them.     

The McGill-Mouse-Miniscope platform: A standardized approach for high-throughput imaging of neuronal dynamics during behavior

Understanding the rules that govern neuronal dynamics throughout the brain to subserve behavior and cognition remains one of the biggest challenges in neuroscience research. Recent technical advances enable the recording of increasingly larger neuronal populations to produce increasingly more sophisticated datasets. Despite bold and important open-science and data-sharing policies, these datasets tend to include unique data acquisition methods, behaviors, and file structures. Discrepancies between experimental protocols present key challenges in comparing data between laboratories and across different brain regions and species. Here, we discuss our recent efforts to create a standardized and high-throughput research platform to address these issues. The McGill-Mouse-Miniscope (M3) platform is an initiative to combine miniscope calcium imaging with standardized touchscreen-based animal behavioral testing. The goal is to curate an open-source and standardized framework for acquiring, analyzing, and accessing high-quality data of the neuronal dynamics that underly cognition throughout the brain in mice, marmosets, and models of disease. We end with a discussion of future developments and a call for users to adopt this standardized approach.     

Brain plasticity and ion channels

It is generally believed that spatio-temporal configurations of distributed activity in the brain contribute to the coding of neuronal information and that synaptic contacts between nerve cells could play a central role in the formation of privileged pathways of activity. Synaptic plasticity is not the only mode of regulation of information processing in the brain and persistent regulations of ionic conductances in some specialized neuronal areas such as the dendrites, the cell body and the axon could also modulate, in the short- and the long-term, the propagation of information in the brain. Persistent changes in intrinsic excitability have been reported in several brain areas in which activity is modified during a classical conditioning. The role of synaptic activity seems to be determinant in the induction but the learning rules and the underlying mechanisms remain to be defined. This review discusses the role of neuronal activity in the induction of intrinsic plasticity in cortical, hippocampal and cerebellar neurons. Activation and inactivation properties of ionic channels in the axon determine the short-term dynamics of axonal propagation and synaptic transmission. Activation of glutamate receptors initiates a long-term modification in neuronal excitability that may represent the substrate for the mnesic engram and for the stabilization of the epileptic state. Similarly to synaptic plasticity, long-lasting intrinsic plasticity appears to be reversible and to express a certain level of input or cellular specificity. These non-synaptic forms of plasticity affect the signal propagation in the axon, the dendrites and the soma. They not only share common learning rules and induction pathways with the better known synaptic plasticity such as NMDA receptor-dependent LTP and LTD but also contribute in synergy with these synaptic changes to the formation of a coherent mnesic engram.     

Beyond the sentence given

A central and influential idea among researchers of language is that our language faculty is organized according to Fregean compositionality, which states that the meaning of an utterance is a function of the meaning of its parts and of the syntactic rules by which these parts are combined. Since the domain of syntactic rules is the sentence, the implication of this idea is that language interpretation takes place in a two-step fashion. First, the meaning of a sentence is computed. In a second step, the sentence meaning is integrated with information from prior discourse, world knowledge, information about the speaker and semantic information from extra-linguistic domains such as co-speech gestures or the visual world. Here, we present results from recordings of event-related brain potentials that are inconsistent with this classical two-step model of language interpretation. Our data support a one-step model in which knowledge about the context and the world, concomitant information from other modalities, and the speaker are brought to bear immediately, by the same fast-acting brain system that combines the meanings of individual words into a message-level representation. Underlying the one-step model is the immediacy assumption, according to which all available information will immediately be used to co-determine the interpretation of the speaker's message. Functional magnetic resonance imaging data that we collected indicate that Broca's area plays an important role in semantic unification. Language comprehension involves the rapid incorporation of information in a 'single unification space', coming from a broader range of cognitive domains than presupposed in the standard two-step model of interpretation.     

The octopus vertical lobe modulates short-term learning rate and uses LTP to acquire long-term memory

Analyzing the processes and neuronal circuitry involved in complex behaviors in phylogenetically remote species can help us understand the evolution and function of these systems. Cephalopods, with their vertebrate-like behaviors but much simpler brains, are ideal for such an analysis. The vertical lobe (VL) of Octopus vulgaris is a pivotal brain station in its learning and memory system. To examine the organization of the learning and memory circuitry and to test whether the LTP that we discovered in the VL is involved in behavioral learning, we tetanized the VL to induce a global synaptic enhancement of the VL pathway. The effects of tetanization on learning and memory of a passive avoidance task were compared to those of transecting the same pathway. Tetanization accelerated and transection slowed short-term learning to avoid attacking a negatively reinforced object. However, both treatments impaired long-term recall the next day. Our results suggest that the learning and memory system in the octopus, as in mammals [9], is separated into short- and long-term memory sites. In the octopus, the two memory sites are not independent; the VL, which mediates long-term memory acquisition through LTP, also modulates the circuitry controlling behavior and short-term learning.     

Do Brain Networks Evolve by Maximizing Their Information Flow Capacity?

We propose a working hypothesis supported by numerical simulations that brain networks evolve based on the principle of the maximization of their internal information flow capacity. We find that synchronous behavior and capacity of information flow of the evolved networks reproduce well the same behaviors observed in the brain dynamical networks of Caenorhabditis elegans and humans, networks of Hindmarsh-Rose neurons with graphs given by these brain networks. We make a strong case to verify our hypothesis by showing that the neural networks with the closest graph distance to the brain networks of Caenorhabditis elegans and humans are the Hindmarsh-Rose neural networks evolved with coupling strengths that maximize information flow capacity. Surprisingly, we find that global neural synchronization levels decrease during brain evolution, reflecting on an underlying global no Hebbian-like evolution process, which is driven by no Hebbian-like learning behaviors for some of the clusters during evolution, and Hebbian-like learning rules for clusters where neurons increase their synchronization.     

Analysis of the effect of backward masking by means of a complex model of a net of neuron-like elements

According to the experiments with a projective-associative model of the neuronal net, the phenomenon of “backward masking” of the first stimulus of a pair of stimuli at a small time gap between the stimuli is caused by two events: (1) pre-excitation inhibition of the first stimulus-induced activation by the second stimulus and (2) disturbance of information processing connected with the deficiency of time needed to match the recalled symbol in memory to the symbol presented to the input subsystem and also to name it. Identification of the second stimulus may be impaired with a decreasing time interval due to: (1) superposition of the second (2) recurrent inhibition occurring in the neuronal net upon recognition of the first stimulus. It was found that in conditions of activity of neuron-like elements of the neuronal net, simulating the states of somnolence or slow-wave sleep, corresponding subsystems failed to learn, while time needed to identify already “learned” symbols substantially increased. The data obtained are in agreement with the hypothesis concerning the causes of backward masking and also with the facts on optimal conditions of learning and reproducing its results in living nervous system. It seems reasonable that discussed disturbances of information processing should be kept in mind in designing computers of a new generation, based on the use of principles of brain functioning, in order to increase the reliability and operation speed of technical systems.     

体外培养的神经元网络可塑性

神经元网络是大脑执行高级认知行为的结构基础,研究证明学习记忆及神经退行性疾病与神经元网络可塑性密切相关。因此,揭示调控和改变神经元网络可塑性的机制对理解神经系统信息交互以及疾病治疗具有重大意义。目前,基于微电极阵列（microelectrode array, MEA）培养的神经元网络是体外探究学习和记忆机制的理想模型,同时针对该模型的研究为预防和治疗神经退行性疾病提供了独特的视角。本文综述了基于MEA采集体外培养神经元网络的放电信号来构建功能网络的相关研究,分别从二维神经元网络和三维脑类器官发育,以及开环和闭环电刺激对神经元网络可塑性影响的角度,总结了体外培养神经元网络可塑性的相关研究,最后对该方向的应用前景进行了展望。     

多巴胺调节海马神经元可塑性的研究进展

多巴胺是脑内重要的信息传递物质,不仅可以作为递质释放到前额叶、伏隔核等脑区,直接进行信息传递,也可以作为调质调节其它突触递质的传递,并影响神经元可塑性。海马参与构成边缘系统,受多巴胺能神经支配,执行着有关学习记忆以及空间定位的功能。海马神经元的可塑性是学习记忆的细胞分子基础。研究表明,多巴胺对海马神经元的突触可塑性和兴奋性可塑性都具有重要的调节作用。本文扼要综述多巴胺对海马神经元突触可塑性和兴奋性可塑性的调节机制的研究进展,以期为DA系统参与海马区学习记忆功能的研究提供新思路,更深入地了解学习记忆的神经机制。     

Generalizable brain network markers of major depressive disorder across multiple imaging sites

Many studies have highlighted the difficulty inherent to the clinical application of fundamental neuroscience knowledge based on machine learning techniques. It is difficult to generalize machine learning brain markers to the data acquired from independent imaging sites, mainly due to large site differences in functional magnetic resonance imaging. We address the difficulty of finding a generalizable marker of major depressive disorder (MDD) that would distinguish patients from healthy controls based on resting-state functional connectivity patterns. For the discovery dataset with 713 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a machine learning MDD classifier. The classifier achieved an approximately 70% generalization accuracy for an independent validation dataset with 521 participants from 5 different imaging sites. The successful generalization to a perfectly independent dataset acquired from multiple imaging sites is novel and ensures scientific reproducibility and clinical applicability.

Biomarkers for psychiatric disorders based on neuroimaging data have yet to be put to practical use. This study overcomes the problems of inter-site differences in fMRI data by using a novel harmonization method, thereby successfully constructing a generalizable brain network marker of major depressive disorder across multiple imaging sites.     

What we see is how we are: new paradigms in visual research

As most sensory modalities, the visual system needs to deal with very fast changes in the environment. Instead of processing all sensory stimuli, the brain is able to construct a perceptual experience by combining selected sensory input with an ongoing internal activity. Thus, the study of visual perception needs to be approached by examining not only the physical properties of stimuli, but also the brain's ongoing dynamical states onto which these perturbations are imposed. At least three different models account for this internal dynamics. One model is based on cardinal cells where the activity of few cells by itself constitutes the neuronal correlate of perception, while a second model is based on a population coding that states that the neuronal correlate of perception requires distributed activity throughout many areas of the brain. A third proposition, known as the temporal correlation hypothesis states that the distributed neuronal populations that correlate with perception, are also defined by synchronization of the activity on a millisecond time scale. This would serve to encode contextual information by defining relations between the features of visual objects. If temporal properties of neural activity are important to establish the neural mechanisms of perception, then the study of appropriate dynamical stimuli should be instrumental to determine how these systems operate. The use of natural stimuli and natural behaviors such as free viewing, which features fast changes of internal brain states as seen by motor markers, is proposed as a new experimental paradigm to study visual perception.     

Simple decision rules for classifying human cancers from gene expression profiles

MOTIVATION: Various studies have shown that cancer tissue samples can be successfully detected and classified by their gene expression patterns using machine learning approaches. One of the challenges in applying these techniques for classifying gene expression data is to extract accurate, readily interpretable rules providing biological insight as to how classification is performed. Current methods generate classifiers that are accurate but difficult to interpret. This is the trade-off between credibility and comprehensibility of the classifiers. Here, we introduce a new classifier in order to address these problems. It is referred to as k-TSP (k-Top Scoring Pairs) and is based on the concept of 'relative expression reversals'. This method generates simple and accurate decision rules that only involve a small number of gene-to-gene expression comparisons, thereby facilitating follow-up studies. RESULTS: In this study, we have compared our approach to other machine learning techniques for class prediction in 19 binary and multi-class gene expression datasets involving human cancers. The k-TSP classifier performs as efficiently as Prediction Analysis of Microarray and support vector machine, and outperforms other learning methods (decision trees, k-nearest neighbour and na?ve Bayes). Our approach is easy to interpret as the classifier involves only a small number of informative genes. For these reasons, we consider the k-TSP method to be a useful tool for cancer classification from microarray gene expression data. AVAILABILITY: The software and datasets are available at http://www.ccbm.jhu.edu CONTACT: actan@jhu.edu.     

Autonomous learning based on cost assumptions: theoretical studies and experiments in robot control

Autonomous learning techniques are based on experience acquisition. In most realistic applications, experience is time-consuming: it implies sensor reading, actuator control and algorithmic update, constrained by the learning system dynamics. The information crudeness upon which classical learning algorithms operate make such problems too difficult and unrealistic. Nonetheless, additional information for facilitating the learning process ideally should be embedded in such a way that the structural, well-studied characteristics of these fundamental algorithms are maintained. We investigate in this article a more general formulation of the Q-learning method that allows for a spreading of information derived from single updates towards a neighbourhood of the instantly visited state and converges to optimality. We show how this new formulation can be used as a mechanism to safely embed prior knowledge about the structure of the state space, and demonstrate it in a modified implementation of a reinforcement learning algorithm in a real robot navigation task.     

Autonomous learning based on cost assumptions: theoretical studies and experiments in robot control

Autonomous learning techniques are based on experience acquisition. In most realistic applications, experience is time-consuming: it implies sensor reading, actuator control and algorithmic update, constrained by the learning system dynamics. The information crudeness upon which classical learning algorithms operate make such problems too difficult and unrealistic. Nonetheless, additional information for facilitating the learning process ideally should be embedded in such a way that the structural, well-studied characteristics of these fundamental algorithms are maintained. We investigate in this article a more general formulation of the Q-learning method that allows for a spreading of information derived from single updates towards a neighbourhood of the instantly visited state and converges to optimality. We show how this new formulation can be used as a mechanism to safely embed prior knowledge about the structure of the state space, and demonstrate it in a modified implementation of a reinforcement learning algorithm in a real robot navigation task.