Revealing ensemble state transition patterns in multi-electrode neuronal recordings using hidden Markov models

In order to harness the computational capacity of dissociated cultured neuronal networks, it is necessary to understand neuronal dynamics and connectivity on a mesoscopic scale. To this end, this paper uncovers dynamic spatiotemporal patterns emerging from electrically stimulated neuronal cultures using hidden Markov models (HMMs) to characterize multi-channel spike trains as a progression of patterns of underlying states of neuronal activity. However, experimentation aimed at optimal choice of parameters for such models is essential and results are reported in detail. Results derived from ensemble neuronal data revealed highly repeatable patterns of state transitions in the order of milliseconds in response to probing stimuli

Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience.

Identifying low-dimensional features that describe large-scale neural recordings is a major challenge in neuroscience. Repeated temporal patterns (sequences) are thought to be a salient feature of neural dynamics, but are not succinctly captured by traditional dimensionality reduction techniques. Here, we describe a software toolbox-called seqNMF-with new methods for extracting informative, non-redundant, sequences from high-dimensional neural data, testing the significance of these extracted patterns, and assessing the prevalence of sequential structure in data. We test these methods on simulated data under multiple noise conditions, and on several real neural and behavioral datas. In hippocampal data, seqNMF identifies neural sequences that match those calculated manually by reference to behavioral events. In songbird data, seqNMF discovers neural sequences in untutored birds that lack stereotyped songs. Thus, by identifying temporal structure directly from neural data, seqNMF enables dissection of complex neural circuits without relying on temporal references from stimuli or behavioral outputs

Temporally delayed linear modelling (TDLM) measures replay in both animals and humans

There are rich structures in off-task neural activity which are hypothesised to reflect fundamental computations across a broad spectrum of cognitive functions. Here, we develop an analysis toolkit - Temporal Delayed Linear Modelling (TDLM) for analysing such activity. TDLM is a domain-general method for finding neural sequences that respect a pre-specified transition graph. It combines nonlinear classification and linear temporal modelling to test for statistical regularities in sequences of task-related reactivations. TDLM is developed on the non-invasive neuroimaging data and is designed to take care of confounds and maximize sequence detection ability. Notably, as a linear framework, TDLM can be easily extended, without loss of generality, to capture rodent replay in electrophysiology, including in continuous spaces, as well as addressing second-order inference questions, e.g., its temporal and spatial varying pattern. We hope TDLM will advance a deeper understanding of neural computation and promote a richer convergence between animal and human neuroscience

Metastable attractors explain the variable timing of stable behavioral action sequences

info:eu-repo/semantics/publishedVersio

Spatiotemporal precision of neuroimaging in psychiatry

Aberrant patterns of cognition, perception, and behaviour seen in psychiatric disorders are thought to be driven by a complex interplay of neural processes that evolve at a rapid temporal scale. Understanding these dynamic processes in vivo in humans has been hampered by a trade-off between the spatial and temporal resolution inherent to current neuroimaging technology. A recent trend in psychiatric research has been the use of high temporal resolution imaging, particularly magnetoencephalography (MEG), often in conjunction with sophisticated machine learning decoding techniques. Developments here promise novel insights into the spatiotemporal dynamics of cognitive phenomena, including domains relevant to psychiatric illness such as reward and avoidance learning, memory, and planning. This review considers recent advances afforded by exploiting this increased spatiotemporal precision, with specific reference to applications the seek to drive a mechanistic understanding of psychopathology and the realisation of preclinical translation

Uncovering the Secrets of the Concept of Place in Cognitive Maps Aided by Artificial Intelligence

Uncovering how mental representations acquire, recall, and decode spatial information about relative locations and environmental attributes (cognitive map) involves different challenges. This work is geared towards theoretical discussions on the controversial issue of cognitive scalability for understanding cognitive map emergence from place and grid cells at the intersection between neuroscience and artificial intelligence. In our view, different place maps emerge from parallel and hierarchical neural structures supporting a global cognitive map. The mechanisms sustaining these maps do not only process sensory input but also assign the input to a location. Contentious issues are presented around these concepts and provide concrete suggestions for moving the field forward. We recommend approaching the described challenges guided by AI-based theoretical aspects of encoded place instead of based chiefly on technological aspects to study the brain. SIGNIFICANCE: A formal difference exists between the concepts of spatial representations between experimental neuroscientists and computer scientists and engineers in the so-called neural-based autonomous navigation field. From a neuroscience perspective, we consider the position of an organism’s body to be entirely determined by translational spatial information (e.g., visited places and velocities). An organism predicts where it is at a specific time using continuous or discrete spatial functions embedded into navigation systems. From these functions, we infer that the concept of place has emerged. However, from an engineering standpoint, we represent structured scaffolds of behavioral processes to determine movements from the organism’s current position to some other spatial locations. These scaffolds are certainly affected by the system’s designer. Therefore, the coding of place, in this case, is predetermined. The contrast between emergent cognitive map through inputs versus predefined spatial recognition between two fields creates an inconsistency. Clarifying this tension can inform us on how the brain encodes abstract knowledge to represent spatial positions, which hints at a universal theory of cognition.Fil: Fernandez Leon, Jose Alberto. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; ArgentinaFil: Acosta, Gerardo Gabriel. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; Argentin