scholarly journals Multi-task representations in human cortex transform along a sensory-to-motor hierarchy

2021 ◽  
Author(s):  
Takuya Ito ◽  
John D Murray
Keyword(s):  
Network Models ◽  
Signal Propagation ◽  
Human Cognition ◽  
Neural Network Models ◽  
Human Cortex ◽  
Cortical Topography ◽  
Structured Representations ◽  
Neural Processes ◽  
Motor Representations ◽  
Task Representations

Human cognition recruits diverse neural processes, yet the organizing computational and functional architectures remain unclear. Here, we characterized the geometry and topography of multi-task representations across human cortex using functional MRI during 26 cognitive tasks in the same subjects. We measured the representational similarity across tasks within a region, and the alignment of representations between regions. We found a cortical topography of representational alignment following a hierarchical sensory-association-motor gradient, revealing compression-then-expansion of multi-task dimensionality along this gradient. To investigate computational principles of multi-task representations, we trained multi-layer neural network models to transform empirical visual to motor representations. Compression-then-expansion organization in models emerged exclusively in a training regime where internal representations are highly optimized for sensory-to-motor transformation, and not under generic signal propagation. This regime produces hierarchically structured representations similar to empirical cortical patterns. Together, these results reveal computational principles that organize multi-task representations across human cortex to support flexible cognition.

Computationalism

2012 ◽  
Author(s):  
Gualtiero Piccinini
Keyword(s):  
Neural Network ◽  
Explanatory Power ◽  
Network Models ◽  
Turing Machines ◽  
Neural Network Models ◽  
Causal Account ◽  
Semantic Account ◽  
Neural Processes ◽  
Processing Information ◽  
Empirical Hypothesis

The introduction of the concept of computation in cognitive science is discussed in this article. Computationalism is usually introduced as an empirical hypothesis that can be disconfirmed. Processing information is surely an important aspect of cognition so if computation is information processing, then cognition involves computation. Computationalism becomes more significant when it has explanatory power. The most relevant and explanatory notion of computation is that associated with digital computers. Turing analyzed computation in terms of what are now called Turing machines that are the kind of simple processor operating on an unbounded tape. Turing stated that any function that can be computed by an algorithm could be computed by a Turing machine. McCulloch and Pitts's account of cognition contains three important aspects that include an analogy between neural processes and digital computations, the use of mathematically defined neural networks as models, and an appeal to neurophysiological evidence to support their neural network models. Computationalism involves three accounts of computation such as causal, semantic, and mechanistic. There are mappings between any physical system and at least some computational descriptions under the causal account. The semantic account may be formulated as a restricted causal account.

scholarly journals Strategic Cognitive Sequencing: A Computational Cognitive Neuroscience Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-18 ◽  
Author(s):  
Seth A. Herd ◽  
Kai A. Krueger ◽  
Trenton E. Kriete ◽  
Tsung-Ren Huang ◽  
Thomas E. Hazy ◽  
...  
Keyword(s):  
Cognitive Process ◽  
Network Models ◽  
Brain Regions ◽  
Human Cognition ◽  
Neural Network Models ◽  
Future Directions ◽  
Error Learning ◽  
Cognitive Actions ◽  
Self Instruction ◽  
The Brain

We address strategic cognitive sequencing, the “outer loop” of human cognition: how the brain decides what cognitive process to apply at a given moment to solve complex, multistep cognitive tasks. We argue that this topic has been neglected relative to its importance for systematic reasons but that recent work on how individual brain systems accomplish their computations has set the stage for productively addressing how brain regions coordinate over time to accomplish our most impressive thinking. We present four preliminary neural network models. The first addresses how the prefrontal cortex (PFC) and basal ganglia (BG) cooperate to perform trial-and-error learning of short sequences; the next, how several areas of PFC learn to make predictions of likely reward, and how this contributes to the BG making decisions at the level of strategies. The third models address how PFC, BG, parietal cortex, and hippocampus can work together to memorize sequences of cognitive actions from instruction (or “self-instruction”). The last shows how a constraint satisfaction process can find useful plans. The PFC maintains current and goal states and associates from both of these to find a “bridging” state, an abstract plan. We discuss how these processes could work together to produce strategic cognitive sequencing and discuss future directions in this area.

scholarly journals A continuous-time neural model for sequential action

2014 ◽  
Vol 369 (1655) ◽  
pp. 20130623 ◽  
Author(s):  
George Kachergis ◽  
Dean Wyatte ◽  
Randall C. O'Reilly ◽  
Roy de Kleijn ◽  
Bernhard Hommel
Keyword(s):  
Continuous Time ◽  
Network Models ◽  
Neural Model ◽  
Learning Mechanisms ◽  
Neural Network Models ◽  
Continuous Processes ◽  
Sequential Action ◽  
Directed Learning ◽  
Bidirectional Associations ◽  
Task Representations

Action selection, planning and execution are continuous processes that evolve over time, responding to perceptual feedback as well as evolving top-down constraints. Existing models of routine sequential action (e.g. coffee- or pancake-making) generally fall into one of two classes: hierarchical models that include hand-built task representations, or heterarchical models that must learn to represent hierarchy via temporal context, but thus far lack goal-orientedness. We present a biologically motivated model of the latter class that, because it is situated in the Leabra neural architecture, affords an opportunity to include both unsupervised and goal-directed learning mechanisms. Moreover, we embed this neurocomputational model in the theoretical framework of the theory of event coding (TEC), which posits that actions and perceptions share a common representation with bidirectional associations between the two. Thus, in this view, not only does perception select actions (along with task context), but actions are also used to generate perceptions (i.e. intended effects). We propose a neural model that implements TEC to carry out sequential action control in hierarchically structured tasks such as coffee-making. Unlike traditional feedforward discrete-time neural network models, which use static percepts to generate static outputs, our biological model accepts continuous-time inputs and likewise generates non-stationary outputs, making short-timescale dynamic predictions.

scholarly journals The Development of Working Memory

2020 ◽  
Vol 29 (6) ◽  
pp. 545-553
Author(s):  
John P. Spencer
Keyword(s):  
Neural Network ◽  
At Risk ◽  
Working Memory ◽  
Computer Simulations ◽  
Working Memory Capacity ◽  
Network Models ◽  
Memory Capacity ◽  
Neural Network Models ◽  
Neural Processes ◽  
Shed Light

Working memory is a central cognitive system that plays a key role in development, with working memory capacity and speed of processing increasing as children move from infancy through adolescence. Here, I focus on two questions: What neural processes underlie working memory, and how do these processes change over development? Answers to these questions lie in computer simulations of neural-network models that shed light on how development happens. These models open up new avenues for optimizing clinical interventions aimed at boosting the working memory abilities of at-risk infants.

scholarly journals Thunderstruck: The ACDC model of flexible sequences and rhythms in recurrent neural circuits

2021 ◽  
Author(s):  
Cristian Buc Calderon ◽  
Tom Verguts ◽  
Michael Joshua Frank
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Human Cognition ◽  
Dependent Manner ◽  
Neural Network Models ◽  
Cortical Dynamics ◽  
Temporal Properties ◽  
Wide Range ◽  
The Moment ◽  
Multiple Sequences

Adaptive sequential behavior is a hallmark of human cognition. In particular, humans can learn to produce precise spatiotemporal sequences given a certain context. For instance, musicians can not only reproduce learned action sequences in a context-dependent manner, they can also quickly and flexibly reapply them in any desired tempo or rhythm without overwriting previous learning. Existing neural network models fail to account for these properties. We argue that this limitation emerges from the fact that order information (i.e., the position of the action) and timing (i.e., the moment of response execution) are typically stored in the same neural network weights. Here, we augment a biologically plausible recurrent neural network of cortical dynamics to include a basal ganglia-thalamic module which uses reinforcement learning to dynamically modulate action. This associative cluster-dependent chain (ACDC) model modularly stores order and timing information in distinct loci of the network. This feature increases computational power and allows ACDC to display a wide range of temporal properties (e.g., multiple sequences, temporal shifting, rescaling, and compositionality), while still accounting for several behavioral and neurophysiological empirical observations. Finally, we apply this ACDC network to show how it can learn the famous Thunderstruck song and then flexibly play it in a bossa nova rhythm without further training.

scholarly journals Constructing neural network models from brain data reveals representational transformations underlying adaptive behavior

2020 ◽  
Author(s):  
Takuya Ito ◽  
Guangyu Robert Yang ◽  
Patryk Laurent ◽  
Douglas H. Schultz ◽  
Michael W. Cole
Keyword(s):  
Neural Network ◽  
Functional Connectivity ◽  
Network Models ◽  
Brain Regions ◽  
Neural Network Models ◽  
Human Ability ◽  
Cognitive Information ◽  
Brain Data ◽  
Motor Representations

AbstractThe human ability to adaptively implement a wide variety of tasks is thought to emerge from the dynamic transformation of cognitive information. We hypothesized that these transformations are implemented via conjunctive representations in conjunction hubs – brain regions that selectively integrate sensory, cognitive, and motor representations. We used recent advances in using functional connectivity to map the flow of activity between brain regions to construct a task-performing neural network model from fMRI data during a cognitive control task. We verified the importance of conjunction hubs in cognitive computations by simulating neural activity flow over this empirically-estimated functional connectivity model. These simulations produced above-chance task performance (motor responses) by integrating sensory and task rule information in conjunction hubs. These findings reveal the role of conjunction hubs in supporting flexible cognitive computations, while demonstrating the feasibility of using empirically-estimated neural network models to gain insight into cognitive computations in the human brain.

Method of complex copper-zinc ore typification using neural network models

2020 ◽  
Vol 5 ◽  
pp. 140-147 ◽  
Author(s):  
T.N. Aleksandrova ◽  
◽  
E.K. Ushakov ◽  
A.V. Orlova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Copper Zinc ◽  
Complex Copper

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

Measuring the World

2018 ◽  
Author(s):  
Ann-Sophie Barwich
Keyword(s):  
Sensory Information ◽  
Network Models ◽  
Model System ◽  
Stimulus Input ◽  
Expectancy Effects ◽  
Neural Network Models ◽  
Perceptual Object ◽  
External Objects ◽  
The Common ◽  
The Brain

How much does stimulus input shape perception? The common-sense view is that our perceptions are representations of objects and their features and that the stimulus structures the perceptual object. The problem for this view concerns perceptual biases as responsible for distortions and the subjectivity of perceptual experience. These biases are increasingly studied as constitutive factors of brain processes in recent neuroscience. In neural network models the brain is said to cope with the plethora of sensory information by predicting stimulus regularities on the basis of previous experiences. Drawing on this development, this chapter analyses perceptions as processes. Looking at olfaction as a model system, it argues for the need to abandon a stimulus-centred perspective, where smells are thought of as stable percepts, computationally linked to external objects such as odorous molecules. Perception here is presented as a measure of changing signal ratios in an environment informed by expectancy effects from top-down processes.

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