Towards blueprints for network architecture, biophysical dynamics and signal transduction

2006 ◽  
Vol 364 (1849) ◽  
pp. 3301-3318 ◽  
Author(s):  
Stephen Coombes ◽  
Brent Doiron ◽  
Krešimir Josić ◽  
Eric Shea-Brown
Keyword(s):  
Signal Transduction ◽  
Decision Making ◽  
Dendritic Spines ◽  
Network Architecture ◽  
Single Neuron ◽  
Applied Mathematics ◽  
Coincidence Detection ◽  
Single Neurons ◽  
Systems Neuroscience ◽  
Neuron Coding

We review mathematical aspects of biophysical dynamics , signal transduction and network architecture that have been used to uncover functionally significant relations between the dynamics of single neurons and the networks they compose. We focus on examples that combine insights from these three areas to expand our understanding of systems neuroscience. These range from single neuron coding to models of decision making and electrosensory discrimination by networks and populations and also coincidence detection in pairs of dendrites and dynamics of large networks of excitable dendritic spines. We conclude by describing some of the challenges that lie ahead as the applied mathematics community seeks to provide the tools which will ultimately underpin systems neuroscience.

149 Human Subthalamic Nucleus Neurons Exhibit Increased Theta-band Phase-locking During High-conflict Decision Making

Neurosurgery ◽  
2017 ◽  
Vol 64 (CN_suppl_1) ◽  
pp. 236-236
Author(s):  
Sheng-Tzung Tsai ◽  
Todd M Herrington ◽  
Shaun Patel ◽  
Kristen Kanoff ◽  
Alik S Widge ◽  
...  
Keyword(s):  
Decision Making ◽  
Subthalamic Nucleus ◽  
Single Neuron ◽  
Neuron Activity ◽  
Single Neurons ◽  
Theta Band ◽  
Decision Conflict ◽  
High Conflict ◽  
Theta Frequency ◽  
Increased Activity

Abstract INTRODUCTION The subthalamic nucleus (STN) is thought to be preferentially engaged during high-conflict decision making in humans. The population neuronal spike rate in the STN has been reported to increase during decision conflict. Conflict and feedback-related activity is also reflected in theta-band (4-8 Hz) oscillations in the STN. It remains unknown how single-neuron activity and theta-band local field potentials (LFP) oscillations interact to support decision making. METHODS We simultaneously recorded single-neuron spike activity and LFP from the STN of eight Parkinson's disease (PD) patients while they performed a novel Aversion-Reward conflict (ARC) task. Subjects decide whether to accept an offer of a monetary reward paired with a variable risk of an aversive air puff to the eye. By varying the reward and risk, we are able to study approach-avoidance decision making across a range of conflict. Using this task, we examined the mechanism of how theta-frequency oscillation and entrained single neurons involve humans' integration of cost and benefit and decision at various conflict statuses. RESULTS >The ARC task reveals diverse risk-reward tradeoff strategies of patients. Consistent across patients, there is a positive correlation between the degree of decision conflict and reaction time (e.g., higher conflict offers require longer for subjects to decide). During high-conflict decisions, LFP in STN had increased activity of sub-theta oscillation, while increased activity of theta was found during low-conflict decisions. Single-trial STN theta-band power was correlated with degree of decision conflict. Interestingly, the decision to take or forgo the reward is predicted by theta-frequency phase-locked of STN neurons. CONCLUSION Our findings support the hypothesis that theta-band oscillations in single-neurons reflect the engagement of STN during conflict decision making. Furthermore, STN neurons with theta-band entrainment correlate with willingness to approach risk to pursue reward.

scholarly journals Autocorrelation structure at rest predicts value correlates of single neurons during reward-guided choice

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Sean E Cavanagh ◽  
Joni D Wallis ◽  
Steven W Kennerley ◽  
Laurence T Hunt
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Orbitofrontal Cortex ◽  
Receptive Fields ◽  
Neural Mechanism ◽  
Spike Rate ◽  
Accumulation Process ◽  
Single Neurons ◽  
Evidence Accumulation ◽  
Autocorrelation Structure

Correlates of value are routinely observed in the prefrontal cortex (PFC) during reward-guided decision making. In previous work (Hunt et al., 2015), we argued that PFC correlates of chosen value are a consequence of varying rates of a dynamical evidence accumulation process. Yet within PFC, there is substantial variability in chosen value correlates across individual neurons. Here we show that this variability is explained by neurons having different temporal receptive fields of integration, indexed by examining neuronal spike rate autocorrelation structure whilst at rest. We find that neurons with protracted resting temporal receptive fields exhibit stronger chosen value correlates during choice. Within orbitofrontal cortex, these neurons also sustain coding of chosen value from choice through the delivery of reward, providing a potential neural mechanism for maintaining predictions and updating stored values during learning. These findings reveal that within PFC, variability in temporal specialisation across neurons predicts involvement in specific decision-making computations.

An Applied Mathematical Model for Business Transformation and Enterprise Architecture

2020 ◽  
pp. 172-210
Keyword(s):  
Mathematical Model ◽  
Decision Making ◽  
Research Method ◽  
Enterprise Architecture ◽  
Financial Analysis ◽  
Applied Mathematics ◽  
Decision Making System ◽  
Information And Communication ◽  
Software Modelling ◽  
Business Engineering

This chapter proposes a cross-business domain holistic mathematical model (HMM) that is the result of a lifetime of research on business transformations, applied mathematics, software modelling, business engineering, financial analysis, and global enterprise architecture. This research is based on an authentic and proprietary mixed research method that is supported by an underlining mainly qualitative holistic reasoning model module. The proposed HMM formalism attempts to mimic some functions of the human brain, which uses empirical processes that are mainly based on the beam-search, like heuristic decision-making process. The HMM can be used to implement a decision-making system or an expert system that can integrate the enterprise's business, information, and communication technology environments.

Sensor-Based Decision Making in Uncertain Context

2017 ◽  
pp. 234-257
Author(s):  
Eric Villeneuve ◽  
François Pérès ◽  
Cedrik Beler ◽  
Vicente González-Prida
Keyword(s):  
Decision Making ◽  
Network Architecture ◽  
Measurement Errors ◽  
False Negative ◽  
Decision Makers ◽  
Organizational Network ◽  
Decision Aiding ◽  
External Impact ◽  
Information Collecting ◽  
Embedded Intelligence

Decision makers, whether human or computer, using sensor networks to instrument information collecting necessary for decision, often face difficulties in assessing confidence granted to signals transmitted and received in the network. Several organizational (network architecture or nature, distance between sensors ...), internal (sensor reliability or accuracy ...) or external (impact of environment ...) factors can lead to measurement errors (false alarm, non-detection by misinterpretation of the analyzed signals, false-negative …). A system-embedded intelligence is then necessary, to compare the information received for the purpose of decision aiding based on margin of errors converted in confidence intervals. In this chapter, the authors present four complementary approaches to quantify the interpretation of signals exchanged in a network of sensors in the presence of uncertainty.

scholarly journals Representation of actions and outcomes in medial prefrontal cortex during delayed conditional decision-making: Population analyses of single neuron activity

2018 ◽  
Vol 2 ◽  
pp. 239821281877386 ◽  
Author(s):  
Miranda J. Francoeur ◽  
Robert G. Mair
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Medial Prefrontal Cortex ◽  
Single Neuron ◽  
Neuron Activity ◽  
Related Activity ◽  
Response Options ◽  
Single Neuron Activity ◽  
Temporal Organisation ◽  
Matching To Position

Background: To respond adaptively in a dynamic environment, it is important for organisms to utilise information about recent events to decide between response options. Methods: To examine the role of medial prefrontal cortex in adaptive decision-making, we recorded single neuron activity in rats performing a dynamic delayed non-matching to position task. Results: We recorded activity from 1335 isolated neurons, 458 (34%) with criterion event-related activity, of which 431 (94%) exhibited 1 of 10 distinct excitatory response types: five at different times relative to delivery (or lack) of reinforcement following sample and choice responses and five correlated with movements or lever press actions that occurred multiple times in each trial. Normalised population averages revealed a precisely timed cascade of population responses representing the temporal organisation behavioural events that constitute delayed non-matching to position trials. Firing field analyses identified a subset of neurons with restricted spatial fields: responding to the conjunction of a behavioural event with a specific location. Anatomical analyses showed considerable overlap in the distribution of different response types in medial prefrontal cortex with a significant trend for dorsal areas to contain more neurons with action-related activity and ventral areas more responses related to action outcomes. Conclusion: These results indicate that medial prefrontal cortex contains discrete populations of neurons that represent the temporal organisation of actions and outcomes during delayed non-matching to position trials. They support the hypothesis that medial prefrontal cortex promotes flexible control of complex behaviours by action–outcome contingencies.

Probability and the Single Neuron: Decisions, Uncertainty and the Brain: The Science of Neuroeconomics, by P.-W. Glimcher. 2003. Cambridge, MA: MIT Press. 375 pp., $40.00.

2004 ◽  
Vol 10 (7) ◽  
pp. 1027-1029
Author(s):  
M. Kinsbourne
Keyword(s):  
Decision Making ◽  
Single Neuron ◽  
Prior Probability ◽  
Inclusive Fitness ◽  
Neural Circuitry ◽  
Inferior Parietal Lobule ◽  
Mathematical Methods ◽  
Economic Problems ◽  
Neural Substrate ◽  
The Brain

In an uncertain world, people and other animals make their living by predicting which of alternative courses of action is likely to yield the best return. For humans the return might take many forms, such as material, financial, social, or esthetic, but the underlying currency involved for any species is “inclusive fitness,” the rate at which an animal's genes are propagated. Professor Glimcher demonstrates that Economics methods are applicable to decision-making under conditions of uncertainty, both at the behavioral and the neuronal level. This approach has been called neuro-economics, although “econometrics” characterizes it more precisely. Econometrics is the application of statistical and mathematical methods in the field of economics to test and quantify economic theories and the solution to economic problems. Specifically, individuals' decision-making benefits from knowing how likely a response is to be reinforced, and knowing the reinforcement's value. Even single neurons are sensitive to these variables. Glimcher reaches beyond the heavily studied neural substrate for sensation and response to predictive neural circuitry that factors in the prior probability of reward, and its expected value. Indeed, he and his colleagues have identified neurons in monkey's inferior parietal lobule whose firing rates reflect both probability and value.

Information coding in the rodent prefrontal cortex. II. Ensemble activity in orbitofrontal cortex

1995 ◽  
Vol 74 (2) ◽  
pp. 751-762 ◽  
Author(s):  
G. Schoenbaum ◽  
H. Eichenbaum
Keyword(s):  
Orbitofrontal Cortex ◽  
Single Neuron ◽  
Activity Space ◽  
Behavioral Performance ◽  
Single Neurons ◽  
Reward Contingency ◽  
Neuron Firing ◽  
Firing Patterns ◽  
Neural Ensembles ◽  
Odor Discrimination Task

1. Neural activity was recorded from the orbitofrontal cortex (OF) of rats performing an eight-odor discrimination task that included predictable associations between particular odor pairs. A modified linear discriminant analysis was employed to characterize the population response in each trial of the task as a point in an N-dimensional activity space with the firing rate of each cell in the population represented on one of the N dimensions. The ability of the ensemble to discriminate among conditions of a variable was reflected in the tendency of population responses to cluster together in this activity space for repetitions of a given condition. We assessed coding of several variables describing the period of odor sampling, focusing on aspects of current, past, and future events reflected in single-neuron firing patterns, in ensembles composed of 22-138 cells active during the period when the rats sampled the discriminative stimulus in each trial. 2. OF ensembles performed well at discriminating variables with relevance to task demands represented in single-neuron firing patterns, specifically the physical attributes and assigned reward contingency of the current odor as well as the expectation of reward in the following trial that could be inferred from the predictable associations between particular pairs of odors. OF ensembles were able to correctly identify the identity and assigned reward contingency of the current odor in up to 52% (chance = 12.5%) and 99% (chance = 50%) of all trials, respectively, such that the observed behavioral performance required a population of 5,364 odor-responsive cells in the case of odor identity and only 40 cells in the case of valence. Expectations regarding upcoming rewards based on both assigned response contingency and associations between particular pairs of odors were correctly classified in up to 67% (chance = 20%) of all trials such that the observed level of behavioral performance required a population of 3,169 cells. 3. Other information represented in the single-neuron firing patterns, such as the identity and reward contingency of the preceding odor and specific odor-odor associations, was poorly encoded by OF ensembles. Thus neural ensembles in OF may represent only some of the information reflected in single-neuron activity. Stable coding of only the most useful and relevant information by the ensemble might emerge from the tuning properties of single neurons under the influence of the task at hand, producing in the well-trained animal the observed pattern of broad and diverse coding by single neurons and selective, task-relevant coding by neural ensembles in OF.

scholarly journals Emergent decision-making in biological signal transduction networks

2008 ◽  
Vol 105 (6) ◽  
pp. 1913-1918 ◽  
Author(s):  
T. Helikar ◽  
J. Konvalina ◽  
J. Heidel ◽  
J. A. Rogers
Keyword(s):  
Signal Transduction ◽  
Decision Making ◽  
Biological Signal ◽  
Signal Transduction Networks

Alternate Task Inhibits Single-neuron Category-selective Responses in the Human Hippocampus while Preserving Selectivity in the Amygdala

2009 ◽  
Vol 21 (2) ◽  
pp. 347-358 ◽  
Author(s):  
Peter N. Steinmetz
Keyword(s):  
Temporal Lobe ◽  
Video Game ◽  
Medial Temporal Lobe ◽  
Hippocampal Neurons ◽  
Single Neuron ◽  
Single Neurons ◽  
Human Epilepsy ◽  
Human Hippocampus ◽  
The Subject

One fifth of neurons in the medial-temporal lobe of human epilepsy patients respond selectively to categories of images, such as faces or cars. Here we show that responses of hippocampal neurons are rapidly modified as subjects alternate (over 60 sec) between two tasks (1) identifying images from a category, or (2) playing a simple video game superimposed on the same images. Category-selective responses, present when a subject identifies categories, are eliminated when the subject shifts to playing the game for 87% of category-selective hippocampal neurons. By contrast, responses in the amygdala are present during both tasks for 72% of category-selective amygdalar neurons. These results suggest that attention to images is required to evoke selective responses from single neurons in the hippocampus, but is not required by neurons in the amygdala.

scholarly journals Partially dissociable roles of OFC and ACC in stimulus-guided and action-guided decision making

2014 ◽  
Vol 111 (9) ◽  
pp. 1717-1720 ◽  
Author(s):  
Abbas Khani
Keyword(s):  
Decision Making ◽  
Anterior Cingulate Cortex ◽  
Orbitofrontal Cortex ◽  
Single Neuron ◽  
Cingulate Cortex ◽  
Anterior Cingulate ◽  
Functional Specialization ◽  
Complex Picture ◽  
Lesion Studies ◽  
The Brain

Recently, the functional specialization of prefrontal areas of the brain, and, specifically, the functional dissociation of the orbitofrontal cortex (OFC) and the anterior cingulate cortex (ACC), during decision making have become a particular focus of research. A number of neuropsychological and lesion studies have shown that the OFC and ACC have dissociable functions in various dimensions of decision making, which are supported by their different anatomical connections. A recent single-neuron study, however, described a more complex picture of the functional dissociation between these two frontal regions during decision making. Here, I discuss the results of that study and consider alternative interpretations in connection with other findings.

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