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Article ; Online: Transcranial Electrical Neurostimulation as a Potential Addiction Treatment.

Brown, Joshua W

Inquiry : a journal of medical care organization, provision and financing

2023 Volume 60, Page(s) 469580231221286

Abstract: Addiction remains difficult to treat, but non-invasive transcranial electrical and magnetic neurostimulation methods may provide promising and cost-effective treatment approaches. We provide a narrative review of recent developments and evidence of ... ...

Abstract	Addiction remains difficult to treat, but non-invasive transcranial electrical and magnetic neurostimulation methods may provide promising and cost-effective treatment approaches. We provide a narrative review of recent developments and evidence of effectiveness and consider newer technology that may yield improved treatment approaches. In particular, we review temporal interference electrical neurostimulation, which allows non-invasive and focal stimulation of deep brain regions. This provides a promising new potential approach to treat addiction, because many of the brain regions that seem most important for addiction are deeper in the brain, out of reach of existing technologies such as transcranial direct current stimulation.
MeSH term(s)	Humans ; Transcranial Direct Current Stimulation ; Behavior, Addictive/therapy ; Substance-Related Disorders/therapy
Language	English
Publishing date	2023-12-25
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	42153-4
ISSN	1945-7243 ; 0046-9580
ISSN (online)	1945-7243
ISSN	0046-9580
DOI	10.1177/00469580231221286
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Article ; Online: Foundations of human spatial problem solving.

Zarr, Noah / Brown, Joshua W

Scientific reports

2023 Volume 13, Issue 1, Page(s) 1485

Abstract: Despite great strides in both machine learning and neuroscience, we do not know how the human brain solves problems in the general sense. We approach this question by drawing on the framework of engineering control theory. We demonstrate a computational ... ...

Abstract	Despite great strides in both machine learning and neuroscience, we do not know how the human brain solves problems in the general sense. We approach this question by drawing on the framework of engineering control theory. We demonstrate a computational neural model with only localist learning laws that is able to find solutions to arbitrary problems. The model and humans perform a multi-step task with arbitrary and changing starting and desired ending states. Using a combination of computational neural modeling, human fMRI, and representational similarity analysis, we show here that the roles of a number of brain regions can be reinterpreted as interacting mechanisms of a control theoretic system. The results suggest a new set of functional perspectives on the orbitofrontal cortex, hippocampus, basal ganglia, anterior temporal lobe, lateral prefrontal cortex, and visual cortex, as well as a new path toward artificial general intelligence.
MeSH term(s)	Humans ; Problem Solving ; Prefrontal Cortex/diagnostic imaging ; Brain/diagnostic imaging ; Temporal Lobe ; Artificial Intelligence ; Brain Mapping ; Magnetic Resonance Imaging
Language	English
Publishing date	2023-01-27
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-28834-3
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Article: Monkey Prefrontal Cortex Learns to Minimize Sequence Prediction Error.

Cheng, Huzi / Chafee, Matthew V / Blackman, Rachael K / Brown, Joshua W

bioRxiv : the preprint server for biology

2024

Abstract: In this study, we develop a novel recurrent neural network (RNN) model of pre-frontal cortex that predicts sensory inputs, actions, and outcomes at the next time step. Synaptic weights in the model are adjusted to minimize sequence prediction error, ... ...

Abstract	In this study, we develop a novel recurrent neural network (RNN) model of pre-frontal cortex that predicts sensory inputs, actions, and outcomes at the next time step. Synaptic weights in the model are adjusted to minimize sequence prediction error, adapting a deep learning rule similar to those of large language models. The model, called Sequence Prediction Error Learning (SPEL), is a simple RNN that predicts world state at the next time step, but that differs from standard RNNs by using its own prediction errors from the previous state predictions as inputs to the hidden units of the network. We show that the time course of sequence prediction errors generated by the model closely matched the activity time courses of populations of neurons in macaque prefrontal cortex. Hidden units in the model responded to combinations of task variables and exhibited sensitivity to changing stimulus probability in ways that closely resembled monkey prefrontal neurons. Moreover, the model generated prolonged response times to infrequent, unexpected events as did monkeys. The results suggest that prefrontal cortex may generate internal models of the temporal structure of the world even during tasks that do not explicitly depend on temporal expectation, using a sequence prediction error minimization learning rule to do so. As such, the SPEL model provides a unified, general-purpose theoretical framework for modeling the lateral prefrontal cortex.
Language	English
Publishing date	2024-02-28
Publishing country	United States
Document type	Preprint
DOI	10.1101/2024.02.28.582611
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Book ; Online: Learning with augmented target information

Cheng, Huzi / Brown, Joshua W.

An alternative theory of Feedback Alignment

2023

Abstract: While error backpropagation (BP) has dominated the training of nearly all modern neural networks for a long time, it suffers from several biological plausibility issues such as the symmetric weight requirement and synchronous updates. Feedback Alignment ( ...

Abstract	While error backpropagation (BP) has dominated the training of nearly all modern neural networks for a long time, it suffers from several biological plausibility issues such as the symmetric weight requirement and synchronous updates. Feedback Alignment (FA) was proposed as an alternative to BP to address those dilemmas and has been demonstrated to be effective on various tasks and network architectures. Despite its simplicity and effectiveness, a satisfying explanation of how FA works across different architectures is still lacking. Here we propose a novel, architecture-agnostic theory of how FA works through the lens of information theory: Instead of approximating gradients calculated by BP with the same parameter, FA learns effective representations by embedding target information into neural networks to be trained. We show this through the analysis of FA dynamics in idealized settings and then via a series of experiments. Based on the implications of this theory, we designed three variants of FA and show their comparable performance on several tasks. These variants also account for some phenomena and theories in neuroscience such as predictive coding and representational drift.
Keywords	Quantitative Biology - Neurons and Cognition ; Computer Science - Machine Learning
Subject code	501
Publishing date	2023-04-03
Publishing country	us
Document type	Book ; Online
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Article: The tale of the neuroscientists and the computer: why mechanistic theory matters.

Brown, Joshua W

Frontiers in neuroscience

2014 Volume 8, Page(s) 349

Language	English
Publishing date	2014-10-31
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2411902-7
ISSN	1662-453X ; 1662-4548
ISSN (online)	1662-453X
ISSN	1662-4548
DOI	10.3389/fnins.2014.00349
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Article: Beyond conflict monitoring: Cognitive control and the neural basis of thinking before you act.

Brown, Joshua W

Current directions in psychological science

2014 Volume 22, Issue 3, Page(s) 179–185

Abstract: Cognitive control refers to the processes by which individual cognitive functions are coordinated in the service of higher level goals. The anterior cingulate cortex (ACC) in the middle front of the brain monitors performance, and it is activated when ... ...

Abstract	Cognitive control refers to the processes by which individual cognitive functions are coordinated in the service of higher level goals. The anterior cingulate cortex (ACC) in the middle front of the brain monitors performance, and it is activated when the need for control is greater, as in difficult situations or when errors occur. Since the late 1990s, the ACC has been thought to signal when there is internal conflict between competing action plans, so that the conflict can be resolved. More recently, an alternative model has reconceptualized the computational role of ACC as predicting and evaluating the likely outcomes of a planned action before actions are made. This new predicted response outcome (PRO) model accounts for a broader range of findings and suggests that the ACC might support the cognitive operations by which individuals can "think before you act" in order to avoid risky or otherwise poor choices.
Language	English
Publishing date	2014-10-29
Publishing country	United States
Document type	Journal Article
ZDB-ID	2026362-4
ISSN	1467-8721 ; 0963-7214
ISSN (online)	1467-8721
ISSN	0963-7214
DOI	10.1177/0963721412470685
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Article ; Online: Frontal cortex function as derived from hierarchical predictive coding.

Alexander, William H / Brown, Joshua W

Scientific reports

2018 Volume 8, Issue 1, Page(s) 3843

Abstract: The frontal lobes are essential for human volition and goal-directed behavior, yet their function remains unclear. While various models have highlighted working memory, reinforcement learning, and cognitive control as key functions, a single framework ... ...

Abstract	The frontal lobes are essential for human volition and goal-directed behavior, yet their function remains unclear. While various models have highlighted working memory, reinforcement learning, and cognitive control as key functions, a single framework for interpreting the range of effects observed in prefrontal cortex has yet to emerge. Here we show that a simple computational motif based on predictive coding can be stacked hierarchically to learn and perform arbitrarily complex goal-directed behavior. The resulting Hierarchical Error Representation (HER) model simulates a wide array of findings from fMRI, ERP, single-units, and neuropsychological studies of both lateral and medial prefrontal cortex. By reconceptualizing lateral prefrontal activity as anticipating prediction errors, the HER model provides a novel unifying account of prefrontal cortex function with broad implications for understanding the frontal cortex across multiple levels of description, from the level of single neurons to behavior.
MeSH term(s)	Computer Simulation ; Deep Learning ; Frontal Lobe/physiology ; Humans ; Learning/physiology ; Memory, Short-Term ; Models, Neurological ; Neural Pathways/physiology ; Neurons/physiology ; Prefrontal Cortex/physiology ; Proof of Concept Study ; Reinforcement (Psychology)
Language	English
Publishing date	2018-03-01
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-018-21407-9
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Article: A computational examination of the two-streams hypothesis: which pathway needs a longer memory?

Alipour, Abolfazl / Beggs, John M / Brown, Joshua W / James, Thomas W

Cognitive neurodynamics

2021 Volume 16, Issue 1, Page(s) 149–165

Abstract: The two visual streams hypothesis is a robust example of neural functional specialization that has inspired countless studies over the past four decades. According to one prominent version of the theory, the fundamental goal of the dorsal visual pathway ... ...

Abstract	The two visual streams hypothesis is a robust example of neural functional specialization that has inspired countless studies over the past four decades. According to one prominent version of the theory, the fundamental goal of the dorsal visual pathway is the transformation of retinal information for visually-guided motor behavior. To that end, the dorsal stream processes input using absolute (or veridical) metrics only when the movement is initiated, necessitating very little, or no, memory. Conversely, because the ventral visual pathway does not involve motor behavior (its output does not influence the real world), the ventral stream processes input using relative (or illusory) metrics and can accumulate or integrate sensory evidence over long time constants, which provides a substantial capacity for memory. In this study, we tested these relations between functional specialization, processing metrics, and memory by training identical recurrent neural networks to perform either a viewpoint-invariant object classification task or an orientation/size determination task. The former task relies on relative metrics, benefits from accumulating sensory evidence, and is usually attributed to the ventral stream. The latter task relies on absolute metrics, can be computed accurately in the moment, and is usually attributed to the dorsal stream. To quantify the amount of memory required for each task, we chose two types of neural network models. Using a long-short-term memory (LSTM) recurrent network, we found that viewpoint-invariant object categorization (object task) required a longer memory than orientation/size determination (orientation task). Additionally, to dissect this memory effect, we considered factors that contributed to longer memory in object tasks. First, we used two different sets of objects, one with self-occlusion of features and one without. Second, we defined object classes either strictly by visual feature similarity or (more liberally) by semantic label. The models required greater memory when features were self-occluded and when object classes were defined by visual feature similarity, showing that self-occlusion and visual similarity among object task samples are contributing to having a long memory. The same set of tasks modeled using modified leaky-integrator echo state recurrent networks (LiESN), however, did not replicate the results, except under some conditions. This may be because LiESNs cannot perform fine-grained memory adjustments due to their network-wide memory coefficient and fixed recurrent weights. In sum, the LSTM simulations suggest that longer memory is advantageous for performing viewpoint-invariant object classification (a putative ventral stream function) because it allows for interpolation of features across viewpoints. The results further suggest that orientation/size determination (a putative dorsal stream function) does not benefit from longer memory. These findings are consistent with the two visual streams theory of functional specialization. Supplementary information: The online version contains supplementary material available at 10.1007/s11571-021-09703-z.
Language	English
Publishing date	2021-08-10
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2276890-7
ISSN	1871-4099 ; 1871-4080
ISSN (online)	1871-4099
ISSN	1871-4080
DOI	10.1007/s11571-021-09703-z
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Article ; Online: Neural correlates of visual attention during risky decision evidence integration.

Purcell, John R / Jahn, Andrew / Fine, Justin M / Brown, Joshua W

NeuroImage

2021 Volume 234, Page(s) 117979

Abstract: Value-based decision-making is presumed to involve a dynamic integration process that supports assessing the potential outcomes of different choice options. Decision frameworks assume the value of a decision rests on both the desirability and risk ... ...

Abstract	Value-based decision-making is presumed to involve a dynamic integration process that supports assessing the potential outcomes of different choice options. Decision frameworks assume the value of a decision rests on both the desirability and risk surrounding an outcome. Previous work has highlighted neural representations of risk in the human brain, and their relation to decision choice. Key neural regions including the insula and anterior cingulate cortex (ACC) have been implicated in encoding the effects of risk on decision outcomes, including approach and avoidance. Yet, it remains unknown whether these regions are involved in the dynamic integration processes that precede and drive choice, and their relationship with ongoing attention. Here, we used concurrent fMRI and eye-tracking to discern neural activation related to visual attention preceding choice between sure-thing (i.e. safe) and risky gamble options. We found activation in both dorsal ACC (dACC) and posterior insula (PI) scaled in opposite directions with the difference in attention to risky rewards relative to risky losses. PI activation also differentiated foveations on both risky options (rewards and losses) relative to a sure-thing option. These findings point to ACC involvement in ongoing evaluation of risky but higher value options. The role of PI in risky outcomes points to a more general evaluative role in the decision-making that compares both safe and risky outcomes, irrespective of potential for gains or losses.
MeSH term(s)	Adult ; Attention/physiology ; Decision Making/physiology ; Eye-Tracking Technology ; Female ; Gambling/psychology ; Gyrus Cinguli/diagnostic imaging ; Gyrus Cinguli/physiology ; Humans ; Magnetic Resonance Imaging/methods ; Male ; Photic Stimulation/methods ; Risk-Taking ; Visual Perception/physiology ; Young Adult
Language	English
Publishing date	2021-03-23
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1147767-2
ISSN	1095-9572 ; 1053-8119
ISSN (online)	1095-9572
ISSN	1053-8119
DOI	10.1016/j.neuroimage.2021.117979
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Article ; Online: The PRO model accounts for the anterior cingulate cortex role in risky decision-making and monitoring.

Woo, Jae Hyung / Azab, Habiba / Jahn, Andrew / Hayden, Benjamin / Brown, Joshua W

Cognitive, affective & behavioral neuroscience

2022 Volume 22, Issue 5, Page(s) 952–968

Abstract: The anterior cingulate cortex (ACC) has been implicated in a number of functions, including performance monitoring and decision-making involving effort. The prediction of responses and outcomes (PRO) model has provided a unified account of much human and ...

Abstract	The anterior cingulate cortex (ACC) has been implicated in a number of functions, including performance monitoring and decision-making involving effort. The prediction of responses and outcomes (PRO) model has provided a unified account of much human and monkey ACC data involving anatomy, neurophysiology, EEG, fMRI, and behavior. We explored the computational nature of ACC with the PRO model, extending it to account specifically for both human and macaque monkey decision-making under risk, including both behavioral and neural data. We show that the PRO model can account for a number of additional effects related to outcome prediction, decision-making under risk, gambling behavior. In particular, we show that the ACC represents the variance of uncertain outcomes, suggesting a link between ACC function and mean-variance theories of decision making. The PRO model provides a unified account of a large set of data regarding the ACC.
MeSH term(s)	Decision Making/physiology ; Gambling/diagnostic imaging ; Gyrus Cinguli/diagnostic imaging ; Gyrus Cinguli/physiology ; Humans ; Magnetic Resonance Imaging ; Prefrontal Cortex/physiology
Language	English
Publishing date	2022-03-24
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2029088-3
ISSN	1531-135X ; 1530-7026
ISSN (online)	1531-135X
ISSN	1530-7026
DOI	10.3758/s13415-022-00992-3
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