12 research outputs found
Automated mapping of virtual environments with visual predictive coding
Humans construct internal cognitive maps of their environment directly from
sensory inputs without access to a system of explicit coordinates or distance
measurements. While machine learning algorithms like SLAM utilize specialized
visual inference procedures to identify visual features and construct spatial
maps from visual and odometry data, the general nature of cognitive maps in the
brain suggests a unified mapping algorithmic strategy that can generalize to
auditory, tactile, and linguistic inputs. Here, we demonstrate that predictive
coding provides a natural and versatile neural network algorithm for
constructing spatial maps using sensory data. We introduce a framework in which
an agent navigates a virtual environment while engaging in visual predictive
coding using a self-attention-equipped convolutional neural network. While
learning a next image prediction task, the agent automatically constructs an
internal representation of the environment that quantitatively reflects
distances. The internal map enables the agent to pinpoint its location relative
to landmarks using only visual information.The predictive coding network
generates a vectorized encoding of the environment that supports vector
navigation where individual latent space units delineate localized, overlapping
neighborhoods in the environment. Broadly, our work introduces predictive
coding as a unified algorithmic framework for constructing cognitive maps that
can naturally extend to the mapping of auditory, sensorimotor, and linguistic
inputs
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Establishing a Gold Standard for Noninvasive Identification of Painful Lumbar Discs: Prospective Comparison of Magnetic Resonance Spectroscopy vs Low-Pressure Provocation Discography.
PURPOSE: Verifying lumbar disc pain can present a clinical challenge. Low-pressure provocative discography (PD) has served as the gold standard, although it is invasive and often a challenge to interpret. We reported that magnetic resonance spectroscopy (MRS) biomarkers accurately predict PD results in lumbar discs and improved outcomes for patients with surgery at positive MRS levels versus nonsurgery. To further substantiate MRS for diagnosing painful discs, we report a prospective comparison of 2 MRS-derived measures: NOCISCORE (pain) and SI-SCORE (degeneration severity). METHODS: Lumbar MRS and software-based postprocessing (NOCISCAN-LS, Aclarion Inc.) was performed in 44 discs in 14 patients (prospective cohort [PC]). PC data were compared to prior data used to establish the NOCISCORE (training cohort [TC]). The NOCISCORE was converted to an ordinal value (high/intermediate/low; NOCI+/mild/-) and compared against painful (P) versus nonpainful (NP) control diagnosis (PD) for 19 discs where PD was performed in the PC (12 NP; 7 P). Sensitivity, specificity, and positive and negative predictive values were calculated. The SI-SCORE was compared against MRI Pfirrmann Grades for 465 discs in 126 patients (PC plus TC). RESULTS: For the PC, MRS (NOCI+/-) compared to PD (P/NP) with an accuracy of 87% and sensitivity and specificity of 100%. The positive and negative predictive values of MRS were 100%. NOCISCOREs were significantly higher for PD+ versus PD- discs for PC and TC (P < 0.05), and the NOCISCORE distributions for PD+/- group were not statistically different between the PC and TC (P > 0.05). SI-SCORES differed between Pfirrmann Grades 1 and 2 (less degenerated) versus Grades 3 and 4 (more degenerated; P < 0.05), with a progressively decreasing trend with Pfirrmann Grades 1-5. CONCLUSION: These current data provide prospective confirmation of the predictive value of disc MRS for distinguishing painful discs and for assessing the disc structural integrity. CLINICAL RELEVANCE: NOCISCAN is an adoptable, noninvasive, and objectively quantitative test to improve management of low back pain patients
Neural Networks with Recurrent Generative Feedback
Neural networks are vulnerable to input perturbations such as additive noise and adversarial attacks. In contrast, human perception is much more robust to such perturbations. The Bayesian brain hypothesis states that human brains use an internal generative model to update the posterior beliefs of the sensory input. This mechanism can be interpreted as a form of self-consistency between the maximum a posteriori (MAP) estimation of an internal generative model and the external environment. Inspired by such hypothesis, we enforce self-consistency in neural networks by incorporating generative recurrent feedback. We instantiate this design
on convolutional neural networks (CNNs). The proposed framework, termed Convolutional Neural Networks with Feedback (CNN-F), introduces a generative feedback with latent variables to existing CNN architectures, where consistent predictions are made through alternating MAP inference under a Bayesian framework. In the experiments, CNN-F shows considerably improved adversarial robustness over conventional feedforward CNNs on standard benchmarks
Neural Networks with Recurrent Generative Feedback
Neural networks are vulnerable to input perturbations such as additive noise
and adversarial attacks. In contrast, human perception is much more robust to
such perturbations. The Bayesian brain hypothesis states that human brains use
an internal generative model to update the posterior beliefs of the sensory
input. This mechanism can be interpreted as a form of self-consistency between
the maximum a posteriori (MAP) estimation of an internal generative model and
the external environment. Inspired by such hypothesis, we enforce
self-consistency in neural networks by incorporating generative recurrent
feedback. We instantiate this design on convolutional neural networks (CNNs).
The proposed framework, termed Convolutional Neural Networks with Feedback
(CNN-F), introduces a generative feedback with latent variables to existing CNN
architectures, where consistent predictions are made through alternating MAP
inference under a Bayesian framework. In the experiments, CNN-F shows
considerably improved adversarial robustness over conventional feedforward CNNs
on standard benchmarks.Comment: NeurIPS 202
A brain network basis of Fragile X syndrome behavioral penetrance determined by X chromosome inactivation in female mice
X-chromosome inactivation (XCI) in females is vital for normal brain function and cognition, as many X-linked genetic mutations lead to mental retardation and autism spectrum disorders, such as the fragile X syndrome (FXS). However, the degree by which XCI regulates disease presentation has been poorly investigated. To study this regulation in the mouse, here we quantified the brainwide composition of active-XC cells at single cell resolution using an X-linked MECP2-EGFP allele with known parent-of-origin. We present evidence that whole-brains, including all regions, on average favor maternal XC-active cells by 20%, or 8 million cells. This bias was conserved in heterozygous FXS mutant mice, which also corresponded to disease penetrance in maternal but not paternal FMR1 null mice. To localize the physical source of behavioral penetrance, brain-wide correlational screens successfully mapped mouse performance to cell densities in putative sensorimotor (e.g. sensory hindbrain, thalamus, globus pallidus) and sociability (e.g. visual/entorhinal cortices, bed nucleus stria terminalis, medial preoptic area) behavioral circuits of the open field sensorimotor and 3-chamber sociability assays, respectively. Overall, 50%/50% healthy/mutant cell density ratios in these brain networks were required for disease presentation in each behavior. These results suggest female X-linked behavioral penetrance of disease is regulated at the distributed level of mutant cell density in behavioral circuits, which is set by XCI that is subject to parent-of-origin effects. This work provides a novel explanation behind the broad and varied behavioral phenotypes commonly featured in female patients debilitated with X-linked mental disorders and may offer new entry points for behavioral therapeutics