7,359 research outputs found
Canonical Cortical Circuits and the Duality of Bayesian Inference and Optimal Control
The duality of sensory inference and motor control has been known since the
1960s and has recently been recognized as the commonality in computations
required for the posterior distributions in Bayesian inference and the value
functions in optimal control. Meanwhile, an intriguing question about the brain
is why the entire neocortex shares a canonical six-layer architecture while its
posterior and anterior halves are engaged in sensory processing and motor
control, respectively. Here we consider the hypothesis that the sensory and
motor cortical circuits implement the dual computations for Bayesian inference
and optimal control, or perceptual and value-based decision making,
respectively. We first review the classic duality of inference and control in
linear quadratic systems and then review the correspondence between dynamic
Bayesian inference and optimal control. Based on the architecture of the
canonical cortical circuit, we explore how different cortical neurons may
represent variables and implement computations.Comment: 13 pages, 3 figur
Rhythmic inhibition allows neural networks to search for maximally consistent states
Gamma-band rhythmic inhibition is a ubiquitous phenomenon in neural circuits
yet its computational role still remains elusive. We show that a model of
Gamma-band rhythmic inhibition allows networks of coupled cortical circuit
motifs to search for network configurations that best reconcile external inputs
with an internal consistency model encoded in the network connectivity. We show
that Hebbian plasticity allows the networks to learn the consistency model by
example. The search dynamics driven by rhythmic inhibition enable the described
networks to solve difficult constraint satisfaction problems without making
assumptions about the form of stochastic fluctuations in the network. We show
that the search dynamics are well approximated by a stochastic sampling
process. We use the described networks to reproduce perceptual multi-stability
phenomena with switching times that are a good match to experimental data and
show that they provide a general neural framework which can be used to model
other 'perceptual inference' phenomena
Temporal Dynamics of Decision-Making during Motion Perception in the Visual Cortex
How does the brain make decisions? Speed and accuracy of perceptual decisions covary with certainty in the input, and correlate with the rate of evidence accumulation in parietal and frontal cortical "decision neurons." A biophysically realistic model of interactions within and between Retina/LGN and cortical areas V1, MT, MST, and LIP, gated by basal ganglia, simulates dynamic properties of decision-making in response to ambiguous visual motion stimuli used by Newsome, Shadlen, and colleagues in their neurophysiological experiments. The model clarifies how brain circuits that solve the aperture problem interact with a recurrent competitive network with self-normalizing choice properties to carry out probablistic decisions in real time. Some scientists claim that perception and decision-making can be described using Bayesian inference or related general statistical ideas, that estimate the optimal interpretation of the stimulus given priors and likelihoods. However, such concepts do not propose the neocortical mechanisms that enable perception, and make decisions. The present model explains behavioral and neurophysiological decision-making data without an appeal to Bayesian concepts and, unlike other existing models of these data, generates perceptual representations and choice dynamics in response to the experimental visual stimuli. Quantitative model simulations include the time course of LIP neuronal dynamics, as well as behavioral accuracy and reaction time properties, during both correct and error trials at different levels of input ambiguity in both fixed duration and reaction time tasks. Model MT/MST interactions compute the global direction of random dot motion stimuli, while model LIP computes the stochastic perceptual decision that leads to a saccadic eye movement.National Science Foundation (SBE-0354378, IIS-02-05271); Office of Naval Research (N00014-01-1-0624); National Institutes of Health (R01-DC-02852
Neural Models of Motion Integration, Segmentation, and Probablistic Decision-Making
When brain mechanism carry out motion integration and segmentation processes that compute unambiguous global motion percepts from ambiguous local motion signals? Consider, for example, a deer running at variable speeds behind forest cover. The forest cover is an occluder that creates apertures through which fragments of the deer's motion signals are intermittently experienced. The brain coherently groups these fragments into a trackable percept of the deer in its trajectory. Form and motion processes are needed to accomplish this using feedforward and feedback interactions both within and across cortical processing streams. All the cortical areas V1, V2, MT, and MST are involved in these interactions. Figure-ground processes in the form stream through V2, such as the seperation of occluding boundaries of the forest cover from the boundaries of the deer, select the motion signals which determine global object motion percepts in the motion stream through MT. Sparse, but unambiguous, feauture tracking signals are amplified before they propogate across position and are intergrated with far more numerous ambiguous motion signals. Figure-ground and integration processes together determine the global percept. A neural model predicts the processing stages that embody these form and motion interactions. Model concepts and data are summarized about motion grouping across apertures in response to a wide variety of displays, and probabilistic decision making in parietal cortex in response to random dot displays.National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624
The New Ledoux: Survival Circuits and the Surplus Meaning of ‘Fear’
ABSTRACT LeDoux's pioneering work on the neurobiology of fear has played a crucial role in informing debates in the philosophy of emotion. For example, it plays a key part in Griffiths’ argument for why emotions don’t form a natural kind. Likewise, it is employed by Faucher and Tappolet to defend pro-emotion views, which claim that emotions aid reasoning. LeDoux, however, now argues that his work has been misread. He argues that using emotion terms, like ‘fear’, to describe neurocognitive data adds a ‘surplus meaning’: it attributes phenomenal properties to survival circuits which they don’t possess. This paper aims to explore LeDoux's new proposal, and examine the potentially devastating consequences that ensue for the aforementioned views. I end by addressing the worry that these lessons are conditional on LeDoux's own higher-order theory of emotional consciousness being true
Linking Visual Development and Learning to Information Processing: Preattentive and Attentive Brain Dynamics
National Science Foundation (SBE-0354378); Office of Naval Research (N00014-95-1-0657
Nonlocal mechanism for cluster synchronization in neural circuits
The interplay between the topology of cortical circuits and synchronized
activity modes in distinct cortical areas is a key enigma in neuroscience. We
present a new nonlocal mechanism governing the periodic activity mode: the
greatest common divisor (GCD) of network loops. For a stimulus to one node, the
network splits into GCD-clusters in which cluster neurons are in zero-lag
synchronization. For complex external stimuli, the number of clusters can be
any common divisor. The synchronized mode and the transients to synchronization
pinpoint the type of external stimuli. The findings, supported by an
information mixing argument and simulations of Hodgkin Huxley population
dynamic networks with unidirectional connectivity and synaptic noise, call for
reexamining sources of correlated activity in cortex and shorter information
processing time scales.Comment: 8 pges, 6 figure
Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future
Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)
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