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44
Book reviews
- Statistics of the British Economy, by F. M. M
"... Overdiagnosis and overtreatment of breast cancer Progression of ductal carcinoma in situ: the pathological perspective ..."
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Overdiagnosis and overtreatment of breast cancer Progression of ductal carcinoma in situ: the pathological perspective
Video Review
- Proc. 9th Symp. on Computational Geometry
, 1993
"... 008 In this paper, we review the nature of illusions using the free-energy formulation of Bayesian 065 ..."
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008 In this paper, we review the nature of illusions using the free-energy formulation of Bayesian 065
Finding and feeling the musical beat: striatal dissociations between detection and prediction of regularity
- Cereb. Cortex
, 2013
"... Perception of temporal patterns is critical for speech, movement, and music. In the auditory domain, perception of a regular pulse, or beat, within a sequence of temporal intervals is associated with basal ganglia activity. Two alternative accounts of this striatal activity are possible: ‘‘searching ..."
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Perception of temporal patterns is critical for speech, movement, and music. In the auditory domain, perception of a regular pulse, or beat, within a sequence of temporal intervals is associated with basal ganglia activity. Two alternative accounts of this striatal activity are possible: ‘‘searching’ ’ for temporal regularity in early stimulus processing stages or ‘‘prediction ’ of the timing of future tones after the beat is found (relying on continuation of an internally generated beat). To resolve between these accounts, we used functional magnetic resonance imaging (fMRI) to investigate different stages of beat perception. Participants heard a series of beat and nonbeat (irregular) monotone sequences. For each sequence, the preceding sequence provided a temporal beat context for the following sequence. Beat sequences were preceded by nonbeat sequences, requiring the beat to be found anew (‘‘beat finding’ ’ condition), or by beat sequences with the same beat rate (‘‘beat continuation’’), or a different rate (‘‘beat adjustment’’). Detection of regularity is highest during beat finding, whereas generation and prediction are highest during beat continuation. We found the greatest striatal activity for beat continuation, less for beat adjustment, and the least for beat finding. Thus, the basal ganglia’s response profile suggests a role in beat prediction, not in beat finding.
2011 Teleonomy: the feedback circuit involving information and thermodynamic processes
- J. Mod. Phys
"... Informational and entropic- metabolic aspects are strictly intertwined in organisms. An overview of bacterial chemotaxis is presented as a good and simple model to study these issues. In particular, the paper shall focus on the ability of the organism to restore its homeostasis not only from a metab ..."
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Informational and entropic- metabolic aspects are strictly intertwined in organisms. An overview of bacterial chemotaxis is presented as a good and simple model to study these issues. In particular, the paper shall focus on the ability of the organism to restore its homeostasis not only from a metabolic point of view but also from an informational point of view. The organism cannot accomplish this task without a good “model ” of the environment and without undertaking appropriate actions that will somehow modify it or at least the relation “organism- environment”. Subsequently, the concept of teleonomy is developed as a dynamical trade- off between segregation and openness of the organism both from a thermodynamic and informational point of view.
Recognizing recurrent neural networks (rrnn): Bayesian inference for recurrent neural networks. Biological cybernetics
, 2012
"... Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learn-ing applications. In an RNN, each neuron com-putes its output as a nonlinear function of its in-tegrated input. While the importance of RNNs, especially as models of brain processing, is undis-puted, it ..."
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Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learn-ing applications. In an RNN, each neuron com-putes its output as a nonlinear function of its in-tegrated input. While the importance of RNNs, especially as models of brain processing, is undis-puted, it is also widely acknowledged that the com-putations in standard RNN models may be an over-simplification of what real neuronal networks com-pute. Here, we suggest that the RNN approach may be made both neurobiologically more plausi-ble and computationally more powerful by its fu-sion with Bayesian inference techniques for non-linear dynamical systems. In this scheme, we use an RNN as a generative model of dynamic input caused by the environment, e.g. of speech or kine-matics. Given this generative RNN model, we de-rive Bayesian update equations that can decode its output. Critically, these updates define a ’recogniz-ing RNN ’ (rRNN), in which neurons compute and exchange prediction and prediction error messages. The rRNN has several desirable features that a con-ventional RNN does not have, for example, fast de-coding of dynamic stimuli and robustness to ini-tial conditions and noise. Furthermore, it imple-ments a predictive coding scheme for dynamic in-puts. We suggest that the Bayesian inversion of recurrent neural networks may be useful both as a model of brain function and as a machine learning tool. We illustrate the use of the rRNN by an ap-plication to the online decoding (i.e. recognition) of human kinematics. 1
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, 2010
"... We suggested recently that attention can be understood as inferring the level of uncertainty or precision during hierarchical perception. In this paper, we try to substantiate this claim using neuronal simulations of directed spatial attention and biased competition. These simulations assume that ne ..."
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We suggested recently that attention can be understood as inferring the level of uncertainty or precision during hierarchical perception. In this paper, we try to substantiate this claim using neuronal simulations of directed spatial attention and biased competition. These simulations assume that neuronal activity encodes a probabilistic representation of the world that optimizes free-energy in a Bayesian fashion. Because free-energy bounds surprise or the (negative) log-evidence for internal models of the world, this optimization can be regarded as evidence accumulation or (generalized) predictive coding. Crucially, both predictions about the state of the world generating sensory data and the precision of those data have to be optimized. Here, we show that if the precision depends on the states, one can explain many aspects of attention. We illustrate this in the context of the Posner paradigm, using the simulations to generate both psychophysical and electrophysiological responses. These simulated responses are consistent with attentional bias or gating, competition for attentional resources, attentional capture and associated speed-accuracy trade-offs. Furthermore, if we present both attended and nonattended stimuli simultaneously, biased competition for neuronal representation emerges as a principled and straightforward property of Bayes-optimal perception.
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, 2012
"... doi: 10.3389/fnhum.2012.00026 Searching for roots of entrainment and joint action in early musical interactions ..."
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doi: 10.3389/fnhum.2012.00026 Searching for roots of entrainment and joint action in early musical interactions
Attentional enhancement of auditory mismatch responses: a DCM/MEG study,” Cerebral Cortex
, 2015
"... Despite similar behavioral effects, attention and expectation influence evoked responses differently: Attention typically enhances event-related responses, whereas expectation reduces them. This dissociation has been reconciled under predictive coding,where prediction errors areweightedbyprecisionas ..."
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Despite similar behavioral effects, attention and expectation influence evoked responses differently: Attention typically enhances event-related responses, whereas expectation reduces them. This dissociation has been reconciled under predictive coding,where prediction errors areweightedbyprecisionassociatedwith attentionalmodulation.Here,we tested thepredictive coding account of attention and expectation using magnetoencephalography and modeling. Temporal attention and sensory expectationwere orthogonallymanipulated in an auditorymismatch paradigm, revealing opposing effects on evoked response amplitude. Mismatch negativity (MMN) was enhanced by attention, speaking against its supposedly pre-attentive nature. This interaction effectwasmodeled in a canonicalmicrocircuit using dynamic causalmodeling, comparingmodelswithmodulation of extrinsic and intrinsic connectivity at different levels of the auditory hierarchy. While MMN was explained by recursive interplay of sensory predictions and prediction errors, attention was linked to the gain of inhibitory interneurons, consistent with its modulation of sensory precision. Key words: attention, dynamic causal modeling, expectation, magnetoencephalography, predictive coding
Edinburgh Research Explorer
"... Toward a neural basis of interactive alignment in conversation Citation for published version: Menenti, L, Pickering, MJ & Garrod, SC 2012, 'Toward a neural basis of interactive alignment in ..."
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Toward a neural basis of interactive alignment in conversation Citation for published version: Menenti, L, Pickering, MJ & Garrod, SC 2012, 'Toward a neural basis of interactive alignment in
