64 research outputs found
Cortical oscillations implement a backbone for sampling-based computation in spiking neural networks
Brains need to deal with an uncertain world. Often, this requires visiting
multiple interpretations of the available information or multiple solutions to
an encountered problem. This gives rise to the so-called mixing problem: since
all of these "valid" states represent powerful attractors, but between
themselves can be very dissimilar, switching between such states can be
difficult. We propose that cortical oscillations can be effectively used to
overcome this challenge. By acting as an effective temperature, background
spiking activity modulates exploration. Rhythmic changes induced by cortical
oscillations can then be interpreted as a form of simulated tempering. We
provide a rigorous mathematical discussion of this link and study some of its
phenomenological implications in computer simulations. This identifies a new
computational role of cortical oscillations and connects them to various
phenomena in the brain, such as sampling-based probabilistic inference, memory
replay, multisensory cue combination and place cell flickering.Comment: 30 pages, 11 figure
A cortical model of object perception based on Bayesian networks and belief propagation.
Evidence suggests that high-level feedback plays an important role in visual perception by shaping
the response in lower cortical levels (Sillito et al. 2006, Angelucci and Bullier 2003, Bullier
2001, Harrison et al. 2007). A notable example of this is reflected by the retinotopic activation
of V1 and V2 neurons in response to illusory contours, such as Kanizsa figures, which has been
reported in numerous studies (Maertens et al. 2008, Seghier and Vuilleumier 2006, Halgren et al.
2003, Lee 2003, Lee and Nguyen 2001). The illusory contour activity emerges first in lateral
occipital cortex (LOC), then in V2 and finally in V1, strongly suggesting that the response is
driven by feedback connections. Generative models and Bayesian belief propagation have been
suggested to provide a theoretical framework that can account for feedback connectivity, explain
psychophysical and physiological results, and map well onto the hierarchical distributed
cortical connectivity (Friston and Kiebel 2009, Dayan et al. 1995, Knill and Richards 1996,
Geisler and Kersten 2002, Yuille and Kersten 2006, Deneve 2008a, George and Hawkins 2009,
Lee and Mumford 2003, Rao 2006, Litvak and Ullman 2009, Steimer et al. 2009).
The present study explores the role of feedback in object perception, taking as a starting point
the HMAX model, a biologically inspired hierarchical model of object recognition (Riesenhuber
and Poggio 1999, Serre et al. 2007b), and extending it to include feedback connectivity.
A Bayesian network that captures the structure and properties of the HMAX model is
developed, replacing the classical deterministic view with a probabilistic interpretation. The
proposed model approximates the selectivity and invariance operations of the HMAX model
using the belief propagation algorithm. Hence, the model not only achieves successful feedforward
recognition invariant to position and size, but is also able to reproduce modulatory effects
of higher-level feedback, such as illusory contour completion, attention and mental imagery.
Overall, the model provides a biophysiologically plausible interpretation, based on state-of-theart
probabilistic approaches and supported by current experimental evidence, of the interaction
between top-down global feedback and bottom-up local evidence in the context of hierarchical
object perception
Top-Down Feedback in an HMAX-Like Cortical Model of Object Perception Based on Hierarchical Bayesian Networks and Belief Propagation
PubMed ID: 2313976
A Computational Investigation of Neural Dynamics and Network Structure
With the overall goal of illuminating the relationship between neural dynamics and neural network
structure, this thesis presents a) a computer model of a network infrastructure capable of global broadcast
and competition, and b) a study of various convergence properties of spike-timing dependent plasticity
(STDP) in a recurrent neural network.
The first part of the thesis explores the parameter space of a possible Global Neuronal Workspace (GNW)
realised in a novel computational network model using stochastic connectivity. The structure of this
model is analysed in light of the characteristic dynamics of a GNW: broadcast, reverberation, and
competition. It is found even with careful consideration of the balance between excitation and inhibition,
the structural choices do not allow agreement with the GNW dynamics, and the implications of this are
addressed. An additional level of competition – access competition – is added, discussed, and found to be
more conducive to winner-takes-all competition.
The second part of the thesis investigates the formation of synaptic structure due to neural and synaptic
dynamics. From previous theoretical and modelling work, it is predicted that homogeneous stimulation in
a recurrent neural network with STDP will create a self-stabilising equilibrium amongst synaptic weights,
while heterogeneous stimulation will induce structured synaptic changes. A new factor in modulating the
synaptic weight equilibrium is suggested from the experimental evidence presented: anti-correlation due
to inhibitory neurons. It is observed that the synaptic equilibrium creates competition amongst synapses,
and those specifically stimulated during heterogeneous stimulation win out. Further investigation is
carried out in order to assess the effect that more complex STDP rules would have on synaptic dynamics,
varying parameters of a trace STDP model. There is little qualitative effect on synaptic dynamics under
low frequency (< 25Hz) conditions, justifying the use of simple STDP until further experimental or
theoretical evidence suggests otherwise
Modelling human choices: MADeM and decision‑making
Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)
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