1,797 research outputs found
Stochasticity from function -- why the Bayesian brain may need no noise
An increasing body of evidence suggests that the trial-to-trial variability
of spiking activity in the brain is not mere noise, but rather the reflection
of a sampling-based encoding scheme for probabilistic computing. Since the
precise statistical properties of neural activity are important in this
context, many models assume an ad-hoc source of well-behaved, explicit noise,
either on the input or on the output side of single neuron dynamics, most often
assuming an independent Poisson process in either case. However, these
assumptions are somewhat problematic: neighboring neurons tend to share
receptive fields, rendering both their input and their output correlated; at
the same time, neurons are known to behave largely deterministically, as a
function of their membrane potential and conductance. We suggest that spiking
neural networks may, in fact, have no need for noise to perform sampling-based
Bayesian inference. We study analytically the effect of auto- and
cross-correlations in functionally Bayesian spiking networks and demonstrate
how their effect translates to synaptic interaction strengths, rendering them
controllable through synaptic plasticity. This allows even small ensembles of
interconnected deterministic spiking networks to simultaneously and
co-dependently shape their output activity through learning, enabling them to
perform complex Bayesian computation without any need for noise, which we
demonstrate in silico, both in classical simulation and in neuromorphic
emulation. These results close a gap between the abstract models and the
biology of functionally Bayesian spiking networks, effectively reducing the
architectural constraints imposed on physical neural substrates required to
perform probabilistic computing, be they biological or artificial
Simulating developmental diversity: Impact of neural stochasticity on atypical flexibility and hierarchy
Introduction: Investigating the pathological mechanisms of developmental disorders is a challenge because the symptoms are a result of complex and dynamic factors such as neural networks, cognitive behavior, environment, and developmental learning. Recently, computational methods have started to provide a unified framework for understanding developmental disorders, enabling us to describe the interactions among those multiple factors underlying symptoms. However, this approach is still limited because most studies to date have focused on cross-sectional task performance and lacked the perspectives of developmental learning. Here, we proposed a new research method for understanding the mechanisms of the acquisition and its failures in hierarchical Bayesian representations using a state-of-the-art computational model, referred to as in silico neurodevelopment framework for atypical representation learning.
Methods: Simple simulation experiments were conducted using the proposed framework to examine whether manipulating the neural stochasticity and noise levels in external environments during the learning process can lead to the altered acquisition of hierarchical Bayesian representation and reduced flexibility.
Results: Networks with normal neural stochasticity acquired hierarchical representations that reflected the underlying probabilistic structures in the environment, including higher-order representation, and exhibited good behavioral and cognitive flexibility. When the neural stochasticity was high during learning, top-down generation using higher-order representation became atypical, although the flexibility did not differ from that of the normal stochasticity settings. However, when the neural stochasticity was low in the learning process, the networks demonstrated reduced flexibility and altered hierarchical representation. Notably, this altered acquisition of higher-order representation and flexibility was ameliorated by increasing the level of noises in external stimuli.
Discussion: These results demonstrated that the proposed method assists in modeling developmental disorders by bridging between multiple factors, such as the inherent characteristics of neural dynamics, acquisitions of hierarchical representation, flexible behavior, and external environment.journal articl
Data-driven modelling of biological multi-scale processes
Biological processes involve a variety of spatial and temporal scales. A
holistic understanding of many biological processes therefore requires
multi-scale models which capture the relevant properties on all these scales.
In this manuscript we review mathematical modelling approaches used to describe
the individual spatial scales and how they are integrated into holistic models.
We discuss the relation between spatial and temporal scales and the implication
of that on multi-scale modelling. Based upon this overview over
state-of-the-art modelling approaches, we formulate key challenges in
mathematical and computational modelling of biological multi-scale and
multi-physics processes. In particular, we considered the availability of
analysis tools for multi-scale models and model-based multi-scale data
integration. We provide a compact review of methods for model-based data
integration and model-based hypothesis testing. Furthermore, novel approaches
and recent trends are discussed, including computation time reduction using
reduced order and surrogate models, which contribute to the solution of
inference problems. We conclude the manuscript by providing a few ideas for the
development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and
Multiscale Dynamics (American Scientific Publishers
Active causation and the origin of meaning
Purpose and meaning are necessary concepts for understanding mind and
culture, but appear to be absent from the physical world and are not part of
the explanatory framework of the natural sciences. Understanding how meaning
(in the broad sense of the term) could arise from a physical world has proven
to be a tough problem. The basic scheme of Darwinian evolution produces
adaptations that only represent apparent ("as if") goals and meaning. Here I
use evolutionary models to show that a slight, evolvable extension of the basic
scheme is sufficient to produce genuine goals. The extension, targeted
modulation of mutation rate, is known to be generally present in biological
cells, and gives rise to two phenomena that are absent from the non-living
world: intrinsic meaning and the ability to initiate goal-directed chains of
causation (active causation). The extended scheme accomplishes this by
utilizing randomness modulated by a feedback loop that is itself regulated by
evolutionary pressure. The mechanism can be extended to behavioural variability
as well, and thus shows how freedom of behaviour is possible. A further
extension to communication suggests that the active exchange of intrinsic
meaning between organisms may be the origin of consciousness, which in
combination with active causation can provide a physical basis for the
phenomenon of free will.Comment: revised and extende
Presynaptic stochasticity improves energy efficiency and helps alleviate the stability-plasticity dilemma
When an action potential arrives at a synapse there is a large probability that no neurotransmitter is released. Surprisingly, simple computational models suggest that these synaptic failures enable information processing at lower metabolic costs. However, these models only consider information transmission at single synapses ignoring the remainder of the neural network as well as its overall computational goal. Here, we investigate how synaptic failures affect the energy efficiency of models of entire neural networks that solve a goal-driven task. We find that presynaptic stochasticity and plasticity improve energy efficiency and show that the network allocates most energy to a sparse subset of important synapses. We demonstrate that stabilising these synapses helps to alleviate the stability-plasticity dilemma, thus connecting a presynaptic notion of importance to a computational role in lifelong learning. Overall, our findings present a set of hypotheses for how presynaptic plasticity and stochasticity contribute to sparsity, energy efficiency and improved trade-offs in the stability-plasticity dilemma
A Novel Predictive-Coding-Inspired Variational RNN Model for Online Prediction and Recognition
This study introduces PV-RNN, a novel variational RNN inspired by the
predictive-coding ideas. The model learns to extract the probabilistic
structures hidden in fluctuating temporal patterns by dynamically changing the
stochasticity of its latent states. Its architecture attempts to address two
major concerns of variational Bayes RNNs: how can latent variables learn
meaningful representations and how can the inference model transfer future
observations to the latent variables. PV-RNN does both by introducing adaptive
vectors mirroring the training data, whose values can then be adapted
differently during evaluation. Moreover, prediction errors during
backpropagation, rather than external inputs during the forward computation,
are used to convey information to the network about the external data. For
testing, we introduce error regression for predicting unseen sequences as
inspired by predictive coding that leverages those mechanisms. The model
introduces a weighting parameter, the meta-prior, to balance the optimization
pressure placed on two terms of a lower bound on the marginal likelihood of the
sequential data. We test the model on two datasets with probabilistic
structures and show that with high values of the meta-prior the network
develops deterministic chaos through which the data's randomness is imitated.
For low values, the model behaves as a random process. The network performs
best on intermediate values, and is able to capture the latent probabilistic
structure with good generalization. Analyzing the meta-prior's impact on the
network allows to precisely study the theoretical value and practical benefits
of incorporating stochastic dynamics in our model. We demonstrate better
prediction performance on a robot imitation task with our model using error
regression compared to a standard variational Bayes model lacking such a
procedure.Comment: The paper is accepted in Neural Computatio
Self-organization of action hierarchy and compositionality by reinforcement learning with recurrent neural networks
Recurrent neural networks (RNNs) for reinforcement learning (RL) have shown
distinct advantages, e.g., solving memory-dependent tasks and meta-learning.
However, little effort has been spent on improving RNN architectures and on
understanding the underlying neural mechanisms for performance gain. In this
paper, we propose a novel, multiple-timescale, stochastic RNN for RL. Empirical
results show that the network can autonomously learn to abstract sub-goals and
can self-develop an action hierarchy using internal dynamics in a challenging
continuous control task. Furthermore, we show that the self-developed
compositionality of the network enhances faster re-learning when adapting to a
new task that is a re-composition of previously learned sub-goals, than when
starting from scratch. We also found that improved performance can be achieved
when neural activities are subject to stochastic rather than deterministic
dynamics
Active inference, evidence accumulation, and the urn task
Deciding how much evidence to accumulate before making a decision is a problem we and other animals often face, but one that is not completely understood. This issue is particularly important because a tendency to sample less information (often known as reflection impulsivity) is a feature in several psychopathologies, such as psychosis. A formal understanding of information sampling may therefore clarify the computational anatomy of psychopathology. In this theoretical letter, we consider evidence accumulation in terms of active (Bayesian) inference using a generic model of Markov decision processes. Here, agents are equipped with beliefs about their own behavior--in this case, that they will make informed decisions. Normative decision making is then modeled using variational Bayes to minimize surprise about choice outcomes. Under this scheme, different facets of belief updating map naturally onto the functional anatomy of the brain (at least at a heuristic level). Of particular interest is the key role played by the expected precision of beliefs about control, which we have previously suggested may be encoded by dopaminergic neurons in the midbrain. We show that manipulating expected precision strongly affects how much information an agent characteristically samples, and thus provides a possible link between impulsivity and dopaminergic dysfunction. Our study therefore represents a step toward understanding evidence accumulation in terms of neurobiologically plausible Bayesian inference and may cast light on why this process is disordered in psychopathology
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