2,956 research outputs found
Identifying Interpretable Visual Features in Artificial and Biological Neural Systems
Single neurons in neural networks are often interpretable in that they
represent individual, intuitively meaningful features. However, many neurons
exhibit , i.e., they represent multiple unrelated
features. A recent hypothesis proposes that features in deep networks may be
represented in , i.e., on non-orthogonal axes by
multiple neurons, since the number of possible interpretable features in
natural data is generally larger than the number of neurons in a given network.
Accordingly, we should be able to find meaningful directions in activation
space that are not aligned with individual neurons. Here, we propose (1) an
automated method for quantifying visual interpretability that is validated
against a large database of human psychophysics judgments of neuron
interpretability, and (2) an approach for finding meaningful directions in
network activation space. We leverage these methods to discover directions in
convolutional neural networks that are more intuitively meaningful than
individual neurons, as we confirm and investigate in a series of analyses.
Moreover, we apply the same method to three recent datasets of visual neural
responses in the brain and find that our conclusions largely transfer to real
neural data, suggesting that superposition might be deployed by the brain. This
also provides a link with disentanglement and raises fundamental questions
about robust, efficient and factorized representations in both artificial and
biological neural systems
A roadmap to integrate astrocytes into Systems Neuroscience.
Systems neuroscience is still mainly a neuronal field, despite the plethora of evidence supporting the fact that astrocytes modulate local neural circuits, networks, and complex behaviors. In this article, we sought to identify which types of studies are necessary to establish whether astrocytes, beyond their well-documented homeostatic and metabolic functions, perform computations implementing mathematical algorithms that sub-serve coding and higher-brain functions. First, we reviewed Systems-like studies that include astrocytes in order to identify computational operations that these cells may perform, using Ca2+ transients as their encoding language. The analysis suggests that astrocytes may carry out canonical computations in a time scale of subseconds to seconds in sensory processing, neuromodulation, brain state, memory formation, fear, and complex homeostatic reflexes. Next, we propose a list of actions to gain insight into the outstanding question of which variables are encoded by such computations. The application of statistical analyses based on machine learning, such as dimensionality reduction and decoding in the context of complex behaviors, combined with connectomics of astrocyte-neuronal circuits, is, in our view, fundamental undertakings. We also discuss technical and analytical approaches to study neuronal and astrocytic populations simultaneously, and the inclusion of astrocytes in advanced modeling of neural circuits, as well as in theories currently under exploration such as predictive coding and energy-efficient coding. Clarifying the relationship between astrocytic Ca2+ and brain coding may represent a leap forward toward novel approaches in the study of astrocytes in health and disease
Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks
Biological plastic neural networks are systems of extraordinary computational
capabilities shaped by evolution, development, and lifetime learning. The
interplay of these elements leads to the emergence of adaptive behavior and
intelligence. Inspired by such intricate natural phenomena, Evolved Plastic
Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed
plastic neural networks with a large variety of dynamics, architectures, and
plasticity rules: these artificial systems are composed of inputs, outputs, and
plastic components that change in response to experiences in an environment.
These systems may autonomously discover novel adaptive algorithms, and lead to
hypotheses on the emergence of biological adaptation. EPANNs have seen
considerable progress over the last two decades. Current scientific and
technological advances in artificial neural networks are now setting the
conditions for radically new approaches and results. In particular, the
limitations of hand-designed networks could be overcome by more flexible and
innovative solutions. This paper brings together a variety of inspiring ideas
that define the field of EPANNs. The main methods and results are reviewed.
Finally, new opportunities and developments are presented
Experimental validation of the free-energy principle with in vitro neural networks
Empirical applications of the free-energy principle are not straightforward because they entail a commitment to a particular process theory, especially at the cellular and synaptic levels. Using a recently established reverse engineering technique, we confirm the quantitative predictions of the free-energy principle using in vitro networks of rat cortical neurons that perform causal inference. Upon receiving electrical stimuli—generated by mixing two hidden sources—neurons self-organised to selectively encode the two sources. Pharmacological up- and downregulation of network excitability disrupted the ensuing inference, consistent with changes in prior beliefs about hidden sources. As predicted, changes in effective synaptic connectivity reduced variational free energy, where the connection strengths encoded parameters of the generative model. In short, we show that variational free energy minimisation can quantitatively predict the self-organisation of neuronal networks, in terms of their responses and plasticity. These results demonstrate the applicability of the free-energy principle to in vitro neural networks and establish its predictive validity in this setting
Modeling biological face recognition with deep convolutional neural networks
Deep convolutional neural networks (DCNNs) have become the state-of-the-art
computational models of biological object recognition. Their remarkable success
has helped vision science break new ground and recent efforts have started to
transfer this achievement to research on biological face recognition. In this
regard, face detection can be investigated by comparing face-selective
biological neurons and brain areas to artificial neurons and model layers.
Similarly, face identification can be examined by comparing in vivo and in
silico multidimensional "face spaces". In this review, we summarize the first
studies that use DCNNs to model biological face recognition. On the basis of a
broad spectrum of behavioral and computational evidence, we conclude that DCNNs
are useful models that closely resemble the general hierarchical organization
of face recognition in the ventral visual pathway and the core face network. In
two exemplary spotlights, we emphasize the unique scientific contributions of
these models. First, studies on face detection in DCNNs indicate that
elementary face selectivity emerges automatically through feedforward
processing even in the absence of visual experience. Second, studies on face
identification in DCNNs suggest that identity-specific experience and
generative mechanisms facilitate this particular challenge. Taken together, as
this novel modeling approach enables close control of predisposition (i.e.,
architecture) and experience (i.e., training data), it may be suited to inform
long-standing debates on the substrates of biological face recognition.Comment: 41 pages, 2 figures, 1 tabl
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