Abstract
The ability to simultaneously record from large numbers of neurons in behaving animals has ushered in a new era for the study of the neural circuit mechanisms underlying cognitive functions. One promising approach to uncovering the dynamical and computational principles governing population responses is to analyze model recurrent neural networks (RNNs) that have been optimized to perform the same tasks as behaving animals. Because the optimization of network parameters specifies the desired output but not the manner in which to achieve this output, “trained” networks serve as a source of mechanistic hypotheses and a testing ground for data analyses that link neural computation to behavior. Complete access to the activity and connectivity of the circuit, and the ability to manipulate them arbitrarily, make trained networks a convenient proxy for biological circuits and a valuable platform for theoretical investigation. However, existing RNNs lack basic biological features such as the distinction between excitatory and inhibitory units (Dale’s principle), which are essential if RNNs are to provide insights into the operation of biological circuits. Moreover, trained networks can achieve the same behavioral performance but differ substantially in their structure and dynamics, highlighting the need for a simple and flexible framework for the exploratory training of RNNs. Here, we describe a framework for gradient descent-based training of excitatory-inhibitory RNNs that can incorporate a variety of biological knowledge. We provide an implementation based on the machine learning library Theano, whose automatic differentiation capabilities facilitate modifications and extensions. We validate this framework by applying it to well-known experimental paradigms such as perceptual decision-making, context-dependent integration, multisensory integration, parametric working memory, and motor sequence generation. Our results demonstrate the wide range of neural activity patterns and behavior that can be modeled, and suggest a unified setting in which diverse cognitive computations and mechanisms can be studied.
Highlights
Computations in the brain are carried out by populations of interconnected neurons
Cognitive functions arise from the coordinated activity of many interconnected neurons
As neuroscientists increasingly use large datasets of simultaneously recorded neurons to study the brain, one approach that has emerged as a promising tool for interpreting population responses is to analyze model recurrent neural networks (RNNs) that have been optimized to perform the same tasks as recorded animals
Summary
Computations in the brain are carried out by populations of interconnected neurons. While single-neuron responses can reveal a great deal about the neural mechanisms underlying various sensory, motor, and cognitive functions, neural mechanisms often involve the coordinated activity of many neurons whose complex individual dynamics are not explained by tuning to experimental parameters [1,2,3,4]. A growing recognition of the importance of studying population-level responses is reflected in the increasing number of studies that use large datasets of simultaneously or sequentially recorded neurons to infer neural circuit mechanisms [5,6,7,8,9]. One approach that has emerged as a promising tool for identifying the dynamical and computational mechanisms embedded in large neural populations is to study model recurrent neural networks (RNNs) whose connection weights have been optimized to perform the same tasks as recorded animals [5, 7]. Complete access to the activity and connectivity of the circuit, and the ability to manipulate them in arbitrary ways, make trained networks a convenient proxy for biological circuits and a valuable platform for theoretical investigation [12, 16, 17]
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