Topic: recurrent dynamics

Cortical circuits exhibit complex activity patterns, both spontaneously and evoked by external stimuli. Information encoding and learning in neural circuits depend on how well time-varying stimuli can control network activity. We show that in firing-rate networks in the balanced state, external control of recurrent dynamics, i.e., the suppression of the internally-generated chaotic variability, strongly depends on correlations in the input: One might expect that driving all neurons with a common input helps to control network dynamics. Surprisingly, we find that the network is far easier to co

Neural circuits exhibit complex activity patterns, both spontaneously and in response to external stimuli. Information encoding and learning in neural circuits depend on the ability of time-varying stimuli to control spontaneous network activity. In particular, variability arising from the sensitivity to initial conditions of recurrent cortical circuits can limit the information conveyed about the sensory input. Spiking and firing rate network models can exhibit such sensitivity to initial conditions that are reflected in their dynamic entropy rate and attractor dimensionality computed from th

By causally mapping neural activity to behavior [1], Brain-Computer Interfaces (BCI) offer a means to study the dynamics of sensorimotor learning. Here, we combine computational modeling and data analysis to study the neural learning algorithm [2] monkeys use to adapt to a changed output mapping in a center-out reaching task. We exploit that the mapping from neural space (ca. 100 dimensions) to the 2D cursor position is a credit assignment problem [3] that is underconstrained, because changes along a large number of output-null dimensions do not influence the behavioral output. We hypothesized

The brain computes internal representations by applying highly recurrent dynamics to feed-forward input. Such dynamics may be viewed as analogous to the inference step in latent variables models, where one usually follows the gradient of the latent posterior distribution until a stable state is reached. At the stable state, when this gradient has approximately zero norm, a parameter optimization step can be applied to slightly increase the likelihood of the current sample under the model. Here we suggest this procedure, which closely matches the expectation-maximization (EM) algorithm, can be

Recurrent dynamics are thought to be central to computation in the cerebral cortex. Yet, how these dynamics differ across areas remains unknown. Here, we used two-photon microscopy to image Ca2+ activity from hundreds of neurons and while delivering brief, focal excitatory input with near-cellular- resolution optogenetics. Specifically, we compared how the primary visual (V1) and the secondary motor (M2) cortices distinctly sustain this input while mice ran spontaneously. Single-neuron responses to focal stimulation were much more prevalent in M2 than V1. Moreover, evoked network-level dynamic