The prefrontal cortex (PFC) plays a crucial role in flexible cognitive behavior by representing task relevant information with its working memory. The working memory with sustained neural activity is described as a neural dynamical system composed of multiple attractors, each attractor of which corresponds to an active state of a cell assembly, representing a fragment of information. Recent studies have revealed that the PFC not only represents multiple sets of information but also switches multiple representations and transforms a set of information to another set depending on a given task context. This representational switching between different sets of information is possibly generated endogenously by flexible network dynamics but details of underlying mechanisms are unclear. Here we propose a dynamically reorganizable attractor network model based on certain internal changes in synaptic connectivity, or short-term plasticity. We construct a network model based on a spiking neuron model with dynamical synapses, which can qualitatively reproduce experimentally demonstrated representational switching in the PFC when a monkey was performing a goal-oriented action-planning task. The model holds multiple sets of information that are required for action planning before and after representational switching by reconfiguration of functional cell assemblies. Furthermore, we analyzed population dynamics of this model with a mean field model and show that the changes in cell assemblies’ configuration correspond to those in attractor structure that can be viewed as a bifurcation process of the dynamical system. This dynamical reorganization of a neural network could be a key to uncovering the mechanism of flexible information processing in the PFC.
Based on firing properties of certain prefrontal cortex neurons the authors suggest a network model in which short-term plasticity implements switches of what the neurons in the network represent. In particular, neurons in prefrontal cortex have been found which switch from representing goals to representing actions (first, their firing varies depending on which goal is shown, then it varies depending on which action is executed afterwards while firing equally for all goals). The authors call this representational switches and they assume that these are implemented via changes in the connection strengths of neurons in a recurrently connected neural network. The network is setup such that network activity always converges to one of several fixed point attractors. A suitable change in connection strengths then leads to a change in the attractor landscape which may be interpreted as a change in what the network represents. The main contribution of the authors is to suggest a particular pattern of short-term plasticity at synapses in the network such that the network exhibits the desired representational switching. Another important aspect of this model is its structure: the network consists of separate cell assemblies, different subsets of which are assumed to be active when either goals or actions are represented and the goal and action subsets are partially overlapping. For example, in their model they have four cell assemblies (A,B,C,D) and the subsets (A,B) and (C,D) are associated with goals while subsets (A,D) and (B,C) are associated with actions. Initially the network is assumed to be in the goal state in which the connection strenghts A-B and C-D are large. The presentation of one of two goals then makes the network activity converge to strong activation of (A,B) or (C,D). Synaptic depression of connections A-B (assuming that this is the active subset) with simultaneous facilitation of connections A-D and B-C then leads to the desired change of connection strengths which implements the representational switch and then makes either subset (A-D), or subset (B-C) the active subset. It is not entirely clear to me why only one action subset becomes active. Maybe this is what the inhibitory units in the model are for (their function is not explained by the authors). In further analysis and experiments the authors confirm the attractor landscape of the model (and how it changes), show that the timing of the representational switch can be influenced by input to the network and show that the probability of changing from a particular goal to a particular action can be manipulated by changing the number of prior connections between the corresponding cell assemblies.
The authors show a nice qualitative correspondence between experimental findings and simulated network behaviour (although some qualitative differences are left, too, e.g., a general increase of firing also for the non-preferred goal and action in the experimental findings). In essence, the authors present a mechanism which could implement the (seemingly) autonomous switching of representations in prefrontal cortex neurons. Whether this mechanism is used by the brain is an entirely different question. I don’t know of evidence backing the chosen special wiring of neurons and distribution of short-term placticity, but this might just reflect my lack of knowledge of the field. Additionally, I wouldn’t exclude the possibility of a hierarchical model. The authors argue against this by presuming that prefrontal cortex already should be the top of the hierarchy, but nothing prevents us to make hierarchical models of prefrontal cortex itself. This points to the mixing of levels of description in the paper: On the one hand, the main contributions of the paper are on the algorithmic level describing the necessary wiring in a network of a few units and how it needs to change to reproduce the behaviour observed in experiments. On the other hand, the main model is on an implementational level showing how these ideas could be implemented in a network of leaky integrate and fire (LIF) neurons. In my opinion, the LIF neuron network doesn’t add anything interesting to the paper apart from the proof that the algorithmic ideas can be implemented by such a network. On the contrary, it masks a bit the main points of the paper by introducing an abundance of additional parameters which needed to be chosen by the authors, but for which we don’t know which of these settings are important. Finally, I wonder how the described network is reset in order to be ready for the next trial. The problem is the following: the authors initialise the network such that the goal subsets have a high synaptic efficacy at the start of the trial. The short-term plasticity then reduces these synaptic efficacies while simultaneously increasing those of the action subsets. At the end of a trial they all end up in a similar range (see Fig. 3A bottom). In order for the network to work as expected in the next trial, it somehow needs to reset to the initial synaptic efficacies.