We examined neural activity in prefrontal (PF) cortex of monkeys performing a delayed paired associate task. Monkeys were cued with a sample object. Then, after a delay, a test object was presented. If the test object was the object associated with the sample during training (i.e., its target), they had to release a lever. Monkeys could bridge the delay by remembering the sample (a sensory-related code) and/or thinking ahead to the expected target (a prospective code). Examination of the monkeys' behavior suggested that they were relying on a prospective code. During and shortly after sample presentation, neural activity in the lateral PF cortex primarily reflected the sample. Toward the end of the delay, however, PF activity began to reflect the anticipated target, which indicated a prospective code. These results provide further confirmation that PF cortex does not simply buffer incoming visual inputs, but instead selectively processes information relevant to current behavioral demands, even when this information must be recalled from long-term memory.

No abstract

Ketamine is an NMDA receptor antagonist commonly used to maintain general anesthesia. At anesthetic doses, ketamine causes bursts of 30-50 Hz oscillations alternating with 0.1 to 10 Hz oscillations. These dynamics are readily observed in local field potentials (LFPs) of non-human primates (NHPs) and electroencephalogram (EEG) recordings from human subjects. However, a detailed statistical analysis of these dynamics has not been reported. We characterize ketamine’s neural dynamics using a hidden Markov model (HMM). The HMM observations are sequences of spectral power in 10 Hz frequency bands between 0 to 50 Hz, where power is averaged within each band and scaled between 0 and 1. We model the observations as realizations of multivariate beta probability distributions that depend on a discrete-valued latent state process whose state transitions obey Markov dynamics. Using an expectation-maximization algorithm, we fit this beta-HMM to LFP recordings from 2 NHPs, and separately, to EEG recordings from 9 human subjects who received anesthetic doses of ketamine. Together, the estimated beta-HMM parameters and optimal state trajectory revealed an alternating pattern of states characterized primarily by gamma burst and slow oscillation activity, as well as intermediate states in between. The mean duration of the gamma burst state was 2.5s([1.9,3.4]s) and 1.2s([0.9,1.5]s) for the two NHPs, and 2.7s([1.9,3.8]s) for the human subjects. The mean duration of the slow oscillation state was 1.6s([1.1,2.5]s) and 0.7s([0.6,0.9]s) for the two NHPs, and 2.8s([1.9,4.3]s) for the human subjects. Our beta-HMM framework provides a useful tool for experimental data analysis. Our characterizations of the gamma-burst process offer detailed, quantitative constraints that can inform the development of rhythm-generating neuronal circuit models that give mechanistic insights into this phenomenon and how ketamine produces altered states of arousal.

11The brain consists of many interconnected networks with time-varying activity. There 12 are multiple sources of noise and variation yet activity has to eventually converge to a 13 stable state for its computations to make sense. We approached this from a control-14 theory perspective by applying contraction analysis to recurrent neural networks. This 15 allowed us to find mechanisms for achieving stability in multiple connected networks 16 with biologically realistic dynamics, including synaptic plasticity and time-varying inputs. 17These mechanisms included anti-Hebbian plasticity, synaptic sparsity and excitatory-18 inhibitory balance. We leveraged these findings to construct networks that could 19 perform functionally relevant computations in the presence of noise and disturbance. 20Our work provides a blueprint for how to construct stable plastic and distributed 21 networks. 22 23 2 2 Introduction 24 25The brain is comprised of networks that are highly dynamic and noisy. Neural activity 26 fluctuates from moment to moment and varies considerably between experimentally 27 identical trials (Latimer et al., 2015;Lundqvist et al., 2016; Churchland et al., 28 2011). These fluctuations can be due to a variety of factors including variability in 29 membrane potentials, inputs, plastic changes due to recent experience and so on. Yet, 30 in spite of these fluctuations, networks must 31 achieve computational stability. Despite 32 being "knocked around" by different starting 33 conditions and noise, networks must reach a 34 highly consistent state for their computations 35 to make sense. 36 The mechanisms that produce neural 37 network stability have been characterized 38 primarily in recurrent neural networks 39 (RNNs)--a general form of brain network-in 40 cases where the network weights are fixed 41 and the input the network receives is 42 constant (Fang and Kincaid 1996; Dayan and Abbot 2005). These stability conditions 43 are bounds on the eigenvalues of the weight matrix and prevent networks from "blowing 44 up", that is, from running away to high levels of excitation (Fang and Kincaid 1996; 45 Matsuoka 1992). This is an important finding but it is not the whole story. Eigenvalue 46 analysis of the weight matrix is only guaranteed to work in RNNs receiving constant 47 input and with fixed synaptic weights (or weights that change very slowly). Biological 48 networks, however, have plastic synaptic weights that change rapidly under constant 49 bombardment from environmental inputs. 50 Such "dynamic stability" can be studied using contraction analysis, a concept developed 51 in control theory. Unlike a chaotic system where perturbations and distortions can be 52 amplified over time, the population activity of a contracting network will converge 53 towards the same trajectory, thus achieving stable dynamics (Figure 1). One way to 54 understand contraction is to represent the state of a network at a given time as a point 55 in the network's 'state-space'. A commonly used state-space in neuroscience is the 56 space...

Categories are reflected in the spiking activity of neurons. However, how neurons form ensembles for categories is unclear. To address this, we simultaneously recorded spiking and local field potential (LFP) activity in the lateral prefrontal cortex (lPFC) of monkeys performing a delayed match to category task with two independent category sets (Animals: Cats vs Dogs; Cars: Sports Cars vs Sedans). We found stimulus and category information in alpha and beta band oscillations. Different category distinctions engaged different frequencies. There was greater spike field coherence (SFC) in alpha (~8-14 Hz) for Cats and in beta (~16-22 Hz) for Dogs. Cars showed similar differences, albeit less pronounced: greater alpha SFC for Sedans and greater beta SFC for Sports Cars. Thus, oscillatory rhythms can help coordinate neurons into different ensembles.Engagement of different frequencies may help differentiate the categories.peer-reviewed)