Memory Enhancement and Brain–Computer Interface Devices: Technological Possibilities and Constitutional Challenges

A specific memory is thought to be encoded by a sparse population of neurons 1,2 . These neurons can be tagged during learning for subsequent identification 3 and manipulation 4,5,6 . Moreover, their ablation or inactivation results in reduced memory expression, suggesting their necessity in mnemonic processes. However, a critical question of sufficiency remains: can one elicit the behavioral output of a specific memory by directly activating a population of neurons that was active during learning? Here we show that optogenetic reactivation of hippocampal neurons activated during fear conditioning is sufficient to induce freezing behavior. We labeled a population of hippocampal dentate gyrus neurons activated during fear learning with channelrhodopsin-2 (ChR2) 7,8 and later optically reactivated these neurons in a different context. The mice showed increased freezing only upon light stimulation, indicating light-induced fear memory recall. This freezing was not detected in non-fear conditioned mice expressing ChR2 in a similar proportion of cells, nor in fear conditioned mice with cells labeled by EYFP instead of ChR2. Finally, activation of cells labeled in a context not associated with fear did not evoke freezing in mice that were previously fear conditioned in a different context, suggesting that light-induced fear memory recall is context-specific. Together, our findings indicate that activating a sparse but specific ensemble of hippocampal neurons that contribute to a memory engram is sufficient for the recall of that memory. Moreover, our experimental approach offers a general method of mapping cellular populations bearing memory engrams.

A primary objective in developing a neural prosthesis is to replace neural circuitry in the brain that no longer functions appropriately. Such a goal requires artificial reconstruction of neuron-to-neuron connections in a way that can be recognized by the remaining normal circuitry, and that promotes appropriate interaction. In this study, the application of a specially designed neural prosthesis using a multi-input/multi-output (MIMO) nonlinear model is demonstrated by using trains of electrical stimulation pulses to substitute for MIMO model derived ensemble firing patterns. Ensembles of CA3 and CA1 hippocampal neurons, recorded from rats performing a delayed-nonmatch-to-sample (DNMS) memory task, exhibited successful encoding of trial-specific sample lever information in the form of different spatiotemporal firing patterns. MIMO patterns, identified online and in real-time, were employed within a closed-loop behavioral paradigm. Results showed that the model was able to predict successful performance on the same trial. Also, MIMO model-derived patterns, delivered as electrical stimulation to the same electrodes, improved performance under normal testing conditions and, more importantly, were capable of recovering performance when delivered to animals with ensemble hippocampal activity compromised by pharmacologic blockade of synaptic transmission. These integrated experimental-modeling studies show for the first time that, with sufficient information about the neural coding of memories, a neural prosthesis capable of real-time diagnosis and manipulation of the encoding process can restore and even enhance cognitive, mnemonic processes.