Using an electronic system that duplicates the neural signals associated with memory, they managed to replicate the brain function in rats associated with long-term learned behavior, even when the rats had been drugged to forget.
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.
In the experiment, the researchers had rats learn a task, pressing one lever rather than another to receive a reward. Using embedded electrical probes, the experimental research team, led by Sam A. Deadwyler of the Wake Forest Department of Physiology and Pharmacology, recorded changes in the rat’s brain activity between the two major internal divisions of the hippocampus, known as subregions CA3 and CA1. During the learning process, the hippocampus converts short-term memory into long-term memory, the researchers prior work has shown.
“No hippocampus,” Berger said, “no long-term memory, but still short-term memory.” CA3 and CA1 interact to create long-term memory, prior research has shown.
In a dramatic demonstration, the experimenters blocked the normal neural interactions between the two areas using pharmacological agents. The previously trained rats then no longer displayed the long-term learned behavior.
“The rats still showed that they knew ‘when you press left first, then press right next time, and vice-versa,’ ” Berger said. “And they still knew in general to press levers for water, but they could only remember whether they had pressed left or right for five to 10 seconds.”
Using a model created by the prosthetics research team led by Berger, the teams then went further and developed an artificial hippocampal system that could duplicate the pattern of interaction between CA3 and CA1.
Long-term memory capability returned to the pharmacologically blocked rats when the team activated the electronic device programmed to duplicate the memory-encoding function.
In addition, the researchers went on to show that if a prosthetic device and its associated electrodes were implanted in animals with a normal, functioning hippocampus, the device could actually strengthen the memory being generated internally in the brain and enhance the memory capability of normal rats.
“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 identification and manipulation of the encoding process can restore and even enhance cognitive mnemonic processes,” the study reported.
Next steps, according to Berger and Deadwyler, will be attempts to duplicate the rat results in monkeys, with the aim of eventually creating prostheses that might help the human victims of Alzheimer’s disease, stroke or injury recover function.
The efficiency of the cortical prosthesis described here utilizing a MIMO model that predicts firing patterns for successful encoding of task specific information, provides a unique yet feasible approach to constructing prostheses to replace information transmitted between brain structures via nonlinear patterned inputs and outputs. In the case of hippocampal based memories, the above demonstration shows that cognitive processes can be detected in terms of a code that reflects the degree to which information is successfully represented and encoded for retrieval at a later time (Wais et al 2006,Wixted 2007,Miller et al 2010). The fact that this code can be mimicked by delivery of electrical stimulation in the same spatio-temporal sequence during the to-be-remembered behavioral event, provides a means of (1) improving performance when encoding is deficient, (2) replacing memory function when hippocampal processes are compromised, and importantly (3) determining the specificity of the electrical stimulation used to mimic the firing patterns that are not present. What is also apparent is that this cortical prosthesis does not require separate probes for delivery of effective neural stimulation, since the stimulation pulses were delivered through the same CA1 array electrodes. Therefore, it is likely that similar assessments could yield further confirmation that MIMO model derived stimulation patterns could be employed as a universal means of recovering lost connections between neural ensembles in other brain regions, as demonstrated here utilizing previously well-characterized hippocampal firing patterns.
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
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