The brain’s got rhythm: Extracting temporal patterns from visual input

To understand how the brain recognizes speech, appreciates music and performs other higher-level functions, it is necessary to understand how neural systems process temporal information. Recently, scientists at Beijing Normal University studied a simple but powerful network model by which a neural system can extract long-period (several seconds in duration) external rhythms from visual input. Moreover, the study’s findings suggest that a large neural network with a scale-free topology – that is, a network in which the probability distribution of the number of connections between its nodes follows a power law – is analogous to a repertoire where neural loops and chains form the mechanism by which exogenous rhythms are learned. Importantly, their model suggests that the brain does not necessarily require an internal clock to acquire and memorize these rhythms.

Prof. Si Wu and Prof. Gang Hu discussed the paper that they and their co-authors recently published in Proceedings of the National Academy of Sciences. “The challenge for generating slow oscillation – that is, on the order of seconds – in a neural system is that the dynamics of single neurons and neuronal synapses are too short,” Wu tells Medical Xpress. “In other words, for an unstructured network, a strong input will typically generate a strong transient response, and hence the system is unable to retain slow oscillation.” To solve this problem, the scientists came up with the idea of using the propagation of activity along a long loop of neurons to hold the rhythm information. “Neurons in the loop need to have low-connectivity degrees to avoid inducing synchronous firing of the network,” Hu adds.

Hu also comments on constructing a network model with scale-free structure. “We knew that a scale-free network had the structure we wanted – namely, it consists of a large number of low-degree neurons which can form different sizes of loops and chains, as well as a few hub neurons which can trigger synchronous firing of the network. Furthermore,” he continues, “we didn’t want hub neurons to be easily elicited; otherwise, the network will always get into epileptic firings.” To solve this problem, the researchers required that the neuronal interactions have the proper form to easily activate low-degree neuron while also making it hard to activate hub neurons. Wu point out that biologically plausible electrical synapses and scaled chemical synapses naturally hold this property.

Wu says that the researchers did not develop innovative techniques in this study. “Our main contribution was to propose a simple and yet effective mechanism for a neural system encoding temporal information,” he explains, noting that this mechanism consists of five key points:

1. Hub neurons, through their massive connections to others, induce synchronous firing of the network

2. Loops of low-degree neurons hold rhythm information, with the loop size deciding the rhythm

3. Proper electrical or scaled chemical neuronal synapses ensure that activating a hub neuron is difficult in comparison with a low-degree neuron – and also avoids epileptic network firing, in which periods of rapid spiking are followed by quiescent, silent, periods

4. A large-size scale-free network is like a reservoir, which contains a large number and various sizes of loops and chains formed by low-degree neurons, and hence can encode a broad range of rhythmic information

5. When an external rhythmic input is presented, the network selects a loop from its reservoir, with the loop size matching the input rhythm – and this matching operation can be achieved by a synaptic plasticity rule

The team’s findings imply that in terms of neural information processing, a neural system can use loops and chains of connected neurons to hold the memory trace of input information and, that the latter might serve as the substrate to process temporal events. “These implications for temporal information processing in neural systems have two aspects,” Wu points out. “Firstly, there’s been a long-standing debate on whether the brain has a global clock that counts time and coordinates temporal events. Our study suggests that this is not necessary: By using intrinsic network dynamics, the neural system can process temporal information in a distributed manner.”

Secondly, Wu continues, the brain may not use very complicated strategies to process temporal information, but by fully utilizing its enormous number of neurons, rather simple ones. “Our study suggests that a large size scale-free network has various lengths of loops and chains to hold different rhythms of inputs, making information encoding very simple. This is not economically efficient, but it simplifies computation, which could be crucial for animals responding quickly in a naturally competitive environment.”

In the presence of an external rhythmic input, Wu says that the neural system responds and holds the residual activity as the memory trace of the input for a sufficiently long time. If this input is repetitively presented, neuron pairs which fire together become connected through the biological synaptic plasticity rule, and thereby a loop matching the input rhythm is established.

Hu tells Medical Xpress that the network topology is not required to be perfectly scale-free, but rather that the network consists of a few neurons having many connections and a large number of neurons with few connections. “For the convenience of analysis, we considered a scale-free network in which the distribution of neuronal connections satisfying a power law. However, in practice, we don’t need such a strong condition. Rather, what we really need is a large number of low-degree neurons forming loops and chains, and a few hub neurons triggering synchronous firing. In other words, scale-free topology is the sufficient, but not the necessary, condition for our model to work.” Although the researchers focused on the visual system and have not applied their model to the auditory system, Hi suspects that it can be applied to the latter, where temporal processing is more critical.

Moving forward, the scientists’ next step is to build large networks having a similar structure but with more realistic neurons and synapses. “Based on this model,” Wu concludes, “we can explore how temporal information encoded in the way proposed in our model is involved in higher brain functions.” Moreover, other dynamical systems which generate slow oscillation and need to hold temporal information by network dynamics might benefit from our study.”

Researchers discover workings of brain’s ‘GPS system’

Just as a global posi­tion­ing sys­tem (GPS) helps find your loca­tion, the brain has an inter­nal sys­tem for help­ing deter­mine the body’s loca­tion as it moves through its surroundings.

A new study from researchers at Prince­ton Uni­ver­sity pro­vides evi­dence for how the brain per­forms this feat. The study, pub­lished in the jour­nal Nature, indi­cates that cer­tain position-tracking neu­rons — called grid cells — ramp their activ­ity up and down by work­ing together in a col­lec­tive way to deter­mine loca­tion, rather than each cell act­ing on its own as was pro­posed by a com­pet­ing theory.

Grid cells are neu­rons that become elec­tri­cally active, or “fire,” as ani­mals travel in an envi­ron­ment. First dis­cov­ered in the mid-2000s, each cell fires when the body moves to spe­cific loca­tions, for exam­ple in a room. Amaz­ingly, these loca­tions are arranged in a hexag­o­nal pat­tern like spaces on a Chi­nese checker board.

“Together, the grid cells form a rep­re­sen­ta­tion of space,” said David Tank, Princeton’s Henry L. Hill­man Pro­fes­sor in Mol­e­c­u­lar Biol­ogy and leader of the study. “Our research focused on the mech­a­nisms at work in the neural sys­tem that forms these hexag­o­nal pat­terns,” he said. The first author on the paper was grad­u­ate stu­dent Cristina Dom­nisoru, who con­ducted the exper­i­ments together with post­doc­toral researcher Amina Kinkhabwala.

Dom­nisoru mea­sured the elec­tri­cal sig­nals inside indi­vid­ual grid cells in mouse brains while the ani­mals tra­versed a computer-generated vir­tual envi­ron­ment, devel­oped pre­vi­ously in the Tank lab. The ani­mals moved on a mouse-sized tread­mill while watch­ing a video screen in a set-up that is sim­i­lar to video-game vir­tual real­ity sys­tems used by humans.

She found that the cell’s elec­tri­cal activ­ity, mea­sured as the dif­fer­ence in volt­age between the inside and out­side of the cell, started low and then ramped up, grow­ing larger as the mouse reached each point on the hexag­o­nal grid and then falling off as the mouse moved away from that point.

This ramp­ing pat­tern cor­re­sponded with a pro­posed mech­a­nism of neural com­pu­ta­tion called an attrac­tor net­work. The brain is made up of vast num­bers of neu­rons con­nected together into net­works, and the attrac­tor net­work is a the­o­ret­i­cal model of how pat­terns of con­nected neu­rons can give rise to brain activ­ity by col­lec­tively work­ing together. The attrac­tor net­work the­ory was first pro­posed 30 years ago by John Hop­field, Princeton’s Howard A. Prior Pro­fes­sor in the Life Sci­ences, Emeritus.

The team found that their mea­sure­ments of grid cell activ­ity cor­re­sponded with the attrac­tor net­work model but not a com­pet­ing the­ory, the oscil­la­tory inter­fer­ence model. This com­pet­ing the­ory pro­posed that grid cells use rhyth­mic activ­ity pat­terns, or oscil­la­tions, which can be thought of as many fast clocks tick­ing in syn­chrony, to cal­cu­late where ani­mals are located. Although the Prince­ton  researchers detected rhyth­mic activ­ity inside most neu­rons, the activ­ity pat­terns did not appear to par­tic­i­pate in posi­tion calculations.